Measuring the Impact of Lean Techniques on Performance ...

Measuring the Impact of Lean Techniques onPerformance Indicators in Logistics Operations

Zur Erlangung des akademischen Grades eines

Doktors der Ingenieurwissenschaften

der Fakultät für Maschinenbau

des Karlsruher Instituts für Technologie (KIT)

genehmigte

Dissertation

von

Dipl.-Wirt.-Ing. Payam Dehdari

Tag der mündlichen Prüfung: 11.06.2013Hauptreferent: Prof. Dr.-Ing. K. FurmansKoreferentin: Prof. Dr.-Ing. B. Deml

Vorwort

Die vorliegende Arbeit entstand während meiner Tätigkeit als Pro-jektleiter in der Zentralstelle Logistik bei der Robert Bosch GmbH.Ich möchte mich an dieser Stelle bei allen Personen bedanken, diezum Gelingen dieser Arbeit beigetragen haben.Herrn Prof. Dr.-Ing. Kai Furmans, Leiter des Instituts für Förder-technik und Logistiksysteme, gilt mein besonderer Dank für dieÜbernahme des Hauptreferats sowie für die Unterstützung meinerTätigkeit als externer Doktorand. Der Begriff “scharfes Nachdenken”hat erst durch seine Fragen an Bedeutung für mich gewonnen undsomit eine erfolgreiche Promotion ermöglicht.Frau Prof. Dr.-Ing. Barbara Deml danke ich für die Übernahme desKorreferats und Herrn Prof. Dr. Robert Stieglitz danke ich für dieÜbernahme des Prüfungsvorsitzes.Mein herzlicher Dank gilt den Hunderten von beteiligten Personender Studie Warehouse Excellence. Ohne deren harte Arbeit in denLogistikzentren wäre die Basis dieser Arbeit nie gegeben gewesen.Außerdem möchte ich insbesondere Herrn Dr. Karl Nowak als Men-tor von Warehouse Excellence danken. Bei Herrn Dr. Wlcek be-danke ich mich ausdrücklich für seine Rolle als Treiber des Projektesund für das mir entgegen gebrachte große Vertrauen. Herrn Dr.-Ing.Beha bin ich für sein Coaching dankbar. Erst dadurch füllte sich dasWort Lean für mich mit Inhalt. Melanie Schwab und Dr.-Ing. Chris-tian Huber bin ich für die endlosen Diskussionen und die Korrekturdes Manuskripts dankbar. Verena Pluhar, Stefan Keiber, PatrikSpalt, Martin Kreuser, Darian Achenbach, Marius Oehms, LauraAulmann, David Geissler und Alexander Lingen, die Ihre Abschluss-arbeit im Rahmen von Warehouse Excellence geschrieben oder alswissenschaftliche Hilfskraft tätig waren, gilt mein besonderer Dank.

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Ohne deren Arbeit wäre Warehouse Excellence nicht das, was esheute ist.Mein tiefster Dank gilt meiner Familie, die in dieser Welt verteilt istund ohne deren Liebe all das nicht möglich wäre.Zuletzt möchte ich mich aufrichtig bei allen meinen Freunden, Bekann-ten und meiner Familie für die letzte Jahre entschuldigen, wo es ofthieß: “Sorry, ich kann nicht mit ich muss an meiner Diss arbeiten.”Das hat mich am meisten geprägt, denn ich hatte vergessen, waswichtig ist.

Stuttgart, Mai 2013 Payam Dehdari

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KurzfassungPayam Dehdari

Messung des Einflusses von LeanTechniken auf Leistungskennzahlen inLogistikzentren

Die Wurzeln von Lean Techniken reichen über 50 Jahre zurück undbefinden sich in den Produktionssystemen der japanischen Automo-bilindustrie. Mehrere umfassende und tiefgreifende Studien bestäti-gen den positiven Einfluss von Lean Techniken auf Leistungskenn-zahlen im Produktionsumfeld. Studien im Lagerumfeld beleuchtenden Zusammenhang hingegen nur unzureichend. Somit besteht zurzeiteine Lücke zwischen den Erkenntnisstand des Einfluss von LeanTechniken auf Leistungskennzahlen im Produktionsumfeld verglichenzum Lagerumfeld.Das Ziel dieser Arbeit ist es dazu beizutragen, die erwähnte Lückezu schließen. Dies soll Entscheider dazu motivieren und dabei un-terstützen, Lean Techniken im Lagerumfeld zu etablieren.Damit dies erreicht wird, wurde vom Jahresende 2010 bis zum Jahre-sanfang 2012 eine Studie mit 16 Lägern in einer Beobachtungs-gruppe und 56 Lägern in einer Kontrollgruppe durchgeführt. Einintensives Befähigungsprogram sicherte ab, dass die Beobachtungs-gruppe Lean Techniken in ihrer Führungskultur, kontinuierlichenVerbesserungsarbeit und operativen Prozessen etablierte.Die Qualität der Umsetzung wurde mit Hilfe eines Lean Lager-assessments, das im Rahmen dieser Arbeit entwickelt wurde, be-

iii

Kurzfassung

wertet. Dieses Lean Lagerassessment basiert auf einer neuen Gener-ation von Assessments, die im Produktionsumfeld genutzt werden.Der Lean Reifegrad aller teilnehmenden Läger wurde anhand desLean Lagerassessments vor und nach dem Projekt aufgenommen.Zusätzlich wurden Leistungskennzahlen vom Jahresanfang 2010 biszum Jahresende 2011 ermittelt.Die Messergebnisse des Lean Reifegrads und die erhobenen Leis-tungskennzahlen wurden mithilfe deskriptiver Statistik verglichenund mit nichtparametrischen zwei Stichprobentests analysiert. DasErgebnis war eine hohe signifikante positive Entwicklung der Leis-tungskennzahlen und des Lean Reifegrads der Beobachtungsgruppe.Daraus wird abgeleitet, dass der Lean Reifegrad eine positive Wirkungauf Leistungskennzahlen hat. Ein genauer mathematischer Zusam-menhang konnte nicht ermittelt werden. Weiterhin wurde beobachtet,dass die Beobachtungsgruppe eine im Vergleich zur Kontrollgruppestärkere Entwicklung des Lean Reifegrads und der Leistungskenn-zahlen aufweist.Dieses Ergebnis trägt dazu bei, die Lücke zwischen dem Erkennt-nisstand über die Wirkung von Lean Techniken auf Leistungskenn-zahlen im Produktionsumfeld zum Lagerumfeld zu schließen. Ent-scheider sind dadurch aufgefordert, sich auf die Etablierung von LeanTechniken im Lagerumfeld zu konzentrieren. Denn eine entschei-dende positive Entwicklung des Lean Reifegrads, die sich positiv aufLeistungskennzahlen wirkt, ist in einem Zeitraum von einem Jahrmöglich.

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AbstractPayam Dehdari

Measuring the Impact of LeanTechniques on Performance Indicators inLogistics Operations

The roots of lean techniques date back 50 years to the productionsystems of the Japanese automotive production industry. Severalin-depth studies have verified the positive impact of lean techniqueson performance indicators in production environments. Studies per-formed on warehouse environments have only partially confirmedthis. Up until now, there has been more evidence supporting thepositive impact of lean techniques on performance indicators in pro-duction environments than in warehouse environments.The purpose of this thesis is to help close the gap between the dis-parities in the level of evidence mentioned above. Closing this gapshould cause decision makers to support the implementation of leantechniques in the warehouse environment. To this end, a study wasconducted from the end of 2010 until the beginning of 2012 that in-cluded 16 warehouses in an observation group and 56 warehouses ina control group. An intensive empowerment program ensured thatthe observation group established the lean philosophy in their lead-ership, continuous improvement work, and operational processes.Lean maturity measurements were carried out using a lean ware-house assessment tool that was developed for this study. The leanwarehouse assessment tool is based on a new generation of assess-

v

Abstract

ments that are used in the production environment. Each participat-ing warehouse was measured before and at the end of the project aspart of the assessment. In addition to this, performance indicatorswere measured from the beginning of 2010 until the end of 2011.The lean maturity results and the performance indicators were com-pared using descriptive statistics and analyzed using two samplenon-parametric hypothesis tests. The result was a highly significantpositive development of the productivity performance indicators andthe lean maturity level within the observation group. This indicatesthat the positive lean maturity development had an impact on theperformance indicators. Further research and analysis was done todetermine if a higher lean maturity resulted in a higher performancedevelopment. The result was that a positive relation between higherlean maturity and better developed performance indicators couldbe determined. A functional relation between the lean maturityand performance indicators could not be established. Finally, theobservation group showed better results in the lean maturity andperformance indicators compared to the control group.These results help close the gap in the evidence and encourage deci-sion makers to concentrate on lean activities within logistics opera-tions. A major lean maturity development that results in a positivehigh performance indicator development is possible within the spanof a year in the warehouse environment.

vi

Contents

Kurzfassung iii

Abstract v

1 Introduction 11.1 Problem Description . . . . . . . . . . . . . . . . . . 21.2 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . 41.3 Organization of the Thesis . . . . . . . . . . . . . . . 9

2 The Lean Philosophy in the Warehouse Environment 132.1 Genesis of Lean . . . . . . . . . . . . . . . . . . . . . 132.2 Warehousing at a Glance . . . . . . . . . . . . . . . 152.3 Warehouse versus Production Environment . . . . . 162.4 LeanWarehousing: Transferring Lean Production into

the Warehouse . . . . . . . . . . . . . . . . . . . . . 19

3 Literature Review: Measuring the Impact of Lean 213.1 Measuring the Impact of Lean on Production . . . . 213.2 Measuring the Impact of Lean on Warehousing . . . 223.3 Tools for Measuring Lean Warehousing . . . . . . . 25

3.3.1 Lean Warehousing Maturity Assessments . . 253.3.2 Lean Warehousing Performance Indicators . 29

3.4 Conclusion of the Literature Review . . . . . . . . . 32

4 Bosch Logistics Warehouse Assessment 354.1 Development of the Bosch Logistics Warehouse As-

sessment . . . . . . . . . . . . . . . . . . . . . . . . . 35

vii

Contents

4.2 Structure of the Bosch Logistic Warehouse Assessment 364.3 Intermediate Result: Measuring Systematic . . . . . 43

5 Design of the Experiment 475.1 Warehouse Excellence Group - the Observation Sam-

ple . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.1.1 The Warehouse Excellence Project - Lean Em-

powerment . . . . . . . . . . . . . . . . . . . 485.2 Control Group . . . . . . . . . . . . . . . . . . . . . 555.3 Method for Testing the Hypotheses . . . . . . . . . . 56

6 Analyzing the Lean Impact 616.1 Statistical Background . . . . . . . . . . . . . . . . . 61

6.1.1 Choosing the Goodness of Fit Test . . . . . . 626.1.2 Choosing the Non-Parametric Test . . . . . 63

6.2 Analysis of Lean Maturity Development . . . . . . . 656.2.1 Lean Maturity Development of the Warehouse

Excellence Group . . . . . . . . . . . . . . . . 656.2.2 The Warehouse Excellence Group versus the

Control Group . . . . . . . . . . . . . . . . . 886.2.3 Intermediate Result: Lean Improvement . . . 101

6.3 Analyzing the Impact on Productivity . . . . . . . . 1026.3.1 Productivity Development of the Warehouse

Excellence Group . . . . . . . . . . . . . . . 1036.3.2 Productivity Development of the Warehouse

Excellence Group versus Control Group . . . 1096.3.3 Intermediate Result: Productivity Improvement111

6.4 Review the Hypotheses . . . . . . . . . . . . . . . . . 1126.4.1 Review of Hypothesis I . . . . . . . . . . . . 1146.4.2 Review of Hypothesis II . . . . . . . . . . . . 1146.4.3 Review of Hypothesis III . . . . . . . . . . . 1156.4.4 Review of Hypothesis IV . . . . . . . . . . . . 115

7 Summary & Conclusion 117

viii

Contents

Bibliography 127

A Appendix - Warehouse Excellence Group Data Sheet 137

B Appendix - Control Group Data Sheet 139

C Appendix - Assessment Questionaire 141

D Appendix - Warehouse Excellence Group Assessment Re-sults 157

E Appendix - Warehouse Excellence Group KPR 161

F Appendix - Control Group Assessment Results 165

G Appendix - Control Group KPR 171

H Appendix - Warehouse Excellence Projects Overview 175

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1 IntroductionKarl Popper was one of the most important philosophers of the 20thcentury (Dykes, 1999, p.1). He believed that whenever a theoryappears to be the only possible solution to a problem, people haveto take this as a sign that the theory has not been understood or thatthe problem was never intended to be solved (Lass, 1984, p.XIV).At the beginning of the 20th century, many managers believed thatthe theory of mass production was the only efficient method for pro-duction (Huber, 2011, p.1). Taiichi Ohno saw the sign that Poppermentioned and questioned the efficiency of the theory of mass pro-duction. Working during the time of the tough economic challengesthat the Japanese industry faced after World War ll, Ohno believedthat it was possible to surpass the conventional style of mass pro-duction and produce value for the customer with less waste and withhigher efficiency (Ohno, 1988, p.2). Motivated by his belief, Ohnodeveloped the Toyota Production System (TPS) (Ohno, 1988; Liker,2004, p.4).In the second phase of the International Motor Vehicle Program(IMVP), scientists benchmarked the Toyota Production System (Hol-weg, 2007) with the mass production methods of other automotivecompanies. Within the scope of this detailed study, they analyzedthe effectiveness of the TPS and determined the superiority of theTPS over traditional production systems (Womack, 2007). Dur-ing the IMVP, John Krafcik coined the term “Lean Production”(Cusumano, 1994) to describe the philosophy behind the TPS. Partsof this lean philosophy were transferred to other functional plant ar-eas and industrial sectors. Terms such as “Lean Management” and“Lean Administration” also came into being (Bell and Orzen, 2011;

1

1 Introduction

Zidel, 2006; Pfeiffer and Weiß, 1994).Elements of the lean philosophy also eventually found their way intothe warehouse environment (Augustin, 2009; Dehdari et al., 2011;Spee and Beuth, 2012; Furmans and Wlcek, 2012). The majority ofreports that analyze lean techniques within the warehouse are basedon pilot studies with a low sample size or even single pilot projectexperience reports.

1.1 Problem DescriptionIn-depth studies that analyze the impact of lean techniques in pro-duction environments are usually based on a combination of threeevaluation techniques:

• Measurement of the lean maturity• Measurement of the performance indicators• Comparison of the samples with each other

In addition to five other research areas, the IMVP analyzed the ma-turity of the production systems within plants. The scientists alsoanalyzed the development of major performance indicators. Theseperformance indicators either focused on one research area or onseveral overarching research areas. The last phase of the researchinvolved a comparison of the data between plants with the TPSand plants with traditional production systems. The results of thecomparison showed that the sample with the TPS had superior per-formance indicators.With these results, the IMVP asserted that the comparison demon-strated a positive impact of the TPS on major performance indi-cators. Other studies have backed up the positive impact of leantechniques on performance indicators in the production environment(Bidgoli, 2004; Hofer et al., 2012; Fullerton et al., 2003; Oeltjenbruns,

2

1.1 Problem Description

2000; Liker, 2004).This also means that the comparison of lean maturity results (inde-pendent variables) with the development of performance indicators(dependent variables) between different samples is a verified methodfor proving the superiority of the TPS in the production environ-ment.As stated earlier, lean techniques have also found their way intothe warehouse environment but the conditions of the warehouse en-vironment differ from those in the production environment. Onedifference is that the less technical nature of the warehouse allowsmore options for process changes. Another difference is that thehigher degree of manual work in a warehouse causes larger fluctua-tions in cycle times. Further differences are highlighted in Dehdariand Schwab (2012). Therefore, the question is raised if the leantechniques that were developed for the production environment areapplicable in the warehouse environment.However, some of the verified measurement techniques used in theproduction environment are also used in studies in the warehouseenvironment. Most studies, such as Reuter (2009), use a majorperformance indicator to measure the impact of the lean techniques.The Reuter study, and other similar studies that will be discussed inchapter 3, does not include a measurement of the lean maturity of theoperation that improves the performance indicator (Reuter, 2009).Other studies, such as Sobanski and Mahfouz, used an assessment toanalyze the lean maturity but did not relate it to the developmentof the performance indicator (Sobanski, 2009; Mahfouz, 2011).All of the known in-depth, verified, and reliable studies that measurethe impact of lean techniques on the production environment wereperformed using a combination of the above-mentioned evaluationtechniques. The known studies on the warehouse environment mea-sure performance indicators but they do not compare their resultswith the results of a measurement of the lean maturity or they donot compare different samples with each other. Some other stud-ies measure the lean maturity but do not link it with the devel-

3

1 Introduction

opment of the performance indicators. A comparison of warehouseperformance indicators for warehouses that improve the lean matu-rity with warehouses that use other techniques is not known. Thismeans that the applicability of verified measurement techniques forthe impact of lean techniques in warehouses is still being studied.Thus, this thesis will help to close the gap between the disparitiesin the level of evidence for the impact of lean techniques on per-formance indicators within the production environment comparedwith the warehouse environment. This thesis will also evaluate theapplicability of using verified measuring methods in production forthe warehouse environment.I hope that by contributing to closing the gap in the levels of evi-dence, I can help companies make the decision to invest in resourcesfor establishing lean techniques within the warehouse environment.This should result in benefits for them because the lean maturity inwarehouses today is low and the high potential for improvement isknown (Furmans and Wlcek, 2012).

1.2 Hypotheses

The research presented in this thesis is based on four hypotheseswhich help to close the gap in the level of evidence for the impact oflean techniques on performance indicators. The hypotheses are de-scribed below. A coordinate system was used to show the differencebetween the hypotheses. An indicator for the lean maturity and anindicator for the performance development were used to test a hy-pothesis. The independent variable lean maturity is located on theabscissa. The dependent variable performance development is lo-cated on the ordinate. It also has to be noticed that the expectationlevel for a clear relation between the lean maturity and performanceindicator rises from hypothesis I till hypothesis IV. This hypothesislead us to discover the relationship between the lean maturity andperformance indicators step by step .

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1.2 Hypotheses

Hypothesis I: Lean has a positive impact on performanceindicators but we do not expect to know if a higher level oflean maturity has a higher influence on performance indi-cators.

Hypothesis I is shown in figure 1.1. To test Hypothesis I, warehousesthat improved their lean maturity were analyzed. For Hypothesis Ito be true, no warehouse that shows a positive lean maturity couldshow a negative development of performance indicators and therecan be no evidence that a higher lean maturity also implies a higherpositive impact on the development of the performance indicators.In the example shown in figure 1.1, WH 2 has a lower performancedevelopment with a higher lean maturity development than WH 1.In conclusion, I restrict myself to show a positive impact withoutthe necessary evidence to show higher lean maturity leads to higherperformance indicator improvement.

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1 Introduction

Performance Indicator Development (relative)

Lean Maturity Points Achieved (absolute)

WH 1

WH 2

WH 3

WH 4

(0,0)

Hypothesis 1: Lean has a positive impact on performance indicator development

Possible Warehouse Position

Impossible Warehouse Position

WH: Warehouse

Figure 1.1: Hypothesis I

Hypothesis II: A higher level of lean maturity has a morepositive impact on performance indicators but we do notknow if this relation follows a mathematical function.

Hypothesis II is shown in figure 1.2. The warehouses that improvedtheir lean maturity also showed a positive development in their per-formance indicators. This is similar to Hypothesis I. The differenceto Hypothesis I is in the level of development of the performanceindicators. If a warehouse reached a higher lean maturity level thananother warehouse, it has at least the same level of performance in-dicator development or even higher. In the example shown in figure1.2, the warehouses with a higher maturity level also have higherperformance indicator levels. At this moment, it is not clear if a

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1.2 Hypotheses

mathematical function can describe the relation between the level oflean maturity and the level of the performance indicators.



WH 1

WH 2

WH 3 WH 4

(0,0)

Possible Warehouse Position

Impossible Warehouse Position

WH: Warehouse

Hypothesis 2: More lean has a more positive impact on performance indicator development without correlation

Figure 1.2: Hypothesis II

Hypothesis III: There is a mathematical correlation be-tween the level of lean maturity and the performance indi-cators. A mathematical function can describe this correla-tion.

Hypothesis III is shown in figure 1.3. There is a clear dependencybetween the level of lean maturity and the performance indicatorsand a mathematical function describes this correlation. This func-tion could be a straight line or a decreasing or increasing curve.

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1 Introduction



WH 1

WH 2

WH 3

WH 4

(0,0)

WH: Warehouse

Hypothesis 3: Positive correlation between the lean maturity level and performance indicator development

Functional Correlation Graph

Figure 1.3: Hypothesis III

Hypothesis IV: Lean techniques have a higher positive im-pact on performance indicators than other approaches.

Hypothesis IV is shown in figure 1.4. The assumption in HypothesisIV is that a group of warehouses that focuses on and improves theirlean activities have a higher positive performance indicator develop-ment than warehouses that use other approaches or anything at all,instead of a focused lean development program.

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1.3 Organization of the Thesis


CoGr 4

CoGr 1 CoGr 2

CoGr 3

(0,0)

WaEx: Warehouse Excellence Group CoGr: Control Group


WaEx 1

WaEx 2

WaEx 3

WaEx 4

Hypothesis 4: Lean has a higher positive influence on performance indicators than other approaches

Figure 1.4: Hypothesis IV


Figure 1.5 shows the structure of this thesis. In chapter 1, the moti-vation behind the thesis is explained. To measure the impact of leantechniques on performance indicators within the warehouse environ-ment, the terms lean and warehousing need to be defined. Theseterms are discussed in chapter 2. Chapter 2 also defines lean ware-housing and discusses what will be measured in the following chap-ters.To build on existing measuring methods, a literature review in chap-ter 3 identifies the relevant publications on measuring lean tech-niques within the production and warehouse environments. Basedon this review, chapter 3 also discusses the existing methods for mea-suring lean maturity and performance indicators. Since a suitablemethod for measuring lean maturity could not be identified, chapter4 describes the development of an appropriate method. This new

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1 Introduction

appropriate method combined with a method that was identified formeasuring the performance indicators is used as the system of mea-surement for this thesis. The design of experiment used to test thehypotheses is defined in chapter 5 and the measurement and inter-pretation of data is presented in chapter 6. Chapter 7 rounds outthe thesis with a conclusion and critical discussion.

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Analyzing the Lean Impact

Motivation and Hypotheses

What is Lean?

What is Warehousing?

Lean Warehousing

Status Measuring Lean Production

Status Assessments

Developing the Bosch Logistics Warehouse Assessment

Design of the Experiment

Descriptive Analyses Inferential Analyses

Summary & Conclusion

1.

2.

3.

4.

5.

6.

7. I know now.

I measure and

interpret.

How do I want to measure?

I want to know...

What do I want to measure?

Status Measuring Lean Warehousing

Status Performance Indicators

Leading Thoughts

Review the Hypotheses

Figure 1.5: Structure of the thesis

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1 Introduction

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2 The Lean Philosophy in theWarehouse Environment

This chapter first highlights the roots of the lean approach andpresents the related milestone literature (see section 2.1). To trans-fer the lean approach to the warehouse environment, it is necessaryto understand the definition, processes, and types of warehouses (seesection 2.2). Using that knowledge, it is possible to highlight the ma-jor differences between the production and warehouse environments(see section 2.3). Finally, it is possible to derive the definition oflean warehousing from the perceptions.

2.1 Genesis of LeanDiscipline and avoidance of waste is deep-rooted in the Japaneseculture (Lebra and Lebra, 1986, p. 70). This is even reflected in thedaily life of the Japanese. For example, when a Japanese chef filetsa salmon to make sashimi he uses the cropped and unused parts ofthe salmon as ingredients for a soup and does not dispose of them.Against this cultural background and the aftermath of World War II,Taiichi Ohno assessed the mass production system of the Americanautomobile industry. At that time, the majority of the companiesin the automotive industry applied the mass production philoso-phy strengthened by the circumstance of increasing demands (Ohno,1988, p.1)(Liker, 2004, p.24). The industry believed that mass pro-duction was the most efficient way to fulfill customer demand. Ohno,however, did not share the view of the overwhelming majority.

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2 The Lean Philosophy in the Warehouse Environment

Ohno saw several forms of waste in the American way and had astrong desire to avoid waste and improve the processes. To servecustomer needs with high product variation was one of his desiresbut this was simply less profitable when mass production was in use.Out of that desire and need he developed the Toyota ProductionSystem (TPS) to enable his organization to meet customer demandin a more efficient way. With the Toyota Production System, heestablished a new culture within Toyota and proved its strength inthe oil crisis in 1973. During the oil crisis, which caused decreasingdemands, other Japanese industry sectors followed Toyota’s methodof production (Ohno, 1988; Liker, 2004, p. xiii).The Toyota Production System received worldwide attention afterthe publication in 1990 of the Womack book The Machine thatChanged the World (Womack, 2007). Other publications like Bösen-berg and Metzen (1993), Liker (2004), Pfeiffer and Weiß (1994),Rother and Shook (2008), Rother and Kinkel (2009), and Womackand Jones (2004) discussed the lean philosophy from different an-gles. Dehdari et al. (2011) analyzed several key literature sourcesand identified the constituents of lean production. The eliminationof waste using a structured continuous improvement cycle is the keyelement in the literature. Furmans and Wlcek (2012) divided thiscontinuous improvement cycle into low and high frequent improve-ment cycles. The low frequent improvement cycle is an analytic andsystematic method resulting from the derivation of the target valuestreams from their implementation. After implementation, the highfrequent improvement cycle stabilizes the implemented standard andimproves it again in a systematic and analytical way. In additionto this, Furmans and Wlcek (2012) identified seven success factorsfor using lean techniques in the warehouse environment. These areleadership, value stream planning, standardisation, work place de-sign, visualisation, work force management, and sustainable problemsolving.

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2.2 Warehousing at a Glance

2.2 Warehousing at a Glance

Section 2.1 identified the roots of the lean philosophy in the Japaneseautomotive industry sector. To measure the impact of lean tech-niques in the warehouse environment, it is necessary to base theresearch on a definition of the function of a warehouse along withan understanding of the processes within warehouses and the iden-tification of different types of relevant warehouses. This sectiontakes valid definitions for the function of a warehouse, warehouseprocesses, and types of warehouses from the key literature for thepurpose of this thesis.Gudehus and Kotzab (2012, p. 19) state that the function of awarehouse in a logistics network is to transfer, store and commissiongoods. Bartholdi and Hackman (2011, p. 5), ten Hompel et al.(2007, p. 50), Arnold et al. (2008, p. 373) use similar definitionsfor the function. Bartholdi and Hackman (2011, p. 5) mentionedthe space and time synchronization function of a warehouse withina supply chain. ten Hompel et al. (2007, p. 50) add the change ofstatus of goods to their definition. Arnold et al. (2008, p. 373) viewthe warehouse from a broader angle. They assert that the function ofa warehouse is, in fact, is to disrupt the supply chain. The EuropeanNorm EN 14943 (2005) defines the function of a warehouse as a spacethat is designed to receive, store, and distribute goods. This thesiswill focus on the processes within a warehouse and not on the role ofthe warehouse within a supply chain. For this reason, this thesis willuse the European Norm EN 14943 as the definition for the functionof a warehouse.The three processes mentioned in the European Norm EN 14943standard are detailed by Arnold et al. (2008, p. 379). In addi-tion to many other information processes, Arnold et al. (2008, p.379) defined the receipt, storage and retrieval, picking, packing, andshipping of goods as the major processes in a warehouse. Bartholdiand Hackman (2011, p. 24), ten Hompel et al. (2007, p. 53), andGudehus and Kotzab (2012, p. 19) supported this definition with

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their own. The VDI 3629 (2005)guideline differs from this defini-tion because it excludes the packing process as a major warehouseprocess.Regarding the information processes mentioned by Arnold et al.(2008, p. 379), the VDI guideline overlaps mainly with Arnold etal. (2008, p. 379). Bartholdi and Hackman (2011, p. 28) overlapwith the control process and ten Hompel et al. (2007, p. 53) overlapwith the identification process. Gudehus and Kotzab (2012, p. 19)do not identify the information processes as major warehouses pro-cesses. This thesis will focus on the definition given by Arnold et al.(2008, p. 379) regarding the processes within warehouses because itis the one that is the most validated by the above-mentioned authorsand guidelines.The definition of the different kinds of warehouse types is based on acombination and emphasis of the different major processes. Arnoldet al. (2008, p. 376) distinguishes eight categories with 29 warehousetypes. The most common warehouse types in the industry sectorare production warehouses and warehouses for product distribution.Since the intent of this thesis is to test the hypotheses that are validfor all warehouses, no distinction will be made between the differentwarehouse types.

2.3 Warehouse versus ProductionEnvironment

An innovation is often designed for one specific environment. Awristwatch, for example, was originally designed for use on land. Ifit is to be used under water, the designer needs to understand the en-vironmental changes and determine if design changes are necessary.The wristwatch, then, must be waterproof and resistant against saltwater. It may also possess other features like an altitude meter.Environmental changes between a production and warehouse envi-

16

2.3 Warehouse versus Production Environment

ronment (see section 2.2) also force us to identify necessary adapta-tions or design changes to the production-based lean approach (seesection 2.1). The environmental changes have to be identified be-fore these adaptations or design changes can be made. Dehdari andSchwab (2012) identified the major differences as follows:

• Differences in the purpose• Differences in the complexity of problem solving• Differences in the complexity of movement• Differences in the order lot size• Differences in the physical order (space versus line)• Differences in the expectations of the output performance• Differences in the leadership

These differences are discussed below.The purpose in production is to add value to raw materials andchange the form; for example, forging steel to make a horseshoe. Inwarehouses, the added value is to transform the time and spatialstatus of the product. The trigger for transformation in warehousesis usually an order from a downstream process. This kind of trans-forming process is called Make to Order (Olhager, 2012). In additionto Make to Order, other possible production triggers are Make toStock and Assemble to Order.These differences in purpose mean that problem-solving is usuallyless complex in warehouses than in production. Changing the formof material is very complex technically and includes several otherscientific disciplines. Often only expert knowledge can solve produc-tion problems. One example is the difference in complexity in un-derstanding thermal problems in treating materials versus materialshandling problems in warehouses, such as closing a box. Difficultiesdo arise in materials handling problems with getting an overview ofthe interdependencies between the different processes but produc-

17


tion also faces this problem at times. However, it is important toremember that a lower complexity does not mean a simple complex-ity.The lot size that is processed in the warehouse is usually one. Thereason for this is that when an order is placed by a customer an-other customer does not usually place exactly the same order. Thesame order means the same products in the same quantity. The lotsize in production can be one but the lot size is often higher to savechangeover time or because the production is not mature enough toperform fast changeovers. Higher productivity within the lot can beachieved because of the higher lot size. The higher lot size in pro-duction also implies a higher degree of repetitive work for employeesand, conversely, a lower degree of repetitive work in warehouses.The lower degree of repetitive work causes higher fluctuation in theworking cycles.Production machines manufacture goods in the same quantity andsame quality over a long period of time. This is done because of thelot size and the purpose of adding value to raw materials. Machinesare very rare in the warehouse environment. Employees are humanbeings and, like all human beings, they do not work as precisely as amachine for a long period of time. The result is a higher fluctuationin quantity and quality compared to the work of a machine withinthe warehouse.The physical environment of production employees is often struc-tured in the form of a line. One workplace follows another work-place. The workplace is designed with less moving complexity forthe worker to ensure higher productivity. The worker usually hasa fixed workplace that does not require much walking. In ware-houses, these processes are structured in an area that is based onthe space required for storing products. This means that the pickerhas to make different kinds of movements when picking goods fromthe area. This fact and the lower degree of repetitiveness mean thatwarehouse employees have to make more complex movements thanproduction employees. In other words, the warehouse environment

18

2.4 Lean Warehousing: Transferring Lean Production into the Warehouse

has longer and higher fluctuating working cycles.The longer and more volatile working cycles also mean that thereare usually no set expectations about the output performance inwarehouses. If the estimated level is not reached, the warehouseleader accepts this situation. In production, failure to reach theestimated level will at least result in questions being raised by theleader on the shop floor.The leadership also changes in the warehouse environment. In pro-duction, the leader has an overview of his staff when they are inthe production line. Direct communication with all of his workersis possible with only a few restrictions. In the warehouse, the work-ers are spread all around. Direct communication with the workersis much more restricted. Another problem occurs in the warehousebecause the processes are not usually synchronized and the workerschange their work and their locations throughout the day. A workermight pick goods in the morning and pack them in the afternoon.This means that the worker reports to two different leaders withinone shift. In the production environment, the worker stays at oneproduction station the whole day.These environmental changes have to be taken into considerationwhen implementing the lean philosophy in the warehouse environ-ment.

2.4 Lean Warehousing: Transferring LeanProduction into the Warehouse

Section 2.1 identified that the key element of the lean philosophy isthe elimination of waste using a structured continuous improvementcycle. The environmental changes (see section 2.2) in the warehouseenvironment (see section 2.3) make it necessary to modify the leanphilosophy. More volatile and longer working cycles require an in-crease in the focus on measuring and controlling the processes. Pro-

19


cess controlling has to be done in a systematic and analytical way.The shop floor leaders face the challenge of workers being spreadaround the warehouse. Since it is not possible to lead the work-ers directly, a structured continuous improvement cycle has to beconsidered.Leadership, measuring, and the driving of improvements in a sys-tematic and analytic way all play an important role when trans-ferring lean approaches from the production environment into thewarehouse environment. Dehdari et al. (2011) considered this in hisdefinition of lean warehousing:

Lean warehousing is a leadership concept. This con-cept aims at a permanent, systematic, analytic, sustain-able, and measurable improvement of processes in thewarehouse environment. This happens with the con-tribution of all employees and with the goal of gainingawareness of perfection in each corporate action.

20

3 Literature Review:Measuring the Impact ofLean

To measure the impact of lean techniques on performance indica-tors, it is necessary to compare the development of an indicator forthe lean maturity (independent variable) with the development of aperformance indicator (assumed dependent variable) for an observedsystem (see section 1.1). By combining them, it is possible to ob-serve if the lean technique has an impact on performance indicators(see Hypothesis I-III in 1.2). A comparison between two samples isnecessary to observe if the lean technique has a higher positive influ-ence on performance indicators compared to other approaches. Onesample contains warehouses that focused on the lean approach andthe other sample has warehouses without that focus (see hypothesisIV in section 1.2). This measuring concept is equal for productionand warehouse environments. This chapter reviews how the existingstudies have considered this measuring concept and what kind ofmeasuring tools are available.

3.1 Measuring the Impact of Lean onProduction

Womack (2007) performed the most popular effectiveness measure-ments. In 1990, he compared the performance indicators of Toyota

21

3 Literature Review: Measuring the Impact of Lean

plants with performance indicators of other plants that did not fo-cus on the lean approach. Womack did not measure the maturitylevel of the Toyota plants. His goal was to show the superiority oflean over other production approaches and he did this by comparingplants that implemented lean with plants that did not.Table 3.1 shows 14 other research results with a total sample size of2318. Twelve studies show a positive impact of lean production onthe financial performance indicator. Claycomb et al. (1999),Fuller-ton and McWatters (2001) and Hofer et al. (2012) observed a corre-lation between a higher lean maturity and the positive developmentof performance indicators. Biggart (1997) and Jayaram et al. (2008)could not find a statistically significant influence of lean productionon performance indicators.These studies are based on surveys for identifying the maturity ofthe lean production implementation and to collecting the financialperformance indicators (Hofer et al., 2012). One advantage of a sur-vey is that it has a high number of samples that can be analyzedwith reasonable resources. A huge disadvantage is that these stud-ies often do not have enough evidence about the reliability of theresponse data. Often companies do not want to answer surveys,possibly because of the low maturity level of their organizations, orthey do not want to take the time to fill out the survey properly.Lean assessments are a more precise way of measuring the matu-rity of lean techniques (see Doolen and Hacker, 2005) and these areperformed by a professional in multi-day workshops.

3.2 Measuring the Impact of Lean onWarehousing

Augustin (2009, p. 94) surveyed the lean maturity of 53 warehousesin his lean warehousing survey. He evaluated the maturity levelwith just one question using a scale with five maturity levels. Au-gustin also did not make any statement about the influence of lean

22

3.2 Measuring the Impact of Lean on Warehousing

Au

tho

r(s)

Sam

ple

Dep

en

den

t

vari

ab

le(s

)

Ind

ep

en

den

t

vari

ab

le(s

)

Em

pir

ical

Fin

din

gs

Inm

an a

nd M

ehra

(1993)

US

manufa

ctu

ring f

irm

s a

dopting J

IT

(N=

114)

RO

I, t

ota

l cost,

serv

ice

JIT

adoption

Regre

ssio

nC

om

pany p

erf

orm

ance im

pro

ves a

s a

result o

f JIT

adoption

Big

gart

(1997)

US

manufa

ctu

ring f

irm

s a

dopting J

IT

(N=

106)

RO

AJIT

adoption

Regre

ssio

nN

o e

vid

ence o

f a s

ignific

ant

eff

ect

of

lean p

roduction a

doption

on R

OA

is f

ound

Cla

ycom

b e

t al. (

1999)

US

manufa

ctu

ring m

anagers

(N=

200)

RO

S,

RO

I, p

rofit,

pro

fit

gro

wth

JIT

adoption

Regre

ssio

nJIT

use w

ith c

usto

mers

results in b

ett

er

financia

l perf

orm

ance

Cla

ycom

b e

t al. (

1999)

US

manufa

ctu

ring m

anagers

(N=

200)

RO

S,

RO

I, p

rofit,

JIT

adoption

Regre

ssio

nT

he g

reate

r th

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hare

of

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nsactions,

the g

reate

r R

OI, R

OS

and c

om

pany p

rofita

bili

ty

Calle

n e

t al. (

2000)

Canadia

n m

anufa

ctu

ring p

lants

(N=

100)

Pro

fita

bili

ty,

tota

l costs

JIT

adoption

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ssio

nJIT

adoption r

esults in low

er

costs

and h

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pro

fits

.

Fulle

rton a

nd M

cW

att

ers

(2001)

US

manufa

ctu

ring f

irm

s a

dopting J

IT

(N=

95)

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fita

bili

ty im

pro

vem

ent

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adoption

AN

OV

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reate

r JIT

im

ple

menta

tion r

esults in g

reate

r pro

fita

bili

ty

impro

vem

ent

Germ

ain

et

al. (

1996)

US

manufa

ctu

ring m

anagers

(N=

200)

RO

S,

RO

I, p

rofit

JIT

adoption

Regre

ssio

nJIT

results in g

reate

r financia

l perf

orm

ance r

ela

tive t

o

industr

y p

eers

.

Kin

ney a

nd W

em

pe (

2002)

US

manufa

ctu

ring f

irm

s a

dopting J

IT

(N=

201×

2)

Pro

fita

bili

ty,

RO

AJIT

adoption

Regre

ssio

nP

rofita

bili

ty a

nd r

etu

rn o

n a

ssets

im

pro

ve a

fter

JIT

adoption

Fulle

rton e

t al. (

2003)

Manufa

ctu

ring f

irm

s

(N=

253)

Pro

fita

bili

ty,

cash f

low

marg

in,

RO

A

Lean p

roduction

imple

menta

tion

Regre

ssio

nT

hre

e lean p

roduction p

ractice b

undle

s a

re a

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ted w

ith

gre

ate

r com

pany p

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orm

ance

Mats

ui (2

007)

Japanese m

anufa

ctu

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lants

(N=

46)

Manufa

ctu

ring c

ost

JIT

adoption

Canonic

al

corr

ela

tions

JIT

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duction s

yste

ms c

ontr

ibute

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petitive

perf

orm

ance o

utc

om

es s

uch a

s low

er

manufa

ctu

ring c

osts

Jayara

m e

t al. (

2008)

Auto

part

s m

anufa

ctu

rers

(N=

57)

Pro

fita

bili

ty,

RO

ALean p

roduction im

ple

menta

tion

SE

MC

om

pany p

erf

orm

ance is n

ot

sig

nific

antly a

ffecte

d

by lean p

roduction

Fulle

rton a

nd W

em

pe (

2009)

Manufa

ctu

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xecutives

(N=

121)

RO

SLean p

roduction

imple

menta

tion

SE

MLean p

ractices h

ave a

direct

and m

edia

ted p

ositiv

e e

ffect

on

financia

l perf

orm

ance

Yang e

t al. (

2011)

IMS

S s

urv

ey d

ata

(N=

309)

RO

S,

RO

ALean

manufa

ctu

ring

SE

MLean p

roduction h

as a

sig

nific

ant

positiv

e im

pact

on f

inancia

l

perf

orm

ance

Hofe

r et

al. (

2012)

Associa

tion f

or

Opera

tions M

anagem

ent

in

manufa

ctu

ring o

rganiz

ations (

N=

229)

RO

S,

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ry

develo

pm

ent,

ELI

Lean p

roduction

imple

menta

tion

Varim

ax

Lean p

roduction h

as a

positiv

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ffect

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inancia

l perf

orm

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ELI=

Em

piric

al Leaness Indic

ato

r, J

IT=

Just

in T

ime,

RO

A=

Retu

rn o

n A

ssets

, R

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Retu

rn o

n Investm

ent,

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Retu

rn o

n S

ale

s,

SE

M =

Str

uctu

ral equationm

odelin

g,

US

= U

nited S

tate

s

Table3.1:

Lean

Prod

uctio

neffi

ciency

stud

ies(

Autho

r’sillustrationba

sedon

Doo

lenan

dHacker,

2005)(Inm

anan

dMeh

ra,1

993;

Biggart,1

997;

Claycom

bet

al.,1999;F

ullerton

and

McW

atters,2

001;

Germainet

al.,1996;K

inne

yan

dWem

pe,2

002;

Fullerton

etal.,

2003;Matsui,2007;Jayaram

etal.,2008;Fu

llerton

andWem

pe,2009;Ya

nget

al.,

2011;H

ofer

etal.,2012;?

)

23


warehousing on performance indicators. Overboom et al. (2010) de-veloped a more detailed assessment for his study that was based onqualitative measures. He published a method for measuring leanmaturity that was based on his analyses of Web pages, a question-naire, and structured interviews of two logistics service providers.However, like Augustin, Overboom did not establish a link betweenlean maturity and performance indicators. Sobanski (2009) also de-veloped a lean assessment for his warehouses within his study. Heverified his assessment and the correlation between subjects with asample size of 25 warehouses. Standard processes and visual man-agement were two of the areas he studied. He assumed a positiveimpact of lean on performance indicators for his study. Sobanski(2009) did not test his assumption by relating the assessment re-sults to the performance indicators of the warehouses.The lack of lean maturity assessments that also consider performanceindicators motivated Mahfouz (2011) to develop a new lean assess-ment for his study. He evaluated a leanness index with a sample sizeof five warehouses in Ireland. The Mahfouz study was also based ona questionnaire but it included some operational and tactical per-formance indicators. The cycle time is an example of an operationalperformance indicator and the number of on-time delivery orders isan example of a tactical performance indicator. Mahfouz used theperformance indicators to quantify the results of the lean maturityassessment. He did not analyze the effect of lean approaches onoperational or even financial performance indicators.Augustin (2009), Sobanski (2009) and Mahfouz (2011) concentratedon measuring the level of lean maturity. The level of performance in-dicator development was considered in the Mahfouz study but onlyto support the level of evidence of the lean maturity. The Distri-bution Center Reference Model (DCRM) (see Wisser, 2009) focuseson the level of performance indicators. The DCRM is based on avery sophisticated metric of performance indicators for generatingan assertion about the leanness of a warehouse. Unfortunately, theDCRM does not include a metric to evaluate the maturity of the

24

3.3 Tools for Measuring Lean Warehousing

lean techniques.


Data for the abscissa and ordinate is required to test the hypothesesthat were defined in section 1.2. Section 3.1 demonstrated that thesekinds of comparisons are a mature and standard way of analysingthe impact of lean approaches on performance indicators. Section3.2 showed that there is a gap between the warehouse environmentand the production environment in terms of the level of evidence ofthe lean impact on performance indicators.Two different measurement tools are necessary to close this gap.The first measurement tool is used to evaluate the maturity of thelean approach in the warehouse environment and the other is used tomeasure the performance indicators. The existing tools are discussedbelow.

3.3.1 Lean Warehousing Maturity Assessments

Maturity assessments make it possible to allocate the relative posi-tion of a selected domain within a maturity model. The maturitymodel consists of a set of criteria that are often ordered on a five-point Likert scale. Usually, level one represents the minimum re-quirement and five represents the highest achievable maturity level(Bruin et al., 2005).Most assessments verify if a standard has been documented but lackthe questions that would verify if the written standard is also exe-cuted. If a high level of evidence of the maturity of a selected ware-house is required, it is also necessary to test the execution. Severallean maturity assessments are in use today.The first step in identifying the most suitable lean maturity assess-ment for this thesis was to get an overview of the existing maturity

25


assessments. More than 70 maturity assessments were identified byreviewing and researching three scientific databases and the inter-net and by questioning experts. Seventeen maturity assessments re-mained after the assessments that did not focus on the lean approachwere eliminated. Figure 3.1 shows these 17 assessments. These 17lean maturity assessments were evaluated again using five criteriawith three levels of fulfillment: fully, partially, and not.The first criterion is Lean Focus. This criterion questioned the depthof lean focus. If, for example, an assessment only asks questionsabout the Just in Time implementation and no other lean tech-niques, then this assessment would partially fulfill the first criterion.The second criterion is Verified Execution, which identifies if the ma-turity assessment verified the execution of the lean approach. Thisis related to the point mentioned earlier that most assessments onlydetermine if a standard is documented. The third criterion is NotSurvey Based and this examines the collection of the data. The datais more reliable and objective if it is not survey based and if it is pro-vided by different individuals in the warehouse. The fourth criterion,Warehouse Focus, determines the depth of the focus on warehouseoperations. For example, this criterion is used to determine if theassessment evaluates the warehouses processes (see section 2.2). Thefifth criterion is Tested in Practice and the purpose of this criterionis to determine if the assessment asks questions about the testing oflean techniques in practice.Fullerton et al. (2003) focused on the lean approach but they onlycovered the Just in Time technique with their research and missedothers. Fullerton et al. (2003) did not focus on warehouse operationsand instead focused on the production environment.The Lean Enterprise Self-Assessment Tool (LESAT) was developedby researchers of the Massachusetts Institute of Technology (MIT).The LESAT is based on the Capability Maturity Model for Soft-ware (CMM) and focuses on the lean enterprise and not, specifically,on the warehouse environment. The strength of the CMM is thatit is not survey based and it was developed by science for use in

26


Full fulfilled Partial fulfilled Not fulfilled

Lean

Focus

Verified

Exce-

cution

Not

Survey

Based

Ware-

house

Focus

Tested

in

Practice

Fullerton, McWatters and Fawson

(Fullerton et al., 2003)

Lean Enterprise Self-Assessment

(Hallam, 2003)

Perez and Sanchez

(Perez et al., 2000)

Panizzolo

(Panizzolo, 1998)

Shah

(Shah, 2003)

Jordan and Michel

(Jordan et al. 2001)

The 360° Lean Audit

(Dollen et al. 2003)

Lean Company Survey


HPEC Assessment


Lean Checklist Self-Assessment


Lean Business Assessment


How Lean is Your Culture?


Dell Business Assessment

(Shan, 2008)

CMMI for Services

(CMMI Product Team, 2010)

Overboom

(Overboom , 2010)

Sobanski

(Sobanski, 2011)

Mahfouz

(Mahfouz, 2011)

BPS Assessment V. 3.1.

(Bosch2012a)

Figure 3.1: Lean Maturity Assessment overview (Fullerton et al.,2003)(Hallam, 2003; Pérez and Sánchez, 2000; Panizzolo,1998; Shah, 2003; Jordan and Michel, 2001; Doolen andHacker, 2005; Shan, 2008; CMMI Product Team, 2010;Overboom et al., 2010; Sobanski, 2009; Robert BoschGmbH, 2012)

27


the aerospace industry. It has also been verified and modified sev-eral times (Hallam, 2003). Pérez and Sánchez (2000) and Panizzolo(1998) used field-based surveys but they did not cover all warehouseoperations. They verified their theory with the help of a small sam-ple size in Spain and Italy. Shah (2003) and Jordan and Michel(2001) did have higher sample sizes but their research is based onthe survey-based approach. They also did not focus on warehousingoperations.In addition to six scientific research-based lean assessments that arealso considered in this section, Doolen and Hacker (2005) describedseveral lean maturity tools that were developed and used in theindustrial environment. None of them focused on warehouse oper-ations but two of them partially verify the execution of the leanapproach. The Lean Company Survey and HPEC questioned therole of the performance indicator to determine the outcome of leanimplementation. Shan (2008) and the CMMI Product Team (2010)also did not focus on warehouse operations but these assessmentsare often used in practice.As discussed in chapter 3, Overboom et al. (2010), Sobanski (2009),and Mahfouz (2011) developed lean warehousing assessments. Theirassessments failed to cover major warehouse operations (see section2.2) or were not conducted with a large enough sample size (Mah-fouz, 2011). In addition to this, their research does not determine if astandard has been executed. This is missing in all of the assessmentsthat have been analysed so far.Robert Bosch GmbH (2012) developed the only lean maturity assess-ment that has a focus on the existence and execution of implementedlean techniques and verified them with performance indicators. Thisassessment has been used multiple times in more than 290 plants indifferent business sectors all over the world. This assessment is notsurvey based. Unfortunately, it focuses on production and only cov-ers some of the warehouse processes that are mentioned in section2.2.

28


3.3.2 Lean Warehousing Performance Indicators

To measure the impact of lean approaches on performance indica-tors, we need to measure performance indicators in addition to mea-suring the lean maturity. Depending on the intensity of the effortsfor implementing lean techniques into a warehouse, the impact couldvary when measuring a performance indicator that includes all areaswithin a warehouse. In the beginning of a lean journey, some ware-houses might implement the lean approach in isolated warehouseareas. In this scenario, a measurement that uses a performance in-dicator that covers all warehouse areas would not represent the trueeffect: the effects of other areas without lean implementations wouldinfluence or even overlap the effect from the selected area.For example, the decision is made to standardize the processes ina warehouse and a shop floor cycle with workforce management isimplemented in a picking area. Measuring the performance of theentire warehouse and drawing conclusions about the lean impactwould not represent the true effect. This is because the effects ofother areas influence the overall performance indicator. It is ratherlike seeking to measure the heat of a small flame located on oneside of a room but doing so by measuring the room temperatureon the other side and concluding that the flame does not affect itsenvironment even though the temperature close to the flame is high.A system is required that will measure precisely at a specific level andcover the effect that different performance indicators have on eachother. Key Performance Indicator (KPI) trees fulfil this requirement(see Scheer, 2005). These kinds of KPI trees are in use at Boschand are named in the Bosch methodology as Bosch Key PerformanceResult (KPR) and KPI Trees. The structure of the Bosch KPR/KPITree is described in figure 3.2. In general, it has four key performancelevels: the top KPR, the value stream KPR, the monitoring KPI,and the improvement KPI level.The result KPR level includes the top KPR for a selected warehouse,involving such aspects as total warehouse costs. The value stream

29


KPR level describes the costs for the different value streams withinthe warehouse, including such aspects as the cost for distributing afull pallet. The KPI for a specific area within the warehouse is mea-sured at the monitoring level. For example, the KPI could evaluatethe productivity of the packaging area or the value stream for thecost for distributing a full pallet. The most detailed measuring isdone at the improvement level. These measurements could includethe quantity of pallets that packer one is packing today. The KPIat the improvement level are usually used for concrete improvementwork.All of these levels are linked with each other. Changes in one levelwill be transmitted to the other levels. For example, the productiv-ity KPI of one packaging team will influence the productivity KPIof the total packaging area and even the total cost KPR of the en-tire warehouse but with less intensity because other teams, like thepickers, also influence the total cost of the warehouse.The purpose of this thesis is to measure the impact of lean ap-proaches on performance indicators. By measuring this, we have toconsider that lean is not something that decision makers want tohave and they can buy and then it is done. Usually, a seed has tobe planted in a specific area of the warehouse. If the people aroundthis seed take care of it, it will grow and spread around the wholewarehouse. This will take time and, as long it is not spread aroundthe whole warehouse, the specific area where it grows has to be mea-sured. The effect of this area might have such a big impact on theKPR that it can be noticed by observing the KPR.A KPR/KPI Tree can be used to recognize the impact in the differentareas and at the different levels. The four levels of the KPR/KPITree make it possible to measure the impact of implemented leantechniques with different levels of penetration.The full implementation of a KPR/KPI Tree with all four levelswithin a warehouse is rare today. The KPIs for the improvementlevel are usually used for concrete improvement work and are oftennot measured constantly. The value stream KPRs are difficult to

30


Val

ue

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re3.2:

KPR

/KPI

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exam

ple

31


measure within warehouses because several, if not hundreds, of valuestreams are merged in the warehouse. Resources are usually notdedicated to one value stream and one task often influence severalvalue streams. As a result, a precise measurement would take ahuge amount of effort. The monitoring KPIs are also hard to find inwarehouses even though they take less effort to measure. Since thestructure of a warehouse is based on functional areas, leaders allocatecapacities to these areas and usually know the daily output. Becausethis is known, if the monitoring KPI are implemented they usuallystill focus on one area and do not cover all areas of the warehouse.Even result KPRs are not common in each warehouse but they arethe most common performance indicators that are measured.A fully implemented KPR/KPI Tree in each warehouse would bedesirable for a precise measurement. Considering the current statusof the available performance indicators in warehouses, it is realisticto focus on the result KPR and monitoring KPI if they are available.

3.4 Conclusion of the Literature Review

The studies in the production environment analysed the impact oflean techniques on performance indicators. Several studies with ahigh sample size analysed the impact by combining the results oflean maturity studies and performance indicators. A positive impactof lean techniques on performance indicators is backed by severalindependent studies (see section 3.1).Some studies could be found in the warehouse environment thatanalysed the lean maturity using a lean assessment. Other stud-ies analysed performance indicators to determine the lean matu-rity. Unlike the production environment, no study was found inthe warehouse environment that combined the two factors: neitherin pure lean warehouses for analysing a correlation between highermaturity and higher performance nor between lean warehouses andwarehouses without lean techniques for analysing how each group

32

3.4 Conclusion of the Literature Review

performs (see section 3.2).In addition to this, the existing lean maturity assessments for ware-houses do not fulfil the desired minimum criteria (see subsection3.3.1). The Bosch Production System Assessment V. 3.1 has thehighest level of fulfilment of the criteria but it does not focus solelyon warehouse operations.In conclusion, there is a huge gap between the levels of evidencefor the impact of lean techniques on performance indicators in thedifferent environments. It is not currently possible to determinethe impact of lean approaches on performance indicators within thewarehouse environment. Thus, a new study is needed to close thisgap in evidence and analyse the hypotheses described in section 1.2.Unfortunately, none of the existing lean maturity assessments thatfocus on warehouses are adequate enough to fulfil the desired mini-mum criteria. The Bosch Production System Assessment V. 3.1 hasthe highest level of fulfilment of the criteria but it focuses on produc-tion and only covers some of the warehouse processes as mention ear-lier. An adaption of this assessment for the warehouse environmentwould fulfil the desired criteria and enable further studies within thissubject.

33


34

4 Bosch Logistics WarehouseAssessment

Within the scope of this study, the Bosch Production System Assess-ment V. 3.1 was adapted for the warehouse environment and trans-formed into the Bosch Logistics Warehouse Assessment (BLWA).The BLWA was developed for the Warehouse Excellence project ofBosch in the beginning of the year 2010 (compare also Dehdari etal. 2012). Several sources and experts were consulted for the trans-formation of the Bosch Production System Assessment V. 3.1 intothe BLWA.The company Bosch and the Warehouse Excellence project will bediscussed later in chapter 5. This chapter describes the develop-ment and then the structure of the assessment. The purpose of thischapter is to give a general overview of how the assessment works.

4.1 Development of the Bosch LogisticsWarehouse Assessment

The BLWA was developed in several steps. In the first step, thekey literature for the lean approach was re-evaluated (see section2.1). The main components of the lean approach that needed to beassessed in the warehouse assessment were identified based on thedefined requirements. The existing lean maturity assessments thatfocused on the warehouse environments (see section 3.2) were alsoanalyzed. The goal was to find the components that could be used

35

4 Bosch Logistics Warehouse Assessment

for the Bosch Logistics Warehouse Assessment.The second step involved the creation of a new structure. The BoschProduction System Assessment V. 3.1 only focused on some ware-house operations. The warehouse processes (see section 2.2) thatwere not covered by the processes in the Bosch Production SystemAssessment V. 3.1 were included. Matched with the content of theliterature and other assessments, the first draft of the BLWA wasfinalized.The first draft of the BLWA was reviewed by experts from the BoschProduction System. After their feedback was included, guideline-based interviews were used to have the first draft checked by expertsfrom several organizational levels within Bosch. These experts in-cluded representatives from the corporate level, the business unit,warehouse leaders, and shop floor personnel. Experts from the Karl-sruhe Institute of Technology were also questioned.A test version of the BLWA was released after the feedback fromthe interviews was incorporated. The test version was tested inthree warehouses. The feedback from the test version was takeninto consideration and, after a final review by the corporate BoschProduction System expert team, the BLWA was released.

4.2 Structure of the Bosch LogisticWarehouse Assessment

The structure of the BLWA is shown in figure 4.1.The structure is divided into three segments: the Continuous Im-provement Process (CIP), overall subjects, and warehouse processes.The CIP consists of the System-CIP and Point-CIP, which are Bosch-specific terms that were developed by Bosch Production System ex-perts (Robert Bosch GmbH, 2012).The System-CIP pertains to process and value stream design. Itaims to capture the current value stream status with techniques

36

4.2 Structure of the Bosch Logistic Warehouse Assessment

CIP Overall Subjects

Warehouse Processes

3.6

Inco

min

g G

oo

ds

3.5

Sto

rage

3.

4 P

icki

ng

3.3

Pac

kagi

ng

3.2

Out

goin

g G

oo

ds

3.1

Ove

rhea

d

1.1

Sys

tem

- C

IP

Vor

schl

ag 2

– n

icht

bla

u au

f bla

u

1.2

Po

int -

CIP

2.1

Failu

re P

reve

ntio

n S

yste

m

2.2

Em

plo

yee

Invo

lvem

ent

2.3

Sta

ndar

dize

d W

ork

Figu

re4.1:

Bosch

LogisticsWareh

ouse

Assessm

entstructure

37


like value stream mapping (Rother and Shook, 2008), layouts, andspaghetti diagrams (Flinchbaugh, 2009). The value stream targetcan also be designed using the true north alignment and the busi-ness requirements of the selected warehouse. System-CIP projectswith target conditions must be defined to close the gap betweenstatus and target. Target conditions consist of a standard, a perfor-mance indicator goal, and a stabilization criterion. The standard hasto be defined, easy to understand, described clearly, and displayedon-site. It also must be possible for workers to meet the standardand it must be measurable. The measurability of the standard isimportant because the second element of the target condition, theperformance indicator, would not make any sense otherwise. Com-prehensive limits also have to be defined to describe the stabilizationcriterion. Finally, adherence to common standards, guidelines, andlegal restrictions has to be ensured. Once these target conditionshave been implemented, they are handed over to the Point-CIP.The Point-CIP is a method for process stabilization and improve-ment. It is comprised of five elements: target condition, quick re-action system, regular communication, sustainable problem solving,and process confirmation. This method continues to be used untilthe stabilization criteria are met permanently.The definition for the Point-CIP target condition is the same asthe one for the System-CIP. The quick reaction system consists ofa trigger for reactions, defined responsibilities, measures, and anescalation scheme. The following questions are used to define a quickreaction system:

• Who? - Responsible person for taking action – starting at theoperator level

• How long? - Time limit for problem solving at each level• What? - Systematic description of measures, how to documentfacts

• How? - Problem solving method to be used

38


• Output? - Records to be created (for example, problem solvingsheet)

• Valid? - 24 hours, Monday to Sunday

Regular communication consists of the definition of the participants,the tools used, and a schedule. Regular communication can be es-tablished at many points within a warehouse: it can be definedat different hierarchical levels or in different areas. An aligned se-quence of the various types of regular communication supports theinput and output of information for the different meetings. Regularcommunication supports the process of sustainable problem solvingand the exchange of information between all departments by rulesdefined at all levels.The following elements are important for sustainable problem solv-ing: a root cause analysis, sustainable countermeasures, sustainableproof of rollout to other areas, prevention of re-occurrences, and astandardization of the result. Sustainable problem solving should bedone in a team with problem solving experts, leaders from the area,and shop floor workers.Process confirmation is a verification of the adherence of the oper-ators to a standard. It also contains an analysis of any deviationsfrom the standard that occurred. Standards have to be checked fre-quently because of fluctuating process outputs, varying parts (forexample, changing the combination of parcels), and changing oper-ators (compare also section 2.3).The strength of the System-CIP and the Point-CIP is the linkingand combination of the elements with each other. The followingreal-world example will make this clear. The System-CIP cyclehighlighted that a new milk run was necessary in one of Bosch’sproduction warehouses in the south of Europe. The target condi-tion for the new milk run was defined: a timetable, a cycle time of 16minutes for the route, and plus or minus 1 minute as the stabiliza-tion criterion. After the milk run was implemented and the workers

39


were trained, the target condition was handed over to the Point-CIP. Hours after the implementation, the milk run driver reached astation on his route with a 1.5 minutes delay. The quick reactionsystem helped the milk run driver react accurately. He escalated thedelay to his supervisor and asked for the support of another worker.The quick reaction system also defined that he had to documentthe delay using the questions mentioned above in a regular commu-nication. In the next regular communication, the supervisor askedthe driver for the information about the delay of the milk run andbecause this was a newly defined critical implementation he decidedto establish a problem solving team to understand why the milk runcame late. After investigating the problem in a structured way, theteam discovered that the milk run collided with another milk runevery second or third cycle. They performed some tests and pro-posed a solution to reschedule the new milk run. After discussingthis proposal in the standard management meeting, the standardof the milk run was changed. As part of the process confirmation,the cycle time adherence was checked daily for three months. Theproblem was only considered to be solved permanently if the milkrun did not come late again during this time period.The roots of the System-CIP and the Point-CIP are in the Japaneseautomotive production systems. A lot of the lean systematic men-tioned in section 2.1 by various authors, and in particular, ana-lyzed and summarized by Dehdari and Schwab (2012) is covered bySystem- and Point-CIP. For example, the general topics of failureprevention systems, employee involvement, and standardized work.The main warehouses processes in the assessment represent the pro-cesses described in chapter 2.2. Only input and output are includedunder storage. A detailed list of the topics that are covered is shownin the figures 4.2 and 4.3.The topics also have subtopics. To cover each subtopic, several crite-ria have to be assessed with different maturity levels. The maturitylevels start at 0 and go up to the level 4. The higher the level, themore challenging and mature the criteria. Each criterion has a con-

40


1.1

Syste

m-C

IP

1.2

Po

int-

CIP

2.1

Fa

ilu

re

Pre

ven

tio

n S

yste

m

2.2

Em

plo

yee

Inv

olv

em

en

t

2.3

Sta

nd

ard

ized

Wo

rk

Busin

ess

requirem

ents

Ta

rget

conditio

nW

ork

Conte

nt

Involv

em

ent

Covera

ge o

f

Sta

ndard

ized W

ork

Valu

e S

tream

pla

nnin

g

Quic

k r

eactio

n

syste

mV

isualiz

atio

nT

arg

et

Deplo

ym

ent

Te

am

Lead

Vis

ualiz

atio

n

Identificatio

n o

f

impro

vem

ent

activitie

s

Regula

r

com

munic

atio

n

Qualif

icatio

n /

Tra

inin

gQ

ualif

icatio

n

Defin

itio

n o

f

targ

et

conditio

ns

Susta

inable

pro

ble

m s

olv

ing

Syste

m-C

IP p

roje

cts

Pro

cess

confirm

atio

n

Poin

t C

IP a

reas

Ta

rget

derivatio

nK

PI-

eff

ect

Err

or

rate

Multi-skill

ed

Opera

tors

Sta

bili

ty

Syste

m C

IP c

ycle

sQ

ualit

y o

f

pro

ble

m s

olv

ing

Opera

tor

Involv

em

ent

5S

Sta

tus

Impro

vem

ent

focus

Leaders

hip

Involv

em

ent

Pro

ductivity

Leaders

hip

involv

em

ent

VS

M-Q

ualit

y

Ta

rget

achie

vem

ent1.

CIP

2.

Overa

ll S

ub

jects

Conception Execution

Figu

re4.2:

Bosch

Logistic

Wareh

ouse

Assessm

entTo

pics

Part

1

41


3.1

Overh

ead

3.2

Ou

tgo

ing

Go

od

s

3.3

Packag

ing

3.4

Pic

kin

g

3.5

Sto

rag

e

3.6

Inc

om

ing

Go

od

s

Qualific

atio

nO

rganiz

atio

nP

ackaged G

ood

Pic

kin

g P

rocess

Sto

rage

Te

chnic

/Layout

Receiv

ing P

rocess

Te

chnic

al E

quip

ment

Packagin

g M

ate

rial

Org

aniz

atio

nal S

yste

mS

tora

ge C

riteria

Entry

/Bookin

g

Vis

ualiz

atio

nP

ackagin

g P

rocess

Info

rmatio

n S

yste

mIn

vento

ry M

anagem

ent

Inspectio

n

Vis

ualiz

atio

nV

isualiz

atio

nV

isualiz

atio

nV

isualiz

atio

n

Tim

e w

indow

adhere

nce

Lead tim

e o

f the

packagin

g p

rocess

Lead tim

e o

f the

pic

kin

g p

rocess

Lead tim

e o

f the

sto

rage p

rocess

Tim

e w

indow

adhere

nce o

f

receiv

ing

Bala

ncin

g o

f com

ple

te

ship

pin

g p

rocesses

Packagin

g e

rror ra

teP

ickin

g e

rror ra

teS

tora

ge e

rror ra

teB

ala

ncin

g o

f receiv

ing

pro

cesses

Lead tim

e o

f the

ship

pin

g p

rocess

Lead tim

e o

f the re

ceiv

ing

pro

cess

Dis

patc

h e

rror ra

teR

eceiv

ing e

rror ra

te

Handlin

g s

teps

Handlin

g s

teps

ConceptionExecution

3. W

are

ho

us

e P

rocesses

Figure4.3:B

oschLogistic

Warehouse

Assessm

entTopics

Part2

42

4.3 Intermediate Result: Measuring Systematic

cept dimension and an execution dimension that are linked together.This link ensures that the documented standard will be checked tosee if it is executed. Figure 4.4 explains the relationship betweentopics, components, criteria, and maturity levels. The link betweenconcept and execution is explained by the example in figure 4.5. Inthis figure, the standard for time windows is asked for at the con-ceptual stage. During execution, the adherence to the time windowis sought.

4.3 Intermediate Result: MeasuringSystematic

To evaluate the impact of lean techniques accurately, we need tomeasure the maturity of the lean entity and the performance changewith performance indicators. Chapter 4 identifies a gap between thematurity of the methodology measuring the impact of leanness in aproduction environment and that in a warehouse environment. De-tailed measurements are needed to gain reliable knowledge. This iswhy section 3.3.1 identifies BPS Assessment V3.1 as the most precisein terms of the requirements. However, an adaption was necessarybecause it was designed for the production environment. The BLWAfulfills these adaptation needs. Together with the KPR/KPI Tree(see sub section 3.3.2), accurate measurement techniques are nowavailable to test the hypotheses described in section 1.2.

43

4 Bosch Logistics Warehouse AssessmentW

are

ho

us

e A

naly

sis

1.0

M

atu

rity L

evel

Po

ints

W

eig

ht-

ing

Overa

ll

Po

ints

N

o.

To

pic

0

1

2

3

4

3.6

Incoming Goods

CONCEPT

Req

uire

men

ts

Req

uire

men

ts

Req

uire

men

ts

Req

uire

men

ts

Req

uire

men

ts

R

ece

ivin

g P

roce

ss:

Tim

e w

ind

ow

s d

efin

ed fo

r

- the re

ce

ivin

g (e

.g. tru

ck a

rrival, u

nlo

adin

g,

depa

rture

)

or

- the b

oo

kin

g p

roce

ss (in

cl. P

ut-a

wa

y).

Rece

ivin

g P

roce

ss:

Tim

e w

ind

ow

s d

efin

ed fo

r

- the re

ceiv

ing (e

.g. tru

ck a

rrival,

unlo

adin

g,

depa

rture

) in

a re

petitive

pa

ttern

with

ma

x. 3h p

er tru

ck

an

d

- the b

ookin

g pro

cess (in

cl. P

ut-a

wa

y).

Rece

ivin

g P

roce

ss:

- as le

vel

2

- Sta

nd

ard

ize

d w

ork

is d

escrib

ed (e

.g. S

tanda

rdiz

ed

Work

sheet) fo

r the co

mple

te re

ceiv

ing pro

cess,

in w

hich

the p

ack

ing unit is

ha

ndle

d.

- Re

packin

g a

ctiv

ities a

re re

duce

d by d

efin

ed p

ackin

g

pre

scrip

tion

s fo

r supplie

rs.

Rece

ivin

g P

roce

ss:

- as le

vel

3

- Opera

tors

are

work

ing >

90

% o

f their w

ork

time

acco

rdin

g sta

nda

rdiz

ed w

ork

.

1

0.5

0.5

*(1*2

)

=

1

R

ece

ivin

g P

roce

ss:

- The re

ceiv

ing (e

.g. tru

ck a

rrival, u

nlo

adin

g,

bookin

g

pro

cess, p

ut-a

wa

y) is d

one in

a re

pe

titive

patte

rn.

Rece

ivin

g P

roce

ss:

- The re

ce

ivin

g is

contro

lled by ta

kte

d M

ilk-R

uns w

ith

fixed tim

es.

- Sta

nd

ard

ize

d o

pe

ratio

nal p

roce

du

res a

re d

efin

ed fo

r

the co

mple

te re

ceiv

ing pro

cess.

Rece

ivin

g P

roce

ss:

- as le

vel

2

- The s

tan

dard

s a

lso co

nta

in ta

rge

t Cyce

l-time

s fo

r

ea

ch p

roce

dure

, w

hic

h a

re s

ynch

ron

ized to

the M

ik-R

un

frequency

.

- Re

packin

g a

ctiv

ities a

re re

duce

d by d

efin

ed p

ackin

g

pre

scrip

tion

s fo

r supplie

rs.

Rece

ivin

g P

roce

ss:

- as le

vel

3

- Opera

tors

pro

cessin

g a

pa

ckin

g unit a

re w

ork

ing >

90

%

of th

eir w

ork

time a

ccord

ing sta

nda

rdiz

ed w

ork

. 2

E

ntry/B

ookin

g:

- Tw

o-le

vel

entry.

Entry/B

ookin

g:

- as le

vel

1

- En

try in

the s

yste

m is

done n

ot m

ore

than

on

e d

ay

afte

r goods a

re re

ceiv

ed

.

Entry/B

ookin

g:

- Sin

gle

-leve

l entry

- Entry in

the s

yste

m is

done n

ot m

ore

tha

n h

alf a

day

afte

r goods a

re re

ceiv

ed

.

Entry/B

ookin

g:

- as le

vel

3

- Entry in

the s

yste

m is

done n

ea

rly at th

e s

am

e tim

e a

s

goods a

re re

ceive

d (n

ot m

ore

tha

n 2

hours

late

r). 3

In

spectio

n:

- Inspectio

n in

ten

sity

depends o

n fa

ilure

rate

of s

up

plie

r

or co

mpa

rable

criteria

.

Inspectio

n:

- Supplie

r deliv

er

alre

ady fille

d out a

nd s

igned c

on

trol

do

cu

me

nts

with

their g

oods.

Inspectio

n:

- Fre

e tic

ke

t (Fre

ipa

ss) fo

r audite

d s

upplie

rs (e

. g. s

kip

-

quota

afte

r a d

efin

ed s

chem

e).

Inspectio

n:

- as le

vel

3

0

V

isua

liza

tion:

- Pla

nn

ed

an

d a

ctu

al tim

e w

ind

ow

s (e

.g. re

ceiv

ing

boa

rd) fo

r rece

ivin

g pro

cess a

re vis

ua

lize

d.

- Nu

mb

er o

f para

llel p

lan

ne

d tim

e w

ind

ow

s p

er h

ou

r is

mea

sure

d a

nd vis

ua

lized (e

.g. tru

ck a

rrival a

nd d

epa

rture

win

dow

s/h

).

- Vis

ua

lized la

ne

s fo

r inco

min

g goods (m

ark

ed flo

or a

nd

wa

ll labels

)

Vis

ua

liza

tion:

- Pla

nned a

nd a

ctua

l time w

indo

ws (e

.g. re

ceiv

ing boa

rd)

for re

ceiv

ing pro

cess a

nd

bookin

g p

roce

ss a

re

visua

lized

.

- Le

ad

time

of th

e re

ceiv

ing a

nd b

ookin

g p

roce

ss p

er

truck vis

ua

liza

tion

.

Vis

ua

liza

tion:

- as le

vel

2

- Wo

rk in

stru

ctio

n (T

arg

et T

ime a

nd S

tanda

rdiz

ed

Work

sheet) a

re a

vaila

ble

a

t work

pla

ces.

Vis

ua

liza

tion:

- as le

vel

3

3

V

isua

liza

tion:

- Pla

nn

ed

an

d a

ctu

al p

oin

t of tim

e (e

.g. re

ceiv

ing

boa

rd) fo

r rece

ivin

g pro

cess a

re vis

ua

lized

.

- Nu

mb

er o

f para

llel p

lanned re

ce

ivin

gs p

er h

ou

r is

mea

sure

d a

nd vis

ua

lized (e

.g. m

ilkru

nds/h

).

- Vis

ua

lized la

ne

s fo

r inco

min

g goods (m

ark

ed flo

or a

nd

wa

ll labels

).

Vis

ua

liza

tion:

- Pla

nned a

nd a

ctua

l poin

t of tim

e fo

r rece

ivin

g pro

cess

an

d b

ookin

g pro

cess a

re vis

ua

lize

d.

- Le

ad

time

of th

e re

ceiv

ing a

nd b

ookin

g p

roce

ss p

er

truck vis

ua

liza

tion

.

Vis

ua

liza

tion:

- as le

vel

2

- Wo

rk in

stru

ctio

n (P

QI a

nd S

tanda

rdiz

ed W

ork

sheet)

are

ava

ilable

a

t work

pla

ces.

Vis

ua

liza

tion:

- as le

vel

3

3

Avera

ge o

f all a

ch

ieved

po

ints

in c

on

cep

t level

2

EXECUTION

Req

uire

men

ts

Req

uire

men

ts

Req

uire

men

ts

Req

uire

men

ts

Req

uire

men

ts

Po

ints

Tim

e w

indo

w a

dhere

nce

of re

ceiv

ing:

> 7

0%

sch

edule

a

dhere

nce

(check

last m

onth

).

Tim

e w

indo

w a

dhere

nce

of re

ceiv

ing:

> 9

0%

sch

edule

a

dhere

nce

(check

last m

onth

).

Tim

e w

indo

w a

dhere

nce

of re

ceiv

ing:

> 9

0%

sch

edule

a

dhere

nce

(check

last 3

mo

nth

s).

Tim

e w

indo

w a

dhere

nce

of re

ceiv

ing:

- as le

vel

3

1

B

ala

ncin

g o

f rece

ivin

g pro

cesses:

- Wo

rk lo

ad

bala

nce

thro

ughout

the d

ay is

me

asu

red

(on b

asis

of p

ara

llel a

rriving supplie

s).

Ba

lancin

g o

f rece

ivin

g pro

cesses:

- Work

loa

d b

ala

nce

thro

ughout th

e d

ay w

as im

pro

ve

d.

Curre

nt le

vel

bette

r tha

n s

tartin

g le

vel

6 m

on

ths a

go

.

Ba

lancin

g o

f rece

ivin

g pro

cesses:

- Work

loa

d b

ala

nce

thro

ughout th

e d

ay w

as im

pro

ved,

positive

tre

nd o

r at a

sta

ble

targ

et le

vel

sin

ce m

in. 6

mo

nth

s.

Ba

lancin

g o

f rece

ivin

g pro

cesses:

- work

loa

d b

ala

nce

thro

ughout th

e d

ay w

as im

pro

ved,

positive

tre

nd s

ince

th

e la

st 1

2 m

onth

s o

r at a

sta

ble

targ

et le

vel

sin

ce m

in.

12

mo

nth

s.

0

Lea

d tim

e o

f the re

ceiv

ing pro

cess:

- Ave

rag

e le

ad

time

of th

e re

ceiv

ing pro

cess fo

r all

pa

cka

gin

g u

nits

is m

easu

red

.

Lea

d tim

e o

f the re

ceiv

ing pro

cess:

- The a

vera

ge le

ad tim

e o

f all p

ack

agin

g u

nits

is s

table

above

targ

et le

vel

or s

how

s a

positive

tre

nd o

ver th

e p

ast

6 m

on

ths.

Lea

d tim

e o

f the re

ceiv

ing pro

cess:

- The a

vera

ge le

ad tim

e o

f pa

cka

gin

g u

nits

is s

table

above

targ

et le

vel

or s

how

s a

positive

tre

nd o

ver th

e p

ast

12

mo

nth

s.

Lea

d tim

e o

f the re

ceiv

ing pro

cess:

- The a

vera

ge le

ad tim

e o

f pa

cka

gin

g u

nits

is s

table

above

targ

et le

vel

or s

how

s a

positive

tre

nd o

ver th

e p

ast

24

mo

nth

s.

0

R

ece

ivin

g erro

r rate

:

- Fa

ilure

s ca

used in

the re

ceiv

ing pro

cess a

re

me

asu

red

.

- A ta

rget le

vel

wa

s d

efin

ed.

Rece

ivin

g erro

r rate

:

- The e

rror ra

te is

sta

ble

or s

how

s a

positiv

e tre

nd fo

r

more

tha

n 6

mo

nth

s.

Rece

ivin

g erro

r rate

:

- The e

rror ra

te is

sta

ble

or s

how

s a

positiv

e tre

nd fo

r

more

tha

n 1

2 m

on

ths.

Rece

ivin

g erro

r rate

:

- The e

rror ra

te is

sta

ble

or s

how

s a

positiv

e tre

nd fo

r

more

tha

n 2

4 m

on

ths.

1

H

andlin

g s

teps:

- Ha

ndlin

g s

teps a

re co

unte

d.

Ha

ndlin

g s

teps:

- Reductio

n of h

andlin

g ste

ps o

n a

vera

ge e

very

6

mo

nth

s.

Ha

ndlin

g s

teps:

- Reductio

n of h

andlin

g ste

ps o

n a

vera

ge e

very

3

mo

nth

s.

Ha

ndlin

g s

teps:

- Reductio

n of h

andlin

g ste

ps o

n a

vera

ge e

ve

ry

mo

nth

s.

2

Avera

ge o

f all a

ch

ieved

po

ints

in e

xec

utio

n le

vel

1

Cu

rren

tly a

ch

ieve

d m

atu

rity le

vel in

sp

ecific

su

bto

pic

Ea

ch

line

rep

res

en

ts a

sp

ecific

su

bto

pic

, wh

os

e m

atu

rity le

ve

l is e

va

lua

ted

To

rea

ch

a

ce

rtain

ma

turity

lev

el in

a

su

bto

pic

,

the

pro

ce

ss

ha

s to

fulfill

the

sp

ecific

req

uire

-

me

nts

of

tha

t lev

el

A

su

bto

pic

is e

va

lu-

ate

d b

y

the

nu

mb

er

of its

ach

iev

ed

ma

turity

lev

el

Figure4.4:B

LWA

linkbetw

eenexecution

andconcept

44

4.3 Intermediate Result: Measuring Systematic

CIPOverall

Subjects

Warehouse

Processes

3.6

Inco

min

g

Go

od

s

3.5

Sto

rag

e

3.4

Pic

kin

g

3.3

Packag

ing

3.2

Ou

tgo

ing

Go

od

s

3.1

Overh

ead

1.1

Syste

m -

CIP

1.2

Po

int -

CIP

2.1

Fail

ure

Pre

ven

tio

n S

yste

m

2.2

Em

plo

yee I

nvo

lvem

en

t

2.3

Sta

nd

ard

ized

Wo

rk

Ware

ho

use

An

aly

sis

1.0

M

atu

rity

Level

Po

ints

N

o.

To

pic

0

1

2

3

4

3.6

Incoming Goods

CONCEPT

Req

uir

em

en

ts

Req

uir

em

en

ts

Req

uir

em

en

ts

Req

uir

em

en

ts

Req

uir

em

en

ts

R

eceiv

ing P

rocess:

Tim

e w

ind

ow

s d

efined

for:

•T

he r

eceiv

ing

(e.g

.

truck a

rriv

al,

unlo

adin

g,

depart

ure

)

Or:

•T

he b

oo

kin

g

pro

cess (

inclu

din

g

puting into

sto

rage)

Receiv

ing P

rocess:

Tim

e w

ind

ow

s d

efined

for:

•T

he r

eceiv

ing (

e.g

.

truck a

rriv

al,

unlo

adin

g,

depart

ure

)

in a

repetitive p

attern

with m

ax.

3h p

er

truck

An

d:

•T

he b

ookin

g p

rocess

(inclu

din

g p

uting into

sto

rage)

Receiv

ing P

rocess:

•A

s level 2

•S

tan

dard

ized

wo

rk

is d

escribed (

e.g

.

Sta

ndard

ized

Work

sheet)

for

the

com

ple

te r

eceiv

ing

pro

cess

•R

ep

ackin

g

acti

vit

ies a

re

reduced b

y defined

packin

g

instr

ucti

on

s f

or

supplie

rs

Receiv

ing

Pro

cess:

•A

s level 3

•O

pera

tors

are

work

ing >

90%

of

their w

ork

tim

e

accord

ing

sta

ndard

ized w

ork

1

…

…

…

…

…

…

Ware

ho

use

An

aly

sis

1.0

M

atu

rity

Level

Po

ints

N

o.

To

pic

0

1

2

3

4

3.6

Incoming Goods

EXECUTION

Req

uir

em

en

ts

Req

uir

em

en

ts

Req

uir

em

en

ts

Req

uir

em

en

ts

Req

uir

em

en

ts

T

ime w

indow

adhere

nce o

f re

ceiv

ing:

•>

70%

schedule

adhere

nce (

check

last m

onth

)

Tim

e w

indow

adhere

nce o

f re

ceiv

ing:

•>

90%

schedule

adhere

nce (

check

last m

onth

)

Tim

e w

indow

adhere

nce o

f re

ceiv

ing:

•>

90%

schedule

adhere

nce (

check

last

3 m

on

ths

)

Tim

e w

indow

adhere

nce o

f re

ceiv

ing:

•A

s level 3

1

…

…

…

…

…

…

Figu

re4.5:

BLW

Aexam

pleforlin

kedcrite

ria

45


46

5 Design of the Experiment

The previous chapters identified and developed the measuring met-hod. This chapter discusses the design of the experiment of thisstudy. This means that we get to know the structure of the obser-vation sample and the control sample that are parts of the designof the experiment. This includes the sample sizes, the warehousetypes (see section 2.2) and geographic regions of the warehouse lo-cations. The use of the measuring methods identified and developedin section 4.3 are also described. The analysis of the samples inthis chapter should make it possible to determine the validity of thedefined hypotheses (see section 1.2) in chapter 6.

5.1 Warehouse Excellence Group - theObservation Sample

The Bosch Group is a supplier of technology and services in theareas of automotive and industrial technology, consumer goods, andbuilding technology. These broad areas form a good cross section ofthe economy. The Bosch Group is made up of Robert Bosch GmbHalong with its roughly 350 subsidiaries and regional companies insome 60 countries, including over 800 warehouses. Nearly half ofthe Bosch warehouses are operated by Bosch and the other half arerun by logistics service providers.The performance of these warehouses is crucial to the success of thecompany. High delivery performance targets and quality require-ments have to be fulfilled. These warehouses also cause significant

47


costs. However, a Bosch internal observation study showed a gap inthe lean maturity level between the production and warehouse envi-ronment. In order to close this gap, the Bosch Group established apilot project to test and evaluate the adaptation of the Bosch Pro-duction System to the warehouse environment. The Bosch Groupalso decided that an additional goal of the project was to measurethe impact of the Bosch Production System on performance indica-tors. This impact measurement would form the basis for the decisionfor a worldwide rollout that would affect all 800 warehouses.The Bosch Group named the pilot project Warehouse Excellence.The Warehouse Excellence group was chosen randomly and con-sisted of 16 warehouses located in seven countries. These were com-prised of six distribution warehouses, seven plant warehouses, andthree raw material warehouses. These 16 warehouses handle threedifferent kind of businesses. The different businesses are automotivetechnology goods, industrial technology and consumer goods, andbuilding technology. Fourteen warehouses out of the 16 warehousesare single user warehouses and handle one business. Two of the 16warehouses are multi-user warehouses and each of them handle twodifferent businesses. In total, two are involved in industrial technol-ogy, 10 warehouses handle automotive technology, and six deal withconsumer goods and building technology.Eight of the warehouses were operated by Bosch and another eightby logistics service providers. These included three of the five biggestlogistics service providers in the world as measured by the turnover.All other detailed structural information are given in appendix Aand appendix B.

5.1.1 The Warehouse Excellence Project - LeanEmpowerment

The project ran from November 2010 to March 2012. Key perfor-mance indicators are available from January 2010 to December 2011.It is safe to assume that there was less focus on lean in the year 2010

48

5.1 Warehouse Excellence Group - the Observation Sample

than in the year 2011. So, ideally, these two years can be comparedwith each other. This section describes the empowerment program,milestones, and the available data that was gathered during thisperiod.

Empowerment

The actions undertaken within the warehouses were based on anempowerment program. Figure 5.1 shows the four elements of theempowerment program. The empowerment program is based onthe literature in section 2.1 and the experience of Bosch ProductionSystem experts. The aim is to enable the warehouse leader to drivethe continuous improvement process as per the lean warehousingdefinition given in section 2.4. As described in chapter 4, the System-CIP and Point-CIP play a significant role in the achievement of thatgoal. The empowerment program consists of four elements that aredescribed below.Each of the three Bosch Logistics Workshops (BLW) was held overtwo days. The workshop sought to introduce knowledge about theSystem-CIP (BLW I), the Point-CIP (BLW II), and special problemsolving (BLW III) in a practical way. For example, the theory behindvalue stream mapping was taught in an hour-long classroom lecture.The participants then tried value stream mapping within instuctor-led groups in the warehouse. A separate session was necessary forproblem solving because it is so highly complex. The first workshoptook place in December 2010, the second in March 2011, and thelast in June 2011.The Bosch Logistics Learning Groups (BLLG) helped warehousemanagers solve upcoming problems together. Rotational one anda half day visits to different warehouses also supported knowledgetransfer between warehouses. The participants met six to seventimes during the project period.The Bosch Interdisciplinary Local Teams (BILT) supported knowl-edge transfer from the workshops to the warehouse operations of

49


the participants. Lean experts coached warehouse managers to ad-just and implement the learned methodologies. The coaches alsoprovided detailed feedback to those in leadership roles.The final component was the exchange of good practices with thelean management working group in the warehouses of the GermanLogistics Association (BVL). In this working group, several compa-nies identified the lean success factors and tested this out in pilotwarehouses of their own. Furmans and Wlcek (2012) summarizedand published the results.

Milestones

The milestones provided a direction and challenged the warehouseswithin their lean activities. These milestones were to be reachedduring the project period. The first milestone required value streammapping, value stream design, and a project plan. The project planwas to close the gap between the two value streams. These repre-sented parts of the System-CIP cycle. The second milestone involvedestablishing regular communication and visualizations. The latterincluded the visualization of standards, process statuses on the shopfloor, and major key performance indicators. The second milestoneserved as preparation for the third, which required one closed Point-CIP cycle from the participating warehouses. In addition to regu-lar communication and target conditions, this included three otherelements: a quick reaction system, problem solving methods, andprocess confirmation. Figure 5.2 shows the milestones covered bythe empowerment components.

Measuring

Three different measurement techniques were used to produce aholistic picture of warehouse performance. These included the BLWA,result KPR tracking, and monitoring KPI tracking if available.In order to reach the milestones, the lean maturity of the warehouses

50


Bo

sc

h L

og

isti

cs

Wo

rks

ho

ps (

BL

W)

•S

yste

m-C

IP, P

oin

t-C

IP a

nd

the

ro

le o

f th

e le

ad

er

•M

ix o

f th

eo

ry a

nd

pra

ctice in a

tra

inin

g w

are

ho

use

•P

art

icip

ants

: M

ix o

f B

osch a

nd

Lo

gis

tic S

erv

ice

Pro

vid

er

Em

plo

ye

es

Bo

sc

h L

og

isti

cs

Le

arn

ing

Gro

up

(B

LL

G)

•G

uid

ed

le

arn

ing

gro

up

s w

ith the

pu

rpo

se

of e

na

blin

g e

ach o

the

r

•R

ota

tio

na

l vis

its o

f d

iffe

rent w

are

ho

use

s

•P

art

icip

ants

: M

ix o

f B

osch a

nd

Lo

gis

tic S

erv

ice

Pro

vid

er

em

plo

ye

es

Bo

sc

h In

terd

isc

iplin

ary

Lo

ca

l Te

am

s (

BIL

T)

•B

PS

Exp

ert

s c

oa

ch a

t th

e s

ide

of th

e w

are

ho

use

ma

na

ge

r

•S

up

po

rt d

uri

ng

im

ple

me

nta

tio

n

•K

no

w-h

ow

of B

PS

and

le

ad

ing

asp

ects

Good Practice Transfer

of the BVL-AK-LML*

*Bund

esvere

inig

ung L

ogis

tik-

Arb

eitskre

is-L

ean M

anagem

ent

in L

ägern

Figu

re5.1:

Wareh

ouse

Excelle

nceem

powermentstructure

51


Bo

sch L

og

istic

s

Wo

rksh

op

(BL

W)

Bo

sch L

og

istic

s

Lea

rnin

g G

roup

(BL

LG

)

Bo

sch In

ter-

dis

cip

lina

ry L

oca

l

Te

am

s

(BIL

T)

Lea

n M

an

ag

em

ent

in W

are

ho

use

s

Wo

rkin

g G

roup

(BV

L-A

K-L

ML

)

20

10

2

01

2

20

11

Ja

n

Feb

M

ar

Ap

r M

ay

Ju

ne

Ju

ly

Se

pt

Oct

No

v

Se

pt

Oct

No

v

De

c

Au

g

Ma

r …

Mile

sto

ne

I

•V

SM

/VS

D

•P

roje

ct p

lan

Mile

sto

ne

II

•R

eg

ula

r Co

m.

•V

isu

aliz

atio

n

Mile

sto

ne

III

•P

oin

t CIP

Eva

lua

tion

Figure5.2:W

arehouseExcellence

projectmilestones

52


was recorded with the BLWA (see chapter 4) before warehouse ac-tivities began. The maturity in the 70 components (see figure 4.2and 4.3) were recorded by the assessors for each warehouse. Bymultiplying the number of components with the number of ware-houses, we find 1120 ordinal data at our disposal (compare Bortzand Weber, 2005, for a definition of ordinal data). Each record wasmade on the shop floor by two assessors. Feedback rounds were usedto ensure that the observations were aligned between the assessorsand the warehouse leaders. This ensured that subjective perceptionswere kept to a minimum. The assessors used a laboratory book asan organizational tool to record the most important environmentalpoints not covered in the structure sheet.Rational data (Bortz and Weber, 2005) were provided by the moni-toring KPI measurements. Result KPRs from January 2010 to De-cember 2011 were also analyzed. Delivery performance, quality per-formance, and productivity were also taken into consideration.Delivery performance at the result KPR level represents the overallprocess in most organizations. Delivery performance usually onlymeasures if the confirmed customer order was fulfilled correctly atthe end of the supply chain. To know the root cause, it is necessaryto know where the problem occurred within the supply chain. Forexample, one frequent reason for a delivery performance error is thatthe product was not assembled in time. A more detailed measure-ment, such as that between two internal processes, would indicatewhere it happened but this kind of measurement is rare. This indi-cates that customer claims frequently cannot be tracked back to thesupply chain participant that caused the problem.Another point regarding the measurement of delivery performanceis that very often no trend or fluctuation could be recognized inthe measured performance indicator within the project. The reasonfor this is that the workforce in the consumer goods warehouses ofthe Warehouse Excellence group is highly flexible. The workforceusually extends their working hours until the last order is fulfilled.A flat delivery performance was also common in the participating

53


automotive warehouses. No recorded incident suggested that a cus-tomer delivery performance claim was caused by the warehouse. Itwill be difficult to conclude any impact of the lean approach on thisperformance indicator at the result KPR level because of that overallmeasurement of the delivery performance indicator and the usuallyflat line of the delivery performance. To measure the impact withinthe Warehouse Excellence project, a more detailed measurement isrequired than what is available now.Like with delivery performance, the quality performance indicatorat the result KPR level also measures the overall process. The mea-surement shows if the right product in the right quality and in theright quantity reached the customer of the end of the supply chain,regardless of where the mistake was made. There is also a timegap between the error caused on the shop floor and the reportedclaim from the customer. This time gap often blurs the KPR. Inthe automotive sector, the annual claim rates are at most a single-digit figure. This statistic of rare events also frequently leads to aflat quality performance line. To measure the impact within theWarehouse Excellence project, a more detailed measurement of thequality performance indicator is required than what is available now.Productivity is measured by comparing an output factor with aninput factor. In the warehouse, a common measurement is to divideorder lines by man-hours. This means that the measured perfor-mance indicator can represent the focused area exactly. Gaps intime do not exist between the effect caused on the shop floor andthe effect discovered with the KPI. Fluctuating order volumes andadjustments in the workforce also lead to volatile productivity linesthat support effect analysis. Measurements between two differentinternal processes using monitoring KPI are also possible and partlyavailable in warehouses. The lack of time gaps, volatile productivitylines, and the availability of monitoring KPIs are all sufficient formeasuring this performance indicator for this thesis.Another reason why no trend can be recognized by observing thequality performance and delivery performance indicators are the

54

5.2 Control Group

boundary conditions. For a warehouse operation, quality and de-livery performance is more a constrain and productivity is a target.This means that the priority is clear and to the account of produc-tivity performance that let a trend be recognized.In summary, detailed measurements of the delivery performance andquality performance figures will not be taken into consideration. Ofcourse it is important to know that the delivery performance andquality performance have not been influenced negatively by the leanapproach so far. We know that no major incident has taken placewithin the project period. A detailed measurement like the pro-ductivity performance measurement is not possible at this momentbecause of the above mentioned reasons. Therefor the focus will beon productivity measurements. Productivity figures from January2010 to December 2011 are available for each warehouse. MonitoringKPI from areas that focused on implementing lean are also availableand will be analyzed. The data for analysis usually ranges from aperiod before implementation to a certain period after it. The min-imum length of time considered before the lean implementation forcontrol measurements is usually, at a minimum, the same length oftime considered for measurements after implementation.

5.2 Control Group

A control group was needed to test hypothesis IV in section 1.2.The control group consisted of 56 randomly selected Bosch ware-houses located across 16 countries. These included 38 distributionwarehouses and 18 plant warehouses. Thirty-seven warehouses arein the automotive business, 6 in industrial technology, and 13 inconsumer goods and building technology. Twenty-seven of the ware-houses are operated by Bosch and 29 by logistics service providers.This included three of the five largest logistics service providers inthe world as measured by the turnover. All other detailed structuralinformation is provided in appendix B.1. The control group was not

55


influenced by an empowerment program or by milestones that theyhad to achieve.Measurements were carried out that were similar to those under-taken for the Warehouse Excellence group. At the same time, lean-ness measurements in the control group were conducted using anonline survey. Before the online survey was released, it was testedon seven staff members from the Karlsruhe Institute of Technology(KIT), one lean expert, and two warehouse managers. The diversityof the test participants ensured feedback from different perspectives.The questionnaire was finalized after the feedback from the test wastaken into consideration.To ensure high response quality, the participating Bosch warehouseswere encouraged by Bosch’s logistic steering committee to completethe survey. The project team contacted each warehouse manager toexplain the purpose and structure of the survey. The warehouseswere given two weeks and the support of the project team to com-plete the survey. A plausibility check ensured that the survey an-swers were filled in precisely. For example, if a warehouse did nothave value stream mapping, they could not have a project plan thatincluded a value stream status and a value stream target. Suchissues were handled by the questionnaire. The final questionnaireconsisted of 19 components that covered the System-CIP and thePoint-CIP. A total of 1064 ordinal data were at our disposal.As a result of the discussion in subsection 5.1.1, the control groupalso focused on productivity KPI so that rational numbers couldbe acquired. From the 56 randomly chosen warehouses, only 18warehouses measured productivity at the result KPR level. This iswhy only 432 rational numbers were acquired.

5.3 Method for Testing the Hypotheses

The method for testing the hypotheses I-IV in section 1.2 is describedin this section. The verification structure is presented in figure 5.3.

56


Lean Maturity

Development

Productivity

Development

Review

Hypotheses

We

mig

ht kn

ow

now

if a

re

latio

nsh

ip e

xis

ts b

etw

een

le

an

ma

turity

deve

lopm

ent a

nd k

ey p

erf

orm

an

ce in

dic

ato

rs

Ch

ap

ter

Hy

po

the

sis

K

ey

Qu

es

tio

n

Wh

at

to D

o?

H

ow

to

Do

It?

6.2

Ba

se

fo

r

ab

scis

sa

of

H I, H

II,

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57


The first analysis will show if the Warehouse Excellence group im-plemented lean approaches and improved them. To do this, theWarehouse Excellence group will be evaluated using the BLWA. Theaggregate average scores in each BLWA component will be shown forthe measurement before and after the project. A deviation will benoticed when the two results are compared and this makes it possi-ble to identify the impact of the project. If an improvement in thelean maturity is detected, the precondition for the Hypotheses I-IIIis established because we now know that a movement on the ab-scissa (see 1.2) can be shown. In order to strengthen the statementthat the Warehouse Excellence group improved their lean maturitylevel and to be able to measure the impact of the project, hypothesistests will be conducted to show that the first data set is significantlydifferent from the second data set.To establish the precondition for Hypothesis IV, the delta and devi-ation of the Warehouse Excellence group will be compared with theBLWA results of the control group. The comparison will be donewith three different group compositions and in the following order:

• The entire control group• The 18 warehouses that measure productivity and are the mostmature in terms of lean development

• The warehouses that do not measure productivity

An additional hypothesis test will be carried out to strengthen thestatement about a higher lean maturity development in the Ware-house Excellence group. The results of the hypothesis test of theWarehouse Excellence group will be compared with the test resultsof the control group.The productivity development will be analyzed in the next step.First, the focus will be on productivity development from January2010 to December 2011 at the result KPR level of the WarehouseExcellence group. Next, the productivity development from Jan-

58


uary 2010 to December 2011 will be compared at the result levelfor both groups. Since the lean activities began in the WarehouseExcellence group at the end of 2010, we should see a positive ten-dency in the group in 2011. This would support Hypothesis I. If thetendency of the Warehouse Excellence group is better than that ofthe control group, it would support Hypothesis IV. Additional sup-port for Hypothesis I will involve the analysis of the productivitymonitoring KPI of the Warehouse Excellence group in areas wherelean techniques were implemented. Additional analysis of the leanassessment results could provide evidence about lean improvementsin these areas. A comparison of the level of lean improvement withthat of an increase in productivity could support Hypotheses II andIII. A correlation analysis to validate Hypotheses II and III will alsobe done at the KPR level for the Warehouse Excellence group, thecontrol group, and both groups together.A final discussion on the qualitative factors and reflection on allhypotheses will complete the verification structure and finalize theresearch.

59


60

6 Analyzing the Lean ImpactThe design of experiment and the testing structure described inchapter 5 will be used in this chapter to test the hypotheses. First,the statistical techniques that were used to analyze the data willbe described and the reason for their selection will be explained.Then, an analysis of lean improvement, productivity impact, andcorrelation will be conducted.

6.1 Statistical Background

The descriptive statistics will be supported by graphs, tables, andcharacteristic values to present the generated data. Average, stan-dard deviation, and linear regressions will be the characteristic val-ues (Bortz and Weber, 2005; Backhaus et al., 2003) used most oftenin this thesis.Unfortunately, descriptive statistics are limited by the generateddata. It may not be possible to offer a statement about the popu-lation or calculate significance levels. During elections, for example,most institutes ask for a small sample size, evaluate the data, test it,and use it to generate a statement about the population. Inferentialstatistics are needed for such tests. These inferential statistics aredivided into parametric and non-parametric tests. Parametric testsassume that the sample belongs to a population whose distributionis known. Statistical tests on samples evaluate their distribution,ascertaining, for example, if they are normally distributed. Thesetests are called goodness of fit tests. If a goodness of fit test can-not indicate a known distribution for the sample, the sample can be

61

6 Analyzing the Lean Impact

tested by a non-parametric test.

6.1.1 Choosing the Goodness of Fit Test

Lehmann and Romano (2005), Anderson and Darling (1954), Sá(2008), Cirrone et al. (2004), Duller (2008); and Janssen (2005) dis-cussed and analyzed several goodness of fit tests. The literaturereview identified the following tests as the most relevant ones:

• Kolmogorov-Smirnov test• Lilliefors test• Chi-Squared test• Anderson-Darling test• Cramer-von Mises test• Shapiro-Wilk test

Research by Janssen (2005) and Lehmann and Romano (2005) showedthat the Kolmogorov-Smirnov test is more effective than the Chi-Squared test in evaluating goodness of fit. The Anderson-Darling,Cramer-von Mises, and Lilliefors tests are based on the Kolmogorov-Smirnov test. Cirrone et al. (2004) and Lehmann and Romano(2005) said that the Anderson-Darling and Cramer-von Mises testsoutperform the Kolmogorov-Smirnov test because they are moresensitive indicators of the distribution. Sá (2008) compared theKolmogorov-Smirnov test, the Shapiro-Wilk test, and the Lillieforstest. He identified the Kolmogorov-Smirnov test as the weakest ofthe three and the Shapiro-Wilk test as the strongest across varioustested sample sizes. Razali and Wah (2011) compared the Shapiro-Wilk test, the Kolmogorov-Smirnov test, the Lilliefors test, and theAnderson-Darling test. They concluded that the Shapiro-Wilk testis the most powerful test for different distributions and sample sizes.Hence, this research uses the Shapiro-Wilk test as described in Sá

62

6.1 Statistical Background

(2008), Duller (2008), and Razali and Wah (2011).If the Shapiro-Wilk test shows that the sample structures are distri-bution free, non-parametric tests will be used to analyze the samples.

6.1.2 Choosing the Non-Parametric Test

Janssen (2005) describes four distinguishing characteristics that mustbe considered before choosing a non-parametric test. These are sam-ple size, scale level, sample quantity, and whether the samples aredependent from each other or not. Duller (2008), Janssen (2005), Sá(2008), Toutenburg et al. (2009), Genschel and Becker (2005), andGibbons (2003) describe several non-parametric tests.The most common tests for two independent samples with minimumordinal scales are as follows:

• Mann-Whitney U test• Median test• Moses test• Kolmogorov-Smirnov Z test• Wald-Wolfowitz test

Toutenburg et al. (2009) and Duller (2008) describe the Mann-Whitney U, Median, and Moses tests that test specific parametersand show where the difference occurs within samples. The Mann-Whitney U and Median tests are sensitive to the location of the dis-tribution among samples. Janssen (2005) describes the Median testas being a very general test and, hence, rather poor in its effective-ness. The Mann-Whitney test is considered to be very effective forlarge samples. The Moses test is sensitive to the shape of the distri-butions and is similar to the Wilcoxon Signed-Rank test, which willbe described later. It is especially suitable when extreme reactionsare expected (Duller, 2008). The advantage of the Kolmogorov-

63


Smirnov Z test and the Wald-Wolfowitz test is their sensitivity tothe location and shape of the samples distribution. The so-calledOmnibus tests that are sensitive to both criteria cannot show wherethe difference occurs within the samples but indicate when signif-icant differences exist (Genschel and Becker, 2005; Gibbons, 2003;Janssen, 2005; Sá, 2008). Duller (2008) describes the Kolmogorov-Smirnov Z test as being more effective than the Wald-Wolfowitz test.In conclusion, we will use the Kolmogorov-Smirnow Z test for testswith two independent samples with minimum ordinal scales data forthis thesis.The most common tests for two dependent samples with minimumordinal scales are (Janssen, 2005; Sá, 2008; Duller, 2008; Genscheland Becker, 2005) as follows:

• Wilcoxon Signed-Rank test• Sign test

The Wilcoxon Signed-Rank test considers the magnitude of the dif-ference between sample parameters. The Sign test does not, whichmakes the Wilcoxon Signed-Rank test more effective (Sá, 2008).This more effective aspect is also the reason why this test will bechosen for this thesis for tests with two dependent samples withminimum ordinal scales data.All of the described statistical tests are conducted using the IBMSPSS Statistics Version 20 software. SPSS calculates significancelevel for each test. Clauß et al. (1994) and Bol (2003) offer thefollowing interpretation for the calculated significance levels:

• Significance level ≤ 0.001 = high significance level• Significance level > 0.001 and ≤ 0.010 = very significant level• Significance level > 0.010 and ≤ 0.050 = significance level• Significance level > 0.050 and ≤ 0.100 = low significance level

64

6.2 Analysis of Lean Maturity Development

• Not significant if above > 0.100


The results of the BLWA are presented in this section. The devel-opment of the lean maturity of the Warehouse Excellence group andthe control group are shown by comparing the results of the firstand second assessment.

6.2.1 Lean Maturity Development of the WarehouseExcellence Group

Descriptive Analysis

Chapter 4 described the development of a maturity assessment formeasuring the leanness of a warehouse and section 5.1 described theproject, Warehouse Excellence, while focusing on the 16 warehousesin the observation group. The Bosch Logistics Warehouse Assess-ment evaluated the 16 warehouses from the end of 2010 to the endof 2011 / beginning of the year 2012.The accumulated results are highlighted in the figures 6.1, 6.2, 6.3,6.4 and the tables 6.1, 6.2.Table 6.3 provides figures for the assessment results over two dif-ferent years. There is a high level of improvement in the overallaverage points as well as in the points focusing on the main leancomponents System-CIP and Point-CIP, which were emphasized inthe Warehouse Excellence project. Additionally, the coefficient ofvariation shows that the spread of the function narrows.Table 6.3 shows that the total average accumulated score improvesby about 60 points, an increase of 84%. D.1 shows that each ware-house improves its entire system. Moreover, the variation coefficient

65


Average of the B

osch Logistics Warehouse A

ssessment 2010

Average of the B


ssessment 2011

CONCEPT

Analysis of the W

arehouse Excellence Group D

evelopment

EXECUTION

Target Condition

Quick Reaction System

Regular Comm

unication

Sustainable Problem Solving

Process Confirmation 0,0

1,0 2,0

3,0 4,0

1.2 Point-CIP

Business Requirements

Value Stream Planning

Identification of Improvem

ent …

Definition of Target Conditions

System-CIP Projects

Point-CIP Areas 0,0 1,0

2,0 3,0

4,0

1.1 System-CIP

Target Derivation

System-CIP Cycles

Improvem

ent Focus

Leadership Involvement

VSM-Q

uality

Target Achievement 0,0

1,0 2,0

3,0 4,0

KPI-effect

Quality of Problem

Solving 0,0 1,0

2,0 3,0

4,0

Figure6.1:B

LWA

results:Warehouse

Excellencegroup

2010vs.

Warehouse

Excellencegroup

2011(part

1)

66


Ave

rage

of t

he B

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ent 2

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CONCEPT EXECUTION

Ana

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t

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k

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ility

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0 2,

0 3,

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2,0

3,0

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Figu

re6.2:

BLW

Aresults

:Wareh

ouse

Excelle

ncegrou

p2010

vs.Wareh

ouse

Excelle

ncegrou

p2011

(part2)

67


Average of the B


ssessment 2010

Average of the B


ssessment 2011

CONCEPTEXECUTION

Analysis of the W

arehouse Excellence Group D

evelopment

Organization

Technical Equipment

Visualization 0,0 1,0

2,0 3,0

4,0

3.2 Outgoing G

oods

Time W

indow Adherence

Balancing of Complete Shipping

Processes

Lead Time of Shipping Process

Dispatch Error Rate

Handling Steps 0,0 1,0

2,0 3,0

4,0

Packaged Good

Packaging Material

Packaging Process

Visualization 0,0 1,0

2,0 3,0

4,0

3.3 Packaging

Lead Time of Packaging Process

Packaging error rate 0,0 1,0

2,0 3,0

4,0

Qualification 0,0

1,0 2,0

3,0 4,0

3.1 Overhead

Figure6.3:B

LWA

results:Warehouse

Excellencegroup

2010vs.

Warehouse

Excellencegroup

2011(part

3)

68


Ave

rage

of t

he B

osch

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areh

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ent 2

010

Ave

rage

of t

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ouse

Ass

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011

CONCEPT EXECUTION

Ana

lysi

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War

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xcel

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roup

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t

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cess

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age

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nic/

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anag

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3.5

Stor

age

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Inco

min

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oods

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1,0

2,0

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Figu

re6.4:

BLW

Aresults

:Wareh

ouse

Excelle

ncegrou

p2010

vs.Wareh

ouse

Excelle

ncegrou

p2011

(part4)

69


Category WaEx 2010 WaEx 20111.1 System-CIP ConceptBusiness Requirements 0.750 2.000Value Stream Planning 0.375 1.875Identification of Improvement Activities 0.125 1.375Definition of Target Conditions 0.125 1.438System-CIP Projects 0.188 1.375Point-CIP Areas 1.125 2.1251.1 System-CIP ExecutionTarget Derivation 0.688 1.375System-CIP Cycles 0.250 1.000Improvement Focus 0.938 2.563Leadership Involvement 0.313 2.188VSM-Quality 0.750 1.938Target Achievement 0.125 1.6881.2 Point-CIP ConceptTarget Condition 0.438 1.438Quick Reaction System 0.438 3.250Regular Communication 1.375 2.813Sustainable Problem Solving 0.438 1.313Process Confirmation 0.313 1.0631.2 Point-CIP ExecutionKPI-Effect 0.188 1.563Quality of Problem Solving 0.250 0.4382.1 Failure Prevention System ConceptWork Content 1.250 1.563Visualization 0.938 1.7502.1 Failure Prevention System ExecutionError Rate 1.938 3.3132.2 Employee Involvement ConceptInvolvement 0.375 0.625Target Deployment Team Lead 1.000 1.938Qualification / Training 1.375 2.0632.2 Employee Involvement ExecutionMulti-Skilled Operators 2.375 3.000Operator Involvement 0.438 1.125Leadership Involvement 0.188 0.6252.3 Standardized Work ConceptCoverage of Standardized Work 0.625 1.313Visualization 0.438 1.500Qualification 0.625 1.4382.3 Standardized Work ExecutionStability 0.188 0.1255S Status 1.500 2.250Productivity 1.438 2.500

Table 6.1: BLWA results: Warehouse Excellence group 2010 vs.Warehouse Excellence group 2011 average scores (part 1)

70


Category WaEx 2010 WaEx 20113.1 Overhead ConceptQualification 0.600 1.6883.2 Outgoing Goods ConceptOrganization 0.750 1.063Technical Equipment 2.500 2.500Visualization 0.438 0.8133.2 Outgoing Goods ExecutionTime Window Adherence 0.375 0.688Balancing of Complete Shipping Processes 0.063 0.188Lead Time of Shipping Process 0.063 0.313Dispatch Error Rate 2.031 2.656Handling Steps 0.125 0.0633.3 Packaging ConceptPackaged Good 1.719 1.885Packaging Material 1.625 2.073Packaging Process 0.938 1.208Visualization 1.063 1.5523.3 Packaging ExecutionLead Time of Packaging Process 0.313 0.438Packaging Error Rate 1.688 2.7503.4 Picking ConceptPicking Process 0.969 1.344Organizational System 1.188 1.250Information System 1.594 1.781Visualization 0.875 1.5313.4 Picking ExecutionLead Time of Picking Process 0.063 0.531Picking Error Rate 1.531 2.3443.5 Storage ConceptStorage Technic/Layout 1.906 1.906Storage Criteria 2.031 2.406Inventory Management 2.063 2.438Visualization 1.188 1.1883.5 Storage ExecutionLead Time of Storage Process 0.125 0.313Storage Error Rate 0.313 1.5633.6 Incoming Goods ConceptReceiving Process 0.563 1.281Entry/Booking 1.688 2.188Inspection 0.867 0.867Visualization 0.375 0.8133.6 Incoming Goods ExecutionTime Window Adherence of Receiving 0.625 0.875Balancing of Receiving Processes 0.063 0.625Lead Time of Receiving Process 0.375 0.781Receiving Error Rate 0.813 1.875Handling Steps 0.250 0.063

Table 6.2: BLWA results: Warehouse Excellence group 2010 vs.Warehouse Excellence group 2011 average scores (part 2)

71


WaEx 2010 WaEx 2011 differenceAverage Points in all assessment categories 57.62 105.77 +83.56 %Coefficient of variance by all assessment categories 47.22 % 30.31 %Average Points in System- & Point-CIP categories 9.18 32.81 +257.14 %Coefficient of variance by Points in System- & Point-CIP categories 88.26 % 28.35 %

Table 6.3: BLWA results: total points

becomes smaller, which indicates a more aligned group. This meansthat the improvement of the average score is not just influenced bysingle warehouses that improved greatly while all other warehousesdid not improve. It is more a sign that the entire group reached aspecific positive development.Focusing solely on the results of the System-CIP and Point-CIP, theaverage accumulated score rises by 257%. This increase is largerthan the improvement to the total score. This also represents thefocus and goals of the project. Appendix D.1 shows that before theWarehouse Excellence project only a few warehouses had establisheda continuous improvement cycle from process design to target con-dition implementation and up to process stabilization. By reachingall of the milestones, all warehouses now have a continuous improve-ment cycle with different maturity levels. These are also representedin the scores shown in Appendix D.1.The System-CIP section consists of six components. The three com-ponents – identification of improvements, definition of target condi-tions, and System-CIP projects – are necessary for implementingspecific actions. These three components show a gap in 2010 as wellas in 2011 compared to other System-CIP components. The averagescore of the three components is 0.15 in 2010. For the others, this is0.75 in 2010. In the year 2011, the three components had an aver-age of 1.4, the others had 2.0. This gap narrows (viewed relatively)slightly but is still visible.This also reflects the project team’s experience during the Ware-house Excellence project. The actions that were defined by the

72


warehouses were inaccurate. For example, the project plans didnot include any capacity planning for the persons involved. Anotherexample is that when a target condition was defined, the KPI neededto monitor that standard was not defined. Also, a systematic wayof identifying the most important areas for improvement did not ex-ist. Furthermore, the link between the goals and the projects wasnot shown with figures. Without these, a comprehensive strategyfor creating and reaching medium-term/long-term goals can neverbe established. This was also one of the major findings during theproject.In the execution part of the System-CIP, the component leadershipinvolvement had an average score of 0.313 in 2010 – one of the lowest.After focusing on the role of the leader in the CIP process, the scorein 2011 was 2.188 points – one of the strongest in the category. Thisindicates that the taken measures were effective.The Point-CIP results improved from an accumulated average scoreof 0.491 to 1.696 in 2011. This delta also shows that the takenmeasures were effective. In 2010, regular communication existed inpractice but most warehouses lacked a well-defined scope for dis-cussing KPIs and their deviations. Problem solving (0.438 pointsin 2010 and 1.313 points in 2011) and process confirmation (0.313points in 2010 and in 1.063 points in 2011) were the two subjectswith the lowest scores but still showed a clear improvement. Theproject team could confirm the assessment results with their expe-riences on the shop floors. None of the warehouses had ever hadproblem solving training with the shop floor team and root causeanalysis was part of the training. Process confirmation was alsolacking. The leaders often implement standards but did not followup on them to ensure adherence.

Inferential Analysis - Goodness of Fit Test

This section analyzes if the changes in the assessment results of theWarehouse Excellence group were significant or not. If we know if

73


the results are significant, we can estimate with a higher level ofevidence if our results are random or based on the influence of thelean approach. In later sections, we will examine the following:

• The total assessment result for each warehouse of the Ware-house Excellence group

• The System-CIP and Point-CIP assessment results for eachwarehouse in the Warehouse Excellence group

• The System-CIP and Point-CIP assessment results for eachwarehouse in the control group

Before using a hypothesis test to describe the significant level of theresults, it is necessary to test how the evaluated data is distributed(see section 6.1.1). If we know that the data is normally distributedor not, we can decide if parametric or non-parametric hypothesistests can be used. To acquire a high level of evidence, the goodnessof fit test will be done on each data set that will be tested later.For the Shapiro-Wilk Test, we will assume the following hypotheses:

H0: The data set is normally distributed -> “We can useparametric two sample tests with a very high test power”

H1: The data set is not normally distributed -> “We have to usenon-parametric two sample tests with a good test power”

Table 6.4 shows the results of the Shapiro-Wilk test for the totalassessment result for each warehouse in the Warehouse Excellencegroup. Each line represents one warehouse. The abbreviation WaExin the row stands for Warehouse Excellence group. The numbers 10and 11 represent the data periods that are evaluated together. Thefirst set of data is from the end of 2010 (10) and the second set is

74


Shapiro-Wilk

Stat- istic df

Signi- ficance

WaEx 10 & 11 W1 .699 138 .000 WaEx 10 & 11 W2 .854 138 .000 WaEx 10 & 11 W3 .789 138 .000 WaEx 10 & 11 W4 .807 138 .000 WaEx 10 & 11 W5 .758 138 .000 WaEx 10 & 11 W6 .891 138 .000 WaEx 10 & 11 W7 .769 138 .000 WaEx 10 & 11 W8 .785 138 .000 WaEx 10 & 11 W9 .855 138 .000 WaEx 10 & 11 W10 .804 138 .000 WaEx 10 & 11 W11 .879 138 .000 WaEx 10 & 11 W12 .728 138 .000 WaEx 10 & 11 W13 .773 138 .000 WaEx 10 & 11 W14 .782 138 .000 WaEx 10 & 11 W15 .854 138 .000 WaEx 10 & 11 W16 .771 138 .000

Table 6.4: Shapiro-Wilk test for the total assessment results of theWarehouse Excellence group

75


taken at the end of 2011 (11). The calculated significance level isbelow 0.001%. This means that we can reject H0 with a high levelof significance and assume that H1 is valid. This means that nowarehouse in the Warehouse Excellence group has assessment resultsthat are normally distributed and this was expected. In conclusion,non-parametric tests should be used for the evaluation of the totalassessment results of the Warehouse Excellence group.The Hypotheses H0 and H1 can also be applied for the Shapiro-Wilktest in which the System-CIP and Point-CIP assessment results foreach warehouse of the Warehouse Excellence group are analyzed.Table 6.5 shows the result of testing the System-CIP and Point-CIPassessment results of the warehouses in the Warehouse Excellencegroup. The terminology in this table is the same that is used in theShapiro-Wilk test table except that the letters S and P are added,which stand for System-CIP and Point-CIP. All of the warehousestested below the significance level of 0.001% in this test as well. Thismeans that H0 can be rejected with a high level of significance andit can be assumed that H1 is valid. This means we can assume thatthe System-CIP and Point-CIP assessment results of the warehousesin the Warehouse Excellence group are not normally distributed.The System-CIP and Point-CIP assessment results for each ware-house in the control group were then tested to identify the distribu-tion of the data. The hypotheses H0 and H1 were also applied forthis test.Table 6.6 shows the results of the testing of the System-CIP andPoint-CIP assessment of the control group warehouses. Each linerepresents the results of one warehouse. CoGr indicates that thewarehouse is from the control group. The range C1 to C56 representsthe 56 warehouses in the control group. The 10 and 11 representsthe time frame when the data was collected. The warehouses C9,C11, and C13 to C26 did not reach any maturity level and did notmake any progress during the course of the project. They are notidentified separately because no distribution exists. Interpreting thedata leads to the fact that H0 can be rejected with a similar high

76


Shapiro-Wilk

Stati-

stic df

Signi-

ficance

WaEx 10 & 11 S+P W1 ,680 38 ,000

WaEx 10 & 11 S+P W2 ,869 38 ,000

WaEx 10 & 11 S+P W3 ,767 38 ,000

WaEx 10 & 11 S+P W4 ,794 38 ,000

WaEx 10 & 11 S+P W5 ,771 38 ,000

WaEx 10 & 11 S+P W6 ,853 38 ,000

WaEx 10 & 11 S+P W7 ,822 38 ,000

WaEx 10 & 11 S+P W8 ,723 38 ,000

WaEx 10 & 11 S+P W9 ,825 38 ,000

WaEx 10 & 11 S+P W10 ,785 38 ,000

WaEx 10 & 11 S+P W11 ,856 38 ,000

WaEx 10 & 11 S+P W12 ,729 38 ,000

WaEx 10 & 11 S+P W13 ,813 38 ,000

WaEx 10 & 11 S+P W14 ,684 38 ,000

WaEx 10 & 11 S+P W15 ,825 38 ,000

WaEx 10 & 11 S+P W16 ,770 38 ,000

Table 6.5: Shapiro-Wilk test for the System-CIP and Point-CIP as-sessment results of the warehouses in the Warehouse Ex-cellence group

77


Shapiro-Wilk

Shapiro-Wilk

Stati-

stic df

Signi-

ficance

Stati-

stic df

Signi-

ficance

CoGr 10 & 11 C1 ,810 38 ,000 CoGr 10 & 11 C37 ,737 38 ,000

CoGr 10 & 11 C2 ,436 38 ,000 CoGr 10 & 11 C38 ,803 38 ,000

CoGr 10 & 11 C3 ,622 38 ,000 CoGr 10 & 11 C39 ,763 38 ,000

CoGr 10 & 11 C4 ,861 38 ,000 CoGr 10 & 11 C40 ,701 38 ,000

CoGr 10 & 11 C5 ,819 38 ,000 CoGr 10 & 11 C41 ,750 38 ,000

CoGr 10 & 11 C6 ,742 38 ,000 CoGr 10 & 11 C42 ,731 38 ,000

CoGr 10 & 11 C7 ,667 38 ,000 CoGr 10 & 11 C43 ,752 38 ,000

CoGr 10 & 11 C8 ,522 38 ,000 CoGr 10 & 11 C44 ,751 38 ,000

CoGr 10 & 11 C10 ,516 38 ,000 CoGr 10 & 11 C45 ,701 38 ,000

CoGr 10 & 11 C12 ,468 38 ,000 CoGr 10 & 11 C46 ,509 38 ,000

CoGr 10 & 11 C27 ,680 38 ,000 CoGr 10 & 11 C47 ,663 38 ,000

CoGr 10 & 11 C28 ,574 38 ,000 CoGr 10 & 11 C48 ,787 38 ,000

CoGr 10 & 11 C29 ,515 38 ,000 CoGr 10 & 11 C49 ,720 38 ,000

CoGr 10 & 11 C30 ,684 38 ,000 CoGr 10 & 11 C50 ,152 38 ,000

CoGr 10 & 11 C31 ,404 38 ,000 CoGr 10 & 11 C51 ,836 38 ,000

CoGr 10 & 11 C32 ,500 38 ,000 CoGr 10 & 11 C52 ,152 38 ,000

CoGr 10 & 11 C33 ,780 38 ,000 CoGr 10 & 11 C53 ,498 38 ,000

CoGr 10 & 11 C34 ,665 38 ,000 CoGr 10 & 11 C54 ,325 38 ,000

CoGr 10 & 11 C35 ,471 38 ,000 CoGr 10 & 11 C55 ,826 38 ,000

CoGr 10 & 11 C36 ,748 38 ,000 CoGr 10 & 11 C56 ,599 38 ,000

Table 6.6: Shapirot-Wilk test for the assessment results of the con-trol group

78


significance and the assumption can be made that the lean maturityresults are not normally distributed.The result of the Shapiro-Wilk test shows that the data sets ofWaEx, WaEx S+P, and CoGr respectively are not normally dis-tributed. This needed to be known before choosing the right test(parametric or non-parametric two sample test) to analyze if thetaken measures of the Warehouse Excellence project did have animpact on the maturity level of the participating warehouses.

Inferential Analysis - Wilcoxon Signed-Rank Test for theWarehouse Excellence Group

We know now with a high significance level that the distributionof the maturity development of the warehouses is not normally dis-tributed. This means non-parametric hypothesis tests will help usto determine if the development of the assessment results of eachwarehouse from 2010 to 2011 is random or not. To determine whichnon-parametric test has to be chosen, we also need to know if thedata set from 2010 and the data set from 2011 is dependent on orindependent from each other.Brosius (2011, p. 888) mentions that samples are dependent if thereis a before and after comparison which is this case here. For thisreason, the non-parametric two-sample dependency test that waschosen in chapter 6.1.2 will be used. The following hypotheses havebeen defined for the Wilcoxon Signed-Rank test:

H0: The samples n1 and n2 are from the same population -> “Thewarehouse did not improve their lean maturity”

H1: The samples n1 and n2 are not from the same population ->“The warehouse did improve their lean maturity”

79


The sample n1 is the data set from the assessment results for theyear 2010. The sample n2 is from the assessment results for the year2011.Table 6.7 shows the ranking table of the Wilcoxon Signed-Rank testtotal assessment results for each warehouse in the Warehouse Excel-lence group. In this analysis, the results obtained from the begin-ning of the project were compared with the results obtained at theend of the project. A negative ranking means that the warehousehad a negative development in an assessment component. A posi-tive ranking means that development in a component was positive.Similarly, a tie means there has been no change in the specific com-ponent. Seventy components were considered in total. However, thewarehouses had an overall negative ranking in 1.4% of the cases. In57.3% of the cases, there were ties and there was a positive rankingin 41.3% of the cases. All warehouses had more positive rankingsthan negative ones. This means that the taken measures, duringthe Warehouse Excellence project, could be seen as a positive leanmaturity development.Table 6.8 shows the test statistics for the Wilcoxon Signed-Rank test.Each box represents the before and after comparison in a particularwarehouse. 15 of the 16 warehouses shows with high significanceand one warehouse with significance that H0 can be rejected. Thismeans that it can be assumed that there is a significant differencebetween the samples. In other words, the results from the year2010 are so different from the year 2011 that it cannot be random.This indicates an overall high significance that the taken measuresfrom the Warehouse Excellence project influenced the warehousesand could be seen by the assessments results.Analogous to the Wilcoxon Signed-Rank test, the analysis was con-ducted with the data set from the System-CIP and Point-CIP as-sessment results of the Warehouse Excellence group.Table 6.9 shows the ranking table of the Wilcoxon Signed-Rank testfor the assessment results of the System-CIP and Point-CIP resultsof the Warehouse Excellence group. The warehouses show negative

80


N Mean Rank

Sum of Ranks N Mean

RankSum of Ranks

Negative Ranks

0a 0.00 0.00 Negative Ranks

4y 21.63 86.50

Positive Ranks

33b 17.00 561.00 Positive Ranks

36z 20.38 733.50

Ties 37c Ties 30aa

Total 70 Total 70Negative Ranks

1d 35.50 35.50 Negative Ranks

3ab 12.33 37.00

Positive Ranks

36e 18.54 667.50 Positive Ranks

24ac 14.21 341.00

Ties 33f Ties 43ad


2g 14.75 29.50 Negative Ranks

3ae 11.83 35.50

Positive Ranks

19h 10.61 201.50 Positive Ranks

20af 12.03 240.50

Ties 49i Ties 46ag


1j 11.00 11.00 Negative Ranks

1ah 6.50 6.50

Positive Ranks

41k 21.76 892.00 Positive Ranks

19ai 10.71 203.50

Ties 28l Ties 50aj


0m 0.00 0.00 Negative Ranks

0ak 0.00 0.00

Positive Ranks

34n 17.50 595.00 Positive Ranks

31al 16.00 496.00

Ties 36o Ties 39am


1p 21.50 21.50 Negative Ranks

0an 0.00 0.00

Positive Ranks

32q 16.86 539.50 Positive Ranks

19ao 10.00 190.00

Ties 37r Ties 51ap


0s 0.00 0.00 Negative Ranks

0aq 0.00 0.00

Positive Ranks

27t 14.00 378.00 Positive Ranks

28ar 14.50 406.00

Ties 43u Ties 42as


0v 0.00 0.00 Negative Ranks

0at 0.00 0.00

Positive Ranks

24w 12.50 300.00 Positive Ranks

38au 19.50 741.00

Ties 46x Ties 32av

Total 70 Total 70

WaEx W5 2011 - WaEx W5 2010

WaEx W6 2011 - WaEx W6 2010

WaEx W7 2011 - WaEx W7 2010

WaEx W8 2011 - WaEx W8 2010

WaEx W1 2011 - WaEx W1 2010

WaEx W2 2011 - WaEx W2 2010

WaEx W3 2011 - WaEx W3 2010

WaEx W4 2011 - WaEx W4 2010

a. WaEx W1 2011 < WaEx W1 2010b. WaEx W1 2011 > WaEx W1 2010c. WaEx W1 2011 = WaEx W1 2010

WaEx W13 2011 - WaEx W13 2010

WaEx W14 2011 - WaEx W14 2010

at. WaEx W16 2011 < WaEx W16 2010au. WaEx W16 2011 > WaEx W16 2010av. WaEx W16 2011 = WaEx W16 2010

WaEx W15 2011 - WaEx W15 2010

WaEx W16 2011 - WaEx W16 2010

WaEx W9 2011 - WaEx W9 2010

WaEx W10 2011 - WaEx W10 2010

WaEx W11 2011 - WaEx W11 2010

WaEx W12 2011 - WaEx W12 2010

between "d" and "as" similar

Table 6.7: Wilcoxon Rank table for the assessment results of thewarehouses in the Warehouse Excellence group

81


WaEx W

1 2011 - W

aEx W1 2010

WaEx W

2 2011 - W

aEx W2 2010

WaEx W

3 2011 - W

aEx W3 2010

WaEx W

4 2011 - W

aEx W4 2010

Z-5,232

b-4,899

b-3,236

b-5,610

b

Asymp. Sig. (2-tailed)

.000.000

.001.000

WaEx W

5 2011 - W

aEx W5 2010

WaEx W

6 2011 - W

aEx W6 2010

WaEx W

7 2011 - W

aEx W7 2010

WaEx W

8 2011 - W

aEx W8 2010

Z-5,157

b-4,710

b-4,724

b-4,485

b


.000.000

.000.000

WaEx W

9 2011 - W

aEx W9 2010

WaEx W

10 2011 - W

aEx W10 2010

WaEx W

11 2011 - W

aEx W11 2010

WaEx W

12 2011 - W

aEx W12 2010

Z-4,396

b-3,754

b-3,171

b-3,780

b


.000.000

.002.000

WaEx W

13 2011 - W

aEx W13 2010

WaEx W

14 2011 - W

aEx W14 2010

WaEx W

15 2011 - W

aEx W15 2010

WaEx W

16 2011 - W

aEx W16 2010

Z-4,944

b-4,014

b-4,671

b-5,443

b


.000.000

.000.000

Test Statistics

a

a. Wilcoxon Test

b. Based on negative ranks.

Table6.8:W

ilcoxonteststatisticsforthe

assessmentresultsofthe

warehousesin

theWarehouse

Excellencegroup

82


NMean Rank

Sum of Ranks N

Mean Rank

Sum of Ranks

Negative Ranks 0a 0.00 0.00 Negative Ranks 0y 0.00 0.00

Positive Ranks 16b 8.50 136.00 Positive Ranks 15z 8.00 120.00Ties 3c Ties 4aa

Total 19 Total 19Negative Ranks 0d 0.00 0.00 Negative Ranks 0ab 0.00 0.00

Positive Ranks 15e 8.00 120.00 Positive Ranks 14ac 7.50 105.00Ties 4f Ties 5ad

Total 19 Total 19Negative Ranks 0g 0.00 0.00 Negative Ranks 3ae 6.33 19.00

Positive Ranks 9h 5.00 45.00 Positive Ranks 7af 5.14 36.00Ties 10i Ties 9ag

Total 19 Total 19Negative Ranks 0j 0.00 0.00 Negative Ranks 0ah 0.00 0.00

Positive Ranks 15k 8.00 120.00 Positive Ranks 14ai 7.50 105.00Ties 4l Ties 5aj

Total 19 Total 19Negative Ranks 0m 0.00 0.00 Negative Ranks 0ak 0.00 0.00

Positive Ranks 17n 9.00 153.00 Positive Ranks 15al 8.00 120.00Ties 2o Ties 4am

Total 19 Total 19Negative Ranks 0p 0.00 0.00 Negative Ranks 0an 0.00 0.00

Positive Ranks 12q 6.50 78.00 Positive Ranks 7ao 4.00 28.00Ties 7r Ties 12ap

Total 19 Total 19Negative Ranks 0s 0.00 0.00 Negative Ranks 0aq 0.00 0.00

Positive Ranks 12t 6.50 78.00 Positive Ranks 13ar 7.00 91.00Ties 7u Ties 6as

Total 19 Total 19Negative Ranks 0v 0.00 0.00 Negative Ranks 0at 0.00 0.00

Positive Ranks 14w 7.50 105.00 Positive Ranks 17au 9.00 153.00Ties 5x Ties 2av

Total 19 Total 19

c. WaEx W1 2011 = WaEx W1 2010 av. WaEx W16 2011 = WaEx W16 2010

WaEx W15 2011 S+P - WaEx W15 2010 S+P

WaEx W16 2011 S+P - WaEx W16 2010 S+P

au. WaEx W16 2011 > WaEx W16 2010at. WaEx W16 2011 < WaEx W16 2010a. WaEx W1 2011 < WaEx W1 2010

b. WaEx W1 2011 > WaEx W1 2010

WaEx W5 2011 S+P - WaEx W5 2010 S+P









WaEx W10 2011 S+P - WaEx W10 2010 S+P

WaEx W11 2011 S+P - WaEx W11 2010 S+P

WaEx W12 2011 S+P - WaEx W12 2010 S+P

WaEx W13 2011 S+P - WaEx W13 2010 S+P

WaEx W14 2011 S+P - WaEx W14 2010 S+P

between "d" and "as" similar

Table 6.9: Wilcoxon Rank table for the assessment results for theSystem-CIP and Point-CIP of the warehouses in theWarehouse Excellence group

83


rankings in 1% of the cases, positive rankings in 69.7 % of the cases,and a tie in 29.3% of the cases. Similar to the earlier results, a pos-itive trend is shown for the taken measures that were implementedduring the Warehouse Excellence project. The higher positive trendcompared to the results of the test that was done before with thedata set of the total assessment underlines the focus of the project:the implementation of a systematic and analytical continuous im-provement cycle with the System-CIP and Point-CIP approach.Table 6.10 shows the test statistics of the Wilcoxon-Signed-Ranktest for the assessment results of the System-CIP and Point-CIPresults of the Warehouse Excellence group. In 11 warehouses, thereis with a high significance level that H0 can be rejected. In fourwarehouses, the results were very significant and in one warehousethe significance level of 0.374 is too low to reject H0. The reasonfor the less strong significance level, compared to the first Wilcoxon-Signed-Rank test statistics, is that the sample size is smaller. Singlenegative cases have a stronger effect on the results because the lowertotal amount of ties does not absorb the single negative impact. Inturn, the other results are very strong despite the small sample size.

Inferential Analysis - Wilcoxon Signed-Rank test for the ControlGroup

In order to evaluate the results of the Wilcoxon Signed-Rank testfor the Warehouse Excellence group, we also analyzed each ware-house in the control group. Tables 6.11 and 6.12 give the rankingtables of the Wilcoxon-Signed-Rank test for the assessment resultsof the System-CIP and Point-CIP components of the control group.The warehouses had negative rankings in 9.3% of the cases, positiverankings in 18.9% of the cases, and ties in 71.8% of the cases. Thisrepresents a moderately positive trend.Seventeen warehouses have ties in all components. This indicatesthat the issue was not totally addressed in the warehouses. Exclud-ing the warehouses with the 19 ties, the results are also moderately

84


Test

Sta

tistic

sa

WaE

x W

1 20

11 S

+P -

WaE

x W

1 20

10 S

+PW

aEx

W2

2011

S+P

- W

aEx

W2

2010

S+P

WaE

x W

3 20

11 S

+P -

WaE

x W

3 20

10 S

+PW

aEx

W4

2011

S+P

- W

aEx

W4

2010

S+P

Z-3

,656

b-3

,535

b-2

,810

b-3

,446

b

Asym

p. S

ig. (

2-ta

iled)

.000

.000

.005

.001

WaE

x W

5 20

11 S

+P -

WaE

x W

5 20

10 S

+PW

aEx

W6

2011

S+P

- W

aEx

W6

2010

S+P

WaE

x W

7 20

11 S

+P -

WaE

x W

7 20

10 S

+PW

aEx

W8

2011

S+P

- W

aEx

W8

2010

S+P

Z-3

,660

b-3

,115

b-3

,126

b-3

,407

b

Asym

p. S

ig. (

2-ta

iled)

.000

.002

.002

.001

WaE

x W

9 20

11 S

+P -

WaE

x W

9 20

10 S

+PW

aEx

W10

201

1 S

+P -

WaE

x W

10 2

010

S+P

WaE

x W

11 2

011

S+P

- W

aEx

W11

201

0 S

+PW

aEx

W12

201

1 S

+P -

WaE

x W

12 2

010

S+P

Z-3

,449

b-3

,360

b-,8

90b

-3,3

70b

Asym

p. S

ig. (

2-ta

iled)

.001

.001

.374

.001

WaE

x W

13 2

011

S+P

- W

aEx

W13

201

0 S

+PW

aEx

W14

201

1 S

+P -

WaE

x W

14 2

010

S+P

WaE

x W

15 2

011

S+P

- W

aEx

W15

201

0 S

+PW

aEx

W16

201

1 S

+P -

WaE

x W

16 2

010

S+P

Z-3

,501

b-2

,456

b-3

,235

b-3

,663

b

Asym

p. S

ig. (

2-ta

iled)

.000

.014

.001

.000

a. W

ilcox

on-T

est

b. B

ased

on

nega

tive

rank

s.

Table6.10:W

ilcoxon

test

statist

icsfor

theassessmentr

esults

fort

heSy

stem

-CIP

andPo

int-CIP

ofthewareh

ousesin

theWareh

ouse

Excelle

ncegrou

p

85


N

Mean

Rank

Sum of

Ranks N

Mean

Rank

Sum of

Ranks N

Mean

Rank

Sum of

Ranks

Negative

Ranks5

aw 6,20 31,00 Negative

Ranks0

ca 0,00 0,00 Negative

Ranks0

de 0,00 0,00

Positive

Ranks7

ax 6,71 47,00 Positive

Ranks0

cb 0,00 0,00 Positive

Ranks0

df 0,00 0,00

Ties 7ay Ties 19

cc Ties 19dg

Total 19 Total 19 Total 19

Negative

Ranks4

az 2,50 10,00 Negative

Ranks1

cd 3,50 3,50 Negative

Ranks0

dh 0,00 0,00

Positive

Ranks0

ba 0,00 0,00 Positive

Ranks3

ce 2,17 6,50 Positive

Ranks0

di 0,00 0,00

Ties 15bb Ties 15

cf Ties 19dj


Negative

Ranks2

bc 5,00 10,00 Negative

Ranks0

cg 0,00 0,00 Negative

Ranks0

dk 0,00 0,00

Positive

Ranks6

bd 4,33 26,00 Positive

Ranks0

ch 0,00 0,00 Positive

Ranks0

dl 0,00 0,00

Ties 11be Ties 19

ci Ties 19dm


Negative

Ranks1

bf 4,50 4,50 Negative

Ranks0

cj 0,00 0,00 Negative

Ranks0

dn 0,00 0,00

Positive

Ranks15

bg 8,77 131,50 Positive

Ranks0

ck 0,00 0,00 Positive

Ranks0

do 0,00 0,00

Ties 3bh Ties 19

cl Ties 19dp


Negative

Ranks3

bi 6,17 18,50 Negative

Ranks0

cm 0,00 0,00 Negative

Ranks0

dq 0,00 0,00

Positive

Ranks16

bj 10,72 171,50 Positive

Ranks0

cn 0,00 0,00 Positive

Ranks0

dr 0,00 0,00

Ties 0bk Ties 19

co Ties 19ds


Negative

Ranks2

bl 4,75 9,50 Negative

Ranks0

cp 0,00 0,00 Negative

Ranks0

dt 0,00 0,00

Positive

Ranks6

bm 4,42 26,50 Positive

Ranks0

cq 0,00 0,00 Positive

Ranks0

du 0,00 0,00

Ties 11bn Ties 19

cr Ties 19dv


Negative

Ranks0

bo 0,00 0,00 Negative

Ranks0

cs 0,00 0,00 Negative

Ranks8

dw 4,63 37,00

Positive

Ranks0

bp 0,00 0,00 Positive

Ranks0

ct 0,00 0,00 Positive

Ranks1

dx 8,00 8,00

Ties 19bq Ties 19

cu Ties 10dy


Negative

Ranks1

br 3,50 3,50 Negative

Ranks0

cv 0,00 0,00 Negative

Ranks5

dz 3,80 19,00

Positive

Ranks4

bs 2,88 11,50 Positive

Ranks0

cw 0,00 0,00 Positive

Ranks1

ea 2,00 2,00

Ties 14bt Ties 19

cx Ties 13eb


Negative

Ranks0

bu 0,00 0,00 Negative

Ranks0

cy 0,00 0,00 Negative

Ranks4

ec 3,38 13,50

Positive

Ranks0

bv 0,00 0,00 Positive

Ranks0

cz 0,00 0,00 Positive

Ranks1

ed 1,50 1,50

Ties 19bw Ties 19

da Ties 14ee


Negative

Ranks0

bx 0,00 0,00 Negative

Ranks0

db 0,00 0,00 Negative

Ranks1

ef 1,00 1,00

Positive

Ranks9

by 5,00 45,00 Positive

Ranks0

dc 0,00 0,00 Positive

Ranks0

eg 0,00 0,00

Ties 10bz Ties 19

dd Ties 18eh


aw. CoGr 2011 C1 < CoGr 2010 C1 ef. CoGr 2011 C30 < CoGr 2010 C30

ax. CoGr 2011 C1 > CoGr 2010 C1 eg. CoGr 2011 C30 > CoGr 2010 C30

CoGr 2011 C27 -

CoGr 2010 C27

CoGr 2011 C28 -

CoGr 2010 C28

CoGr 2011 C29 -

CoGr 2010 C29

CoGr 2011 C30 -

CoGr 2010 C30

eh. CoGr 2011 C30 = CoGr 2010 C30

CoGr 2011 C21 -

CoGr 2010 C21

CoGr 2011 C22 -

CoGr 2010 C22

CoGr 2011 C23 -

CoGr 2010 C23

CoGr 2011 C24 -

CoGr 2010 C24

CoGr 2011 C25 -

CoGr 2010 C25

CoGr 2011 C26 -

CoGr 2010 C26

ay. CoGr 2011 C1 = CoGr 2010 C1

CoGr 2011 C11 -

CoGr 2010 C11

CoGr 2011 C12 -

CoGr 2010 C12

CoGr 2011 C13 -

CoGr 2010 C13

CoGr 2011 C14 -

CoGr 2010 C14

CoGr 2011 C15 -

CoGr 2010 C15

CoGr 2011 C16 -

CoGr 2010 C16

CoGr 2011 C17 -

CoGr 2010 C17

CoGr 2011 C18 -

CoGr 2010 C18

CoGr 2011 C19 -

CoGr 2010 C19

CoGr 2011 C20 -

CoGr 2010 C20

CoGr 2011 C1 -

CoGr 2010 C1

CoGr 2011 C2 -

CoGr 2010 C2

CoGr 2011 C3 -

CoGr 2010 C3

CoGr 2011 C4 -

CoGr 2010 C4

CoGr 2011 C5 -

CoGr 2010 C5

CoGr 2011 C6 -

CoGr 2010 C6

CoGr 2011 C7 -

CoGr 2010 C7

CoGr 2011 C8 -

CoGr 2010 C8

CoGr 2011 C9 -

CoGr 2010 C9

CoGr 2011 C10 -

CoGr 2010 C10

between "aw" and "eh" similar

Table 6.11: Wilcoxon Rank table for the assessment results for theSystem-CIP and Point-CIP of the warehouses C1-C30 inthe control group

86


N Mean Rank

Sum of Ranks

N Mean Rank

Sum of Ranks

N Mean Rank

Sum of Ranks

Negative Ranks

1ei 4.00 4.00 Negative Ranks

2fj 5.00 10.00 Negative Ranks

3gk 5.00 15.00

Positive Ranks

4ej 2.75 11.00 Positive Ranks

7fk 5.00 35.00 Positive Ranks

6gl 5.00 30.00

Ties 14ek Ties 10fl Ties 10gm


Negative Ranks

3el 3.33 10.00 Negative Ranks

3fm 2.67 8.00 Negative Ranks

0gn 0.00 0.00

Positive Ranks

3em 3.67 11.00 Positive Ranks

3fn 4.33 13.00 Positive Ranks

1go 1.00 1.00

Ties 13en Ties 13fo Ties 18gp


Negative Ranks

5eo 6.90 34.50 Negative Ranks

1fp 5.50 5.50 Negative Ranks

4gq 6.00 24.00

Positive Ranks

7ep 6.21 43.50 Positive Ranks

7fq 4.36 30.50 Positive Ranks

11gr 8.73 96.00

Ties 7eq Ties 11fr Ties 4gs

Total 19 Total 19 Total 19Negative Ranks

0er 0.00 0.00 Negative Ranks

0fs 0.00 0.00 Negative Ranks

0gt 0.00 0.00

Positive Ranks

8es 4.50 36.00 Positive Ranks

7ft 4.00 28.00 Positive Ranks

1gu 1.00 1.00

Ties 11et Ties 12fu Ties 18gv


0eu 0.00 0.00 Negative Ranks

3fv 2.50 7.50 Negative Ranks

4gw 2.50 10.00

Positive Ranks

1ev 1.00 1.00 Positive Ranks

8fw 7.31 58.50 Positive Ranks

0gx 0.00 0.00

Ties 18ew Ties 8fx Ties 15gy


5ex 5.90 29.50 Negative Ranks

3fy 3.50 10.50 Negative Ranks

0gz 0.00 0.00

Positive Ranks

5ey 5.10 25.50 Positive Ranks

5fz 5.10 25.50 Positive Ranks

4ha 2.50 10.00

Ties 9ez Ties 11ga Ties 15hb


1fa 4.00 4.00 Negative Ranks

5gb 3.70 18.50 Negative Ranks

3hc 4.00 12.00

Positive Ranks

7fb 4.57 32.00 Positive Ranks

1gc 2.50 2.50 Positive Ranks

9hd 7.33 66.00

Ties 11fc Ties 13gd Ties 7he


6fd 3.67 22.00 Negative Ranks

7ge 5.57 39.00 Negative Ranks

2hf 3.00 6.00

Positive Ranks

1fe 6.00 6.00 Positive Ranks

2gf 3.00 6.00 Positive Ranks

6hg 5.00 30.00

Ties 12ff Ties 10gg Ties 11hh


0fg 0.00 0.00 Negative Ranks

1gh 2.00 2.00

Positive Ranks

8fh 4.50 36.00 Positive Ranks

10gi 6.40 64.00

Ties 11fi Ties 8gj

Total 19 Total 19

ej. CoGr 2011 C31 > CoGr 2010 C31

CoGr 2011 C35 - CoGr 2010 C35

CoGr 2011 C36 - CoGr 2010 C36

CoGr 2011 C37 - CoGr 2010 C37

CoGr 2011 C31 - CoGr 2010 C31

CoGr 2011 C32 - CoGr 2010 C32

CoGr 2011 C46 - CoGr 2010 C46

ek. CoGr 2011 C31 = CoGr 2010 C31

ei. CoGr 2011 C31 < CoGr 2010 C31

CoGr 2011 C38 - CoGr 2010 C38

CoGr 2011 C39 - CoGr 2010 C39

CoGr 2011 C42 - CoGr 2010 C42

CoGr 2011 C43 - CoGr 2010 C43

CoGr 2011 C33 - CoGr 2010 C33

CoGr 2011 C34 - CoGr 2010 C34

CoGr 2011 C47 - CoGr 2010 C47

CoGr 2011 C44 - CoGr 2010 C44

CoGr 2011 C45 - CoGr 2010 C45

CoGr 2011 C40 - CoGr 2010 C40

CoGr 2011 C41 - CoGr 2010 C41

CoGr 2011 C51 - CoGr 2010 C51

CoGr 2011 C52 - CoGr 2010 C52

CoGr 2011 C48 - CoGr 2010 C48

CoGr 2011 C49 - CoGr 2010 C49

CoGr 2011 C50 - CoGr 2010 C50

hf. CoGr 2011 C56 < CoGr 2010 C56hg. CoGr 2011 C56 > CoGr 2010 C56hh. CoGr 2011 C56 = CoGr 2010 C56

CoGr 2011 C53 - CoGr 2010 C53

CoGr 2011 C54 - CoGr 2010 C54

CoGr 2011 C55 - CoGr 2010 C55

CoGr 2011 C56 - CoGr 2010 C56

between "aw" and "hh" similar

Table 6.12: Wilcoxon Rank table for the assessment results for theSystem-CIP and Point-CIP of the warehouses C31-C56in the control group

87


positive: 13.4% of the cases have a negative ranking, 27.1% of thecases show a positive trend, and 59.55% of the rankings were ties.Tables 6.13 and 6.14 show the test statistics for the Wilcoxon-Signed-Rank test for the assessment results of the System-CIP and Point-CIP results of the control group. H0 can be rejected

• with a high significance level for one warehouse,• with a very significance level for five warehouses,• with significance for six warehouses and• with a low significance level for nine warehouses.

In other words, it can be assumed, that in 21 warehouses, withminimum low significance level, the warehouses did improve theirlean maturity. In the case of the other 35 warehouse, H0 cannot berejected and this indicates that these warehouses did not improvetheir lean maturity significant.Summarized for the control group this means that just 37.5% of thewarehouses did improve their lean maturity with a minimum lowsignificance level. Compared to the Warehouse Excellence group,in which 93.75% of the warehouses showed with minimum a verysignificance level an improvement, this means that the control groupimproved absolute and relative much less.

6.2.2 The Warehouse Excellence Group versus theControl Group

In earlier sections, we analyzed the development of the WarehouseExcellence group. We also compared the test statistics results ofthe Warehouse Excellence group with the control group. In thissection we will describe the direct comparison of the assessmentresults of the Warehouse Excellence group with the control group.This will be done first with the entire control group of 56 warehouses,

88


Test Statisticsa

CoGr 2011 C1 - CoGr 2010 C1

CoGr 2011 C2 - CoGr 2010 C2

CoGr 2011 C3 - CoGr 2010 C3

CoGr 2011 C4 - CoGr 2010 C4

Z -,644b -1,841c -1,140b -3,337b

Asymp. Sig. (2-tailed) .519 .066 .254 .001

CoGr 2011 C5 - CoGr 2010 C5

CoGr 2011 C6 - CoGr 2010 C6

CoGr 2011 C7 - CoGr 2010 C7

CoGr 2011 C8 - CoGr 2010 C8

Z -3,111b -1,199b ,000d -1,131b

Asymp. Sig. (2-tailed) .002 .230 1.000 .258

CoGr 2011 C9 - CoGr 2010 C9

CoGr 2011 C10 - CoGr 2010 C10

CoGr 2011 C11 - CoGr 2010 C11

CoGr 2011 C12 - CoGr 2010 C12

Z ,000d -2,751b ,000d -,552b

Asymp. Sig. (2-tailed) 1.000 .006 1.000 .581

CoGr 2011 C13 - CoGr 2010 C13

CoGr 2011 C14 - CoGr 2010 C14

CoGr 2011 C15 - CoGr 2010 C15

CoGr 2011 C16 - CoGr 2010 C16

Z ,000d ,000d ,000d ,000d

Asymp. Sig. (2-tailed) 1.000 1.000 1.000 1.000

CoGr 2011 C17 - CoGr 2010 C17

CoGr 2011 C18 - CoGr 2010 C18

CoGr 2011 C19 - CoGr 2010 C19

CoGr 2011 C20 - CoGr 2010 C20

Z ,000d ,000d ,000d ,000d

Asymp. Sig. (2-tailed) 1.000 1.000 1.000 1.000

CoGr 2011 C21 - CoGr 2010 C21

CoGr 2011 C22 - CoGr 2010 C22

CoGr 2011 C23 - CoGr 2010 C23

CoGr 2011 C24 - CoGr 2010 C24

Z ,000d ,000d ,000d ,000d

Asymp. Sig. (2-tailed) 1.000 1.000 1.000 1.000

CoGr 2011 C25 - CoGr 2010 C25

CoGr 2011 C26 - CoGr 2010 C26

CoGr 2011 C27 - CoGr 2010 C27

CoGr 2011 C28 - CoGr 2010 C28

Z ,000d ,000d -1,780c -1,807c

Asymp. Sig. (2-tailed) 1.000 1.000 .075 .071a. Wilcoxon Testb. Based on negative ranks.b. Based on positive ranks.d. The sum of the negative ranks is equal the sum of the positive ranks

Table 6.13: Wilcoxon test statistics for the assessment results for theSystem-CIP and Point-CIP of the warehouses C1-C28 inthe control group

89


Test Statisticsa

CoGr 2011 C29 - CoGr 2010 C29

CoGr 2011 C30 - CoGr 2010 C30

CoGr 2011 C31 - CoGr 2010 C31

CoGr 2011 C32 - CoGr 2010 C32

Z -1,633c -1,000c -,966b -,105b

Asymp. Sig. (2-tailed) .102 .317 .334 .916

CoGr 2011 C33 - CoGr 2010 C33

CoGr 2011 C34 - CoGr 2010 C34

CoGr 2011 C35 - CoGr 2010 C35

CoGr 2011 C36 - CoGr 2010 C36

Z -,365b -2,636b -1,000b -,207c

Asymp. Sig. (2-tailed) .715 .008 .317 .836

CoGr 2011 C37 - CoGr 2010 C37

CoGr 2011 C38 - CoGr 2010 C38

CoGr 2011 C39 - CoGr 2010 C39

CoGr 2011 C40 - CoGr 2010 C40

Z -2,111b -1,364c -2,588b -1,667b

Asymp. Sig. (2-tailed) .035 .172 .010 .096

CoGr 2011 C41 - CoGr 2010 C41

CoGr 2011 C42 - CoGr 2010 C42

CoGr 2011 C43 - CoGr 2010 C43

CoGr 2011 C44 - CoGr 2010 C44

Z -,539b -1,781b -2,401b -2,303b

Asymp. Sig. (2-tailed) .590 .075 .016 .021

CoGr 2011 C45 - CoGr 2010 C45

CoGr 2011 C46 - CoGr 2010 C46

CoGr 2011 C47 - CoGr 2010 C47

CoGr 2011 C48 - CoGr 2010 C48

Z -1,100b -1,725c -1,997c -2,791b

Asymp. Sig. (2-tailed) .271 .084 .046 .005

CoGr 2011 C49 - CoGr 2010 C49

CoGr 2011 C50 - CoGr 2010 C50

CoGr 2011 C51 - CoGr 2010 C51

CoGr 2011 C52 - CoGr 2010 C52

Z -,917b -1,000b -2,078b -1,000b

Asymp. Sig. (2-tailed) .359 .317 .038 .317

CoGr 2011 C53 - CoGr 2010 C53

CoGr 2011 C54 - CoGr 2010 C54

CoGr 2011 C55 - CoGr 2010 C55

CoGr 2011 C56 - CoGr 2010 C56

Z -1,890c -1,841b -2,144b -1,725b

Asymp. Sig. (2-tailed) .059 .066 .032 .084a. Wilcoxon Testb. Based on negative ranks.b. Based on positive ranks.d. The sum of the negative ranks is equal the sum of the positive ranks

Table 6.14: Wilcoxon test statistics for the assessment results for theSystem-CIP and Point-CIP of the warehouses C29-C56in the control group

90


then with the 18 warehouses in the control group that have detailedperformance indicators, and finally with the 38 warehouses that donot yet implement performance indicators.

Warehouse Excellence Group versus Control Group (56)

Figure 6.5 reflects the accumulated System-CIP and Point-CIP as-sessment results of the Warehouse Excellence group in comparisonwith the control group before the start of the project. Both groupshad similar maturity levels. The biggest deviations are in the fol-lowing areas: Point-CIP with a 0.5 point difference, VSM-Qualitywith a 0.7 point difference, Sustainable Problem Solving with a 0.6point difference, and Quality of Problem Solving with a 1.0 pointdifference. However, the total average score of the control group is8.6 points and the average total score of the Warehouse Excellencegroup is 9.2 points. The difference of 0.6 points shows that bothgroups had similar maturity levels at the beginning of the projectbecause 0.6 points represents 6.5% of the total average score of theWarehouse Excellence group.However, compared to this development, the gap between the Ware-house Excellence group and the control group is distinctly higherin 2011. Figure 6.6 shows this gap, especially in the Quick Reac-tion System component which has a difference of 2.0 points and theImprovement Focus, Leadership Involvement, and Regular Commu-nication components which each have a 1.7 point difference. Thetotal score of the Warehouse Excellence group is 32.8 points. Thecontrol group has a total score of 12.8 points. This means thata development is recognizable but since it is 20 points lower thanthe Warehouse Excellence group it is clearly less developed in leantechniques.Table 6.15 summarizes the comparison of the results of the Ware-house Excellence group and the control group as discussed above.The coefficient of variation has also been listed. Both groups hadvery similar figures in 2010 but in the year 2011 the Warehouse Ex-

91


Average of the control group

Average of the W

arehouse Excellence G

roup

Com

parsion of the WaEx G

roup and the Control G

roup in 2010

CONCEPTEXECUTION

Target condition

Quick reaction system

Regular comm

unication

Sustainable problem solving

Process confirmation 0.0

1.0 2.0

3.0 4.0

1.2 Point-CIP

Business requirements

Value Stream planning

Identification of improvem

ent …

Definition of target conditions

System-CIP projects

Point CIP areas 0.0 1.0

2.0 3.0

4.0

1.1 System-CIP

Target derivation

System CIP cycles

Improvem

ent focus

Leadership involvement

VSM-Q

uality

Target achievement 0.0

1.0 2.0

3.0 4.0

KPI-effect

Quality of problem

solving 0.0 1.0

2.0 3.0

4.0

Figure6.5:B

LWA

results:Warehouse

Excellencegroup

2010vs.

controlgroup2010

92


Ave

rage

of t

he c

ontro

l gro

upA

vera

ge o

f the

War

ehou

se E

xcel

lenc

e G

roup

CONCEPT EXECUTION

Com

pars

ion

of th

e W

aEx

Gro

up a

nd th

e C

ontr

ol G

roup

in 2

011

Targ

et co

nditi

on

Qui

ck re

actio

n sy

stem

Regu

lar c

omm

unic

atio

n

Sust

aina

ble

prob

lem

solv

ing

Proc

ess c

onfir

mat

ion

0.0

1.0

2.0

3.0

4.0

1.2

Poin

t-CI

P

Busin

ess r

equi

rem

ents

Valu

e St

ream

pla

nnin

g

Iden

tific

atio

n of

impr

ovem

ent …

Defin

ition

of t

arge

t con

ditio

ns

Syst

em-C

IP p

roje

cts

Poin

t CIP

are

as 0.

0 1.

0 2.

0 3.

0 4.

0

1.1

Syst

em-C

IP

Targ

et d

eriv

atio

n

Syst

em C

IP c

ycle

s

Impr

ovem

ent f

ocus

Lead

ersh

ip in

volv

emen

t

VSM

-Qua

lity

Targ

et a

chie

vem

ent

0.0

1.0

2.0

3.0

4.0

KPI-e

ffect

Qua

lity

of p

robl

em so

lvin

g

0.0

1.0

2.0

3.0

4.0

Figu

re6.6:

BLW

Aresults

:Wareh

ouse

Excelle

ncegrou

p2011

vs.controlg

roup

2011

93


WaEx

2010

CoGr (38)

2010

difference

2010

WaEx

2011

CoGr (38)

2011

difference

2011

Average Points in the

System- and Piont-CIP assessment topics9,18 7,82 1,36 32,81 10,32 22,49

Variance coefficient

in the System- and Point-CIP assessment topics88,26% 107,66% 28,35% 134,66%

WaEx

2010

CoGr (18)

2010

difference

2010

WaEx

2011

CoGr (18)

2011

difference

2011

Average Points in the System- and Piont CIP

assessment categories9,18 10,28 3,9 32,81 18,11 14,7



WaEx

2010

CoGr

2010

difference

2010

WaEx

2011

CoGr

2011

difference

2011





Table 6.15: Assessment results achieved

cellence group had a distinct projection. The Warehouse Excellencegroup also improved more uniformly overall in contrast to the con-trol group. In this group, a few good warehouses pulled the averagetotal score up from 8.61 to 12.82. An indication for this is the coef-ficient of variation for the groups. The Warehouse Excellence grouphad a narrower spread in 2011 compared to the control group. Thespread of the control group in 2011 was higher than in 2010.


Figure 6.7 shows the accumulated System-CIP and Point-CIP as-sessment results of the Warehouse Excellence group in comparisonwith control group (18). Control group (18) consists of 18 ware-houses from the entire control group. These warehouses have beenseparated because they are the only ones that measure productivitywithin the respective warehouse. This indicates that these ware-houses have a focuse on facts and figures and might also promotelean techniques. Thus, this comparison will isolate supposedly ma-ture warehouses from the entire control group.Both groups in this comparison also had similar profiles and matu-rity levels. However, the gap in the total score achieved betweenthe two groups is broader than in chapter 6.2.2. The total score ofthe Warehouse Excellence group is 9.2 points and the total score ofcontrol group (18) is 10.3 points. The biggest deviations from eachother are in the components Improvement Focus with a 1.2 pointdifference and Quality of Problem solving with 1.1 points difference.

94


Aver

age

of th

e co

ntro

l gro

upAv

erag

e of

the

War

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xcel

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e G

roup

Com

pars

ion

of th

e W

aEx

Gro

up a

nd th

e C

ontr

ol G

roup

(18)

in 2

010

CONCEPT EXECUTION

Targ

et co

nditi

on

Qui

ck re

actio

n sy

stem

Regu

lar c

omm

unic

atio

n

Sust

aina

ble

prob

lem

solv

ing

Proc

ess c

onfir

mat

ion

0.0

1.0

2.0

3.0

4.0

1.2

Poin

t-CI

P

Busin

ess r

equi

rem

ents

Valu

e St

ream

pla

nnin

g

Iden

tific

atio

n of

impr

ovem

ent …

Defin

ition

of t

arge

t con

ditio

ns

Syst

em-C

IP p

roje

cts

Poin

t CIP

are

as 0.

0 1.

0 2.

0 3.

0 4.

0

1.1

Syst

em-C

IP

Targ

et d

eriv

atio

n

Syst

em C

IP c

ycle

s

Impr

ovem

ent f

ocus

Lead

ersh

ip in

volv

emen

t

VSM

-Qua

lity

Targ

et a

chie

vem

ent

0.0

1.0

2.0

3.0

4.0

KPI-e

ffect

Qua

lity

of p

robl

em so

lvin

g

0.0

1.0

2.0

3.0

4.0

Figu

re6.7:

BLW

Aresults

:Wareh

ouse

Excelle

ncegrou

p2010

vs.controlg

roup

(18)

2010

95


WaEx

2010

CoGr (38)

2010

difference

2010

WaEx

2011

CoGr (38)

2011

difference

2011

Average Points in the

System- and Piont-CIP assessment topics9,18 7,82 1,36 32,81 10,32 22,49



WaEx

2010

CoGr (18)

2010

difference

2010

WaEx

2011

CoGr (18)

2011

difference

2011





Table 6.16: BLWA results points: Warehouse Excellence group vs.control group (18)

Figure 6.8 compares the 2011 assessment results of the WarehouseExcellence group with control group (18). The Warehouse Excel-lence group has a distinctly higher level of maturity in the major-ity of the components. Control group (18) performs better onlyin System-CIP Projects and Quality of Problem Solving. The to-tal score of the Warehouse Excellence group is 32.8 points and thecontrol group (18) has a total score of 18.1 points. Control group(18) performs better than the control group (56) but the WarehouseExcellence group still has 14.7 points more.Table 6.16 shows the results of the comparison of the WarehouseExcellence group with control group (18). The results show thatcontrol group (18) performed better than control group (56). Higheraverage total scores and a less negative development of the coeffi-cient of variation demonstrate this. However, the gap in maturityin this comparison is not as high as the gap in maturity betweenthe Warehouse Excellence group and control group (56). In sum-mary, the Warehouse Excellence group also performed better thanthe stronger control group (18).


Control group (38) consists of 38 warehouses from the entire controlgroup (56). These warehouses have been separated because they donot measure productivity within the warehouse. We assume thatthese are the less mature warehouses and we want to complete the

96


Aver

age

of th

e co

ntro

l gro

up (1

8)Av

erag

e of

the

War

ehou

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xcel

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e G

roup

Com

pars

ion

of th

e W

aEx

Gro

up a

nd th

e C

ontr

ol G

roup

(18)

in 2

011

CONCEPT EXECUTION

Targ

et co

nditi

on

Qui

ck re

actio

n sy

stem

Regu

lar c

omm

unic

atio

n

Sust

aina

ble

prob

lem

solv

ing

Proc

ess c

onfir

mat

ion

0.0

1.0

2.0

3.0

4.0

1.2

Poin

t-CI

P

Busin

ess r

equi

rem

ents

Valu

e St

ream

pla

nnin

g

Iden

tific

atio

n of

impr

ovem

ent …

Defin

ition

of t

arge

t con

ditio

ns

Syst

em-C

IP p

roje

cts

Poin

t CIP

are

as 0.

0 1.

0 2.

0 3.

0 4.

0

1.1

Syst

em-C

IP

Targ

et d

eriv

atio

n

Syst

em C

IP c

ycle

s

Impr

ovem

ent f

ocus

Lead

ersh

ip in

volv

emen

t

VSM

-Qua

lity

Targ

et a

chie

vem

ent

0.0

1.0

2.0

3.0

4.0

KPI-e

ffect

Qua

lity

of p

robl

em so

lvin

g

0.0

1.0

2.0

3.0

4.0

Figu

re6.8:

BLW

Aresults

:Wareh

ouse

Excelle

ncegrou

p2011

vs.controlg

roup

(18)

2011

97


partial comparison that we began with control group (18). Figure6.9 shows the accumulated System-CIP and Point-CIP assessmentresults of the Warehouse Excellence group in comparison with thoseof control group (38).Both groups had similar profiles and maturity levels in 2010. Thegap in the total score between the two groups is slightly larger thanin the previous comparison. The major difference is that controlgroup (38) has lower total average assessment results for the year2010. The Warehouse Excellence group scored 9.2 points and controlgroup (38) scored 7.82 points. The biggest deviations from eachother are in the components Sustainable Problem Solving with a0.9 point difference and Quality of Problem Solving Process with a0.8 point difference. These are followed by Regular Communicationand Value Stream Quality with a 0.7 point difference each, Point-CIP and Improvement Focus with a 1.3 point difference each, andQuality of Problem Solving with a 1.4 point difference.Figure 6.10 compares the Warehouse Excellence group and controlgroup (38) assessment results for 2011. The Warehouse Excellencegroup has a higher maturity level in almost all of the components.The only areas where control group (38) has more points are Sus-tainable Problem Solving, with a 0.2 point difference, and Qualityof Problem Solving Process, with a 0.7 point difference. The totalaverage score of the Warehouse Excellence group is 32.82 points.Control group (38) has a total score of 10.32 points. Control group(38) is found to have performed worse than control group (56) andthe gap with the Warehouse Excellence group is 22 points largerthan the gap with control group (56).Table 6.17 summarizes the results of the Warehouse Excellence groupin comparison with the control group. It shows that our assump-tion was correct that the warehouses that do not measure produc-tivity perform with a lower maturity level compared to warehouseswith productivity measurements. The results also show that controlgroup (38) did not improve consistently: the coefficient of variationshows this. Both groups had very similar figures in 2010. The Ware-

98


Aver

age

of th

e co

ntro

l gro

up (3

8)Av

erag

e of

the

War

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xcel

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roup

Com

pars

ion

of th

e W

aEx

Gro

up a

nd th

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ontr

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(38)

in 2

010

CONCEPT EXECUTION

Targ

et co

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Qui

ck re

actio

n sy

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Regu

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omm

unic

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n

Sust

aina

ble

prob

lem

solv

ing

Proc

ess c

onfir

mat

ion

0.0

1.0

2.0

3.0

4.0

1.2

Poin

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P

Busin

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equi

rem

ents

Valu

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ream

pla

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Iden

tific

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impr

ovem

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Defin

ition

of t

arge

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ditio

ns

Syst

em-C

IP p

roje

cts

Poin

t CIP

are

as 0.

0 1.

0 2.

0 3.

0 4.

0

1.1

Syst

em-C

IP

Targ

et d

eriv

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n

Syst

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ycle

s

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volv

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VSM

-Qua

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Targ

et a

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ent

0.0

1.0

2.0

3.0

4.0

KPI-e

ffect

Qua

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of p

robl

em so

lvin

g

0.0

1.0

2.0

3.0

4.0

Figu

re6.9:

BLW

Aresults

:Wareh

ouse

Excelle

ncegrou

p2010

vs.controlg

roup

(38)

2010

99


Average of the control group (38)Average of the W

arehouse Excellence Group

Com

parsion of the WaEx G

roup and the Control G

roup (38) in 2011

CONCEPTEXECUTION

Target condition

Quick reaction system

Regular comm

unication

Sustainable problem solving

Process confirmation 0.0

1.0 2.0

3.0 4.0

1.2 Point-CIP

Business requirements

Value Stream planning

Identification of improvem

ent …

Definition of target conditions

System-CIP projects

Point CIP areas 0.0 1.0

2.0 3.0

4.0

1.1 System-CIP

Target derivation

System CIP cycles

Improvem

ent focus

Leadership involvement

VSM-Q

uality

Target achievement 0.0

1.0 2.0

3.0 4.0

KPI-effect

Quality of problem

solving 0.0 1.0

2.0 3.0

4.0

Figure6.10:B

LWA

results:Warehouse

Excellencegroup

2011vs.

controlgroup(18)

2011

100


WaEx 2010

CoGr (38) 2010

difference 2010

WaEx 2011

CoGr (38) 2011

difference 2011

Average Points in the System- and Piont-CIP assessment topics 9,18 7,82 1,36 32,81 10,32 22,49Variance coefficient in the System- and Point-CIP assessment topics 88,26% 107,66% 28,35% 134,66%

Table 6.17: BLWA results points: Warehouse Excellence group vs.control group (38)

house Excellence group had a narrower spread in 2011 compared tothe control group. The spread of the control group in 2010 was lowerthan in 2011.

6.2.3 Intermediate Result: Lean Improvement

Subsection 6.2.1 demonstrated a noticeable improvement in the leanmaturity level of the Warehouse Excellence group. That sectionalso showed that the coefficient of variation was lower in 2011 thanin 2010. This indicates that the warehouses focused on the leanimprovement approach. The better results in the coefficient of vari-ation could be explained with the set milestone goals. Before theproject, none of the participating warehouses were mature enough toreach the milestones without an empowerment program. After theempowerment program, all warehouses reached the milestones andfulfilled the set minimum requirements. Since the milestones wereset as goals and the warehouses achieved them, a slight tendencytowards an alignment of the maturity had taken place.The notable improvement in the lean maturity levels are clearlyshown by the Wilcoxon Signed-Rank test in 6.2.1. The WilcoxonSigned-Rank test determined that differences between the data setsof 2010 and 2011 exist for 93.75% of the warehouses with a high levelof significance. In the control group, the Wilcoxon Signed-Rank testshowed that a difference between the samples of the years 2010 and2011 exists for 21 Warehouses with minimum significance. In con-

101


clusion, the percentage of warehouses and significance levels withinthe control group was lower compared to the Warehouse Excellencegroup.The direct comparison of the Warehouse Excellence group with thecontrol group shows that the improvement within the WarehouseExcellence group is higher than the improvement within the controlgroup. The biggest gap in the maturity level is seen in the com-parison with the 38 warehouses of the control group which do notmeasure the productivity, as described in chapter 6.2.2. In turn, the18 warehouses that measure productivity have the smallest gap, asshown in chapter 6.2.2. Finally, the comparison with the total con-trol group ranks between the two above-mentioned comparisons (seesubsection 6.2.2). This leads us to the definition of lean warehousingthat is described in chapter 2.4. Part of the philosophy is an analyt-ical approach to driving the continuous improvement process. Ana-lytical approaches are always based on facts and figures. Measuringproductivity is a major part in determining the path for improve-ment. Without the right path, it is difficult to reach a high level oflean maturity. Since we also identify the warehouses that measureproductivity as the most mature ones, this indicates that measuringproductivity could positively influence improvement. This, in turn,speaks for the quality of the Bosch Logistics Warehouse Assessmentthat measured the improvement (see chapter 4).

6.3 Analyzing the Impact on Productivity

The development of lean maturity was analyzed in chapter (6.2).The focus of this section is on the development of productivity KPRand KPI. First, the development of the KPR and KPI of the Ware-house Excellence group is analyzed. Then, the KPR development ofthe Warehouse Excellence group is compared with the KPR devel-opment of the control group.

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6.3.1 Productivity Development of the WarehouseExcellence Group

Warehouse Excellence Result KPR Productivity Development

From the beginning of the year 2010, warehouses in the WarehouseExcellence group measured the monthly productivity of the entirewarehouse operation. Each warehouse reported the monthly aver-age. These monthly average figures were then normalized. Thismeans that the monthly average of January 2010 was set as theindex base 100. All further figures were related to that base andrepresent the development of the original figure. For example, awarehouse had the monthly average productivity of 20 order linesper man hour in January. This would set the index figure at 100. Ifthe figure had a positive development of 10% to 22 order lines perman hour in February, the index would rise to 110. The average ofthe index developments of the warehouses is shown in figure 6.11.In 2010, the slope of the trend line was 0.0116. In 2011, the sloperose to the value of 0.0282. The coefficient of the determination ofthe trend line also rose from 0.2516 to 0.4073. This shows a clearimprovement of the KPR in the year 2011. The graph also showstypical seasonal effects on the productivity in summer and winterof those years. These seasonal effects are also an indicator that thegraph is reliable.The Wilcoxon test was used to compare the reported index figures ofeach warehouse from 2010 with the figures for each warehouse from2011. The hypotheses are as follows:

H0:: The samples n1 and n2 are from the same population

H1:: The samples n1 and n2 are not from the same population

103


Jan 10Feb 10

Mar 10

Apr 10

May 10

Jun 10Jul 10

Aug 10

Sep 10O

ct 10N

ov 10D

ec 10W


100.000101.532

103.385104.964

105.465104.254

100.746104.914

106.728106.081

108.163101.743

Jan 11Feb 11

Mar 11

Apr 11

May 11

Jun 11Jul 11

Aug 11

Sep 11O

ct 11N

ov 11D

ec 11W


109.768113.668

114.616117.314

120.246121.140

115.057126.208

122.157121.085

124.397115.949

0

20

40

60

80

100

120

140

Jan 10

Feb 10

Mar

10 Apr 10

May

10 Jun 10

Jul 10

Aug 10

Sep 10

Oct

10 N

ov 10

Dec

10 Jan 11

Feb 11

Mar

11 Apr 11

May

11 Jun 11

Jul 11

Aug 11

Sep 11

Oct

11 N

ov 11

Dec

11

Accumulated Productivity Index

Warehouse

Excellence Group Trend W

aEx

Figure6.11:W

arehouseExcellence

KPR

development

104


WaEx 2011 -

WaEx 2010

Z -8,355b

Asymp. Sig. (2-tailed) ,000

N

Mean

Rank

Sum of

Ranks

Negative

Ranks41

a 56,00 2296,00

Positive

Ranks139

b 100,68 13994,00

Ties 0c

Total 180

Statistic for Testa

a. Wilcoxon-Test


WaEx 2011 -

WaEx 2010

a. WaEx 2011 < WaEx 2010

b. WaEx 2011 > WaEx 2010

c. WaEx 2011 = WaEx 2010

Table 6.18: Wilcoxon Rank table for the Warehouse Excellence KPRindex in the years 2010 and 2011

The sample n1 are the figures for the year 2010. Sample n2 indicatesthe sample for the figures in 2011.The ranking table for the test is shown in table 6.18. It shows thatthe positive rankings exceed the negative rankings. Finally, the teststatistics in 6.18 show with high enough significance that H0 can berejected.

Warehouse Excellence Monitoring KPI ProductivityDevelopment

During the Warehouse Excellence project, the participating ware-house managers defined projects in specific areas of their warehouses.The goal of these projects was to drive the lean approach, especiallythe closed loop between the System-CIP and Point-CIP methodolo-gies. Most of the warehouses defined more than one project. How-ever, a minimum of one project was required from the WarehouseExcellence project team. The projects were closely monitored andalso followed up on regularly by the project team.

105


WaEx 2011 -

WaEx 2010

Z -8,355b

Asymp. Sig. (2-tailed) ,000

N

Mean

Rank

Sum of

Ranks

Negative

Ranks41

a 56,00 2296,00

Positive

Ranks139

b 100,68 13994,00

Ties 0c

Total 180

Statistic for Testa

a. Wilcoxon-Test


WaEx 2011 -

WaEx 2010

a. WaEx 2011 < WaEx 2010

b. WaEx 2011 > WaEx 2010

c. WaEx 2011 = WaEx 2010

Table 6.19: Wilcoxon test statistics for the Warehouse ExcellenceKPR index in the years 2010 and 2011

Figure 6.12 shows a detailed analysis of each project using one ex-ample. The KPI development is highlighted on the left side of thegraph. The definition of the KPI is displayed at the top of thefigure as is the project name of the warehouse. In this case, thename of the project is W16b. The time line for the project is alsoshown on this graph. In each example, there is a segment before thebeginning of the project. This serves as a basis for comparison. Spe-cific measures in the warehouse that deeply influence productivityare also highlighted. In this example, workforce management wasstarted in November 2011. The average productivity and deviationis listed below for the different segments. The assessment results ofthe Point-CIP for the years 2010 and 2011 are on the right side. Inaddition to this, the success factors that the warehouse focused onare also indicated (see section 2.1). Further examples are listed inappendix H.The 16 projects in the different warehouses had a massive positiveinfluence on the productivity. The improvement was by 26.02% onaverage. Table 6.20 provides the detailed results of the monitoringKPI development.

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107


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onitoringKPI

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overview

108


6.3.2 Productivity Development of the WarehouseExcellence Group versus Control Group

The KPR and KPI development of the Warehouse Excellence groupwas shown, analyzed, and interpreted in subsection 6.3.1. This sec-tion analyzes the KPR development of the control group. This com-parison is shown in figure 6.13. Both of the groups had a similardevelopment in 2010 and the seasonal effects in summer and win-ter time can be seen. The situation changed in 2011: the controlgroup showed a negative trend. The average index value of 105.26 in2010 changed to 108.26 in 2011. The trend line slope changed from0.01463 to -0.02327. In contrast, the Warehouse Excellence groupimproved its trend from an average of 104.00 in 2010 to an averageof 118.47 in 2011. The slope of the trend line increased from 0.01164to 0.02822.The non-accumulated index figures of both groups were analyzedusing the Kolmogorov-Smirnov Z test to test if improvements aresignificant or might be coincidences. In the first test, the figures ofthe Warehouse Excellence group for the year 2010 were comparedwith the figures of the control group. In the second test, the figuresfor the year 2011 were compared. The hypotheses for the test are asfollows:

H0: The samples n1 and n2 are from the same population

H1: The samples n1 and n2 are not from the same population

The sample n1 indicates the data from 2010. Sample n2 is the datafrom 2011.Table 6.21 shows the test frequency. Further results are plotted intable 6.22. In 2010, the significance level is too low to reject H0.On the other hand, H0 could be rejected with high significance in

109


Jan 10Feb 10

Mar 10

Apr 10M

ay 10Jun 10

Jul 10Aug 10

Sep 10O

ct 10N

ov 10D

ec 10W


100.000101.532

103.385104.964

105.465104.254

100.746104.914

106.728106.081

108.163101.743

Control G

roup100.000

100.051103.784

105.211108.627

106.667109.244

101.932108.948

107.633110.285

100.712

Jan 11Feb 11

Mar 11

Apr 11M

ay 11Jun 11

Jul 11Aug 11

Sep 11O

ct 11N

ov 11D

ec 11W


109.768113.668

114.616117.314

120.246121.140

115.057126.208

122.157121.085

124.397115.949

Control G

roup110.112

110.879110.935

112.570108.072

112.334108.371

103.474107.923

105.430106.657

102.408

0

20

40

60

80

100

120

140

Jan 10

Feb 10

Mar

10 Apr 10

May

10 Jun 10

Jul 10

Aug 10

Sep 10

Oct

10 N

ov 10

Dec

10 Jan 11

Feb 11

Mar

11 Apr 11

May

11 Jun 11

Jul 11

Aug 11

Sep 11

Oct

11 N

ov 11

Dec 11

Accumulated Productivity Index

Warehouse

Excellence Group

Control Group

Trend WaEx

Trend CoGr

Figure6.13:K

PRcom

parisonbetw

eenthe

Warehouse

Excellencegroup

andcontrolgroup

110


2010 2011

most extrem

differenz

Absolut ,093 ,244

Positiv ,081 ,244

Negativ -,093 -,005

Kolmogorov-Smirnov-Z ,910 2,456

Asymp. Sig. (2-tailed) ,379 ,000

Name N

2010 CoGr 207

WaEx 180

Total 387

2011 CoGr 216

WaEx 192

Total 408

Table 6.21: Kolmogorov-Smirnov-Test frequency of KPR WaEx vs.CoGr

2010 2011

most extrem

differenz

Absolut ,093 ,244

Positiv ,081 ,244

Negativ -,093 -,005

Kolmogorov-Smirnov-Z ,910 2,456

Asymp. Sig. (2-tailed) ,379 ,000

Name N

2010 CoGr 207

WaEx 180

Total 387

2011 CoGr 216

WaEx 192

Total 408

Table 6.22: Kolmogorov-Smirnov-Test of KPR WaEx vs. CoGr

2011. This means that a large difference in significance can be seenbetween the data set of the Warehouse Excellence group in 2011compared to the data set of the control group in 2011.

6.3.3 Intermediate Result: Productivity Improvement

The first analyze of productivity improvement was in subsection6.3.1 with the Warehouse Excellence KPR. Figure 6.13 shows ahigher improvement in year 2011 compared to 2010 and the Kolmogorov-Smirnov-Z test shows, with a high significance, that the data setbetween 2010 and 2011 is not from the same population. This sup-ports the thesis that an effect could influence KPR development.The analysis in subsection 6.3.1 of the projects carried out duringthe Warehouse Excellence project tries to explain what the effectcould be. The summary shows that a high improvement in the mon-

111


itoring KPIs has an effect on KPRs.These positive effects in the Warehouse Excellence group were com-pared with the control group development in subsection 6.3.2. Thegraph in figure 6.13 shows a higher productivity development forthe Warehouse Excellence group in 2011. The positive trend of thegraph is characterized by the Kolmogorov-Smirnov-Z test to show ifthe result was random or significant. The KPR index graphs from2010 could be from the same population but in 2011 the graphs showhigh significance so they are not from the same distribution.In conclusion, the data and development in 2010 are similar for bothgroups but are significantly different in 2011 which leads us to theassumption that something happened in the Warehouse Excellencegroup that did not happen in the control group and it resulted inan improvement of performance. We may suspect that this was theWarehouse Excellence project.

6.4 Review the Hypotheses

We defined the four hypotheses that we wanted to analyze in sec-tion 1.2 and they were also explained using a coordinate system infigures 1.1, 1.2, 1.3, and 1.4. The abscissa of the coordinate systemshows the lean maturity. The lean maturity improvement was ana-lyzed and an improvement in the Warehouse Excellence group wasshown in section 6.2. The ordinate shows the development of theperformance indicator. The performance indicator was analyzed insection 6.3. The Warehouse Excellence group showed an improve-ment in productivity in the result KPR and even a stronger one atthe KPI level. We now have the data and the intermediate resultswe need to make conclusions about the lean impact when discussingthe hypotheses and this is done in this section.Before we start, we will establish the basis for the discussion byshowing the relationship between the assessment results and the re-sult KPR of the Warehouse Excellence group in figure 6.14. The

112


y =

0,00

82x

- 0,1

307

R² =

0,3

184

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

0 2

4 6

8 10

12

14

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

Procentage of result KPR development

Abso

lut d

evel

opm

ent o

f BLW

A m

atur

ity p

oint

s in

Poin

t- &

Sys

tem

-CIP

Figu

re6.14:C

orrelatio

nbe

tweenab

solute

BLW

ASy

stem

-and

Point-CIP

developm

enta

ndmon

-ito

ringKPI

develope

ment

113


abscissa is the absolute lean maturity development in the System-CIP and the Point-CIP. The ordinate is the percentage of the resultKPR development from the year 2010 to the year 2011. Each pointrepresents one warehouse and the line represents the trend line. Thetrend line hits the abscissa at the value of 15.94. This indicates thateven if some efforts are taken the expected positive lean effect onproductivity KPR might not be reached. A minimum higher in-vestment is necessary to gain from the benefits. The slope of thetrend line is 0.0082, which implies a positive trend. This shows thatif more lean efforts are taken, the productivity gain is also higher.The coefficient of determination is 0.3184, which indicates how wellthe relation can be described by a linear function.

6.4.1 Review of Hypothesis I

Hypothesis I states that lean techniques have a positive impact onperformance indicators. Figure 6.14 shows that most warehousesdid have an improvement with the exception of two. The interestingthing is that these two warehouses with the negative developmentin the result productivity KPR belong to the group of warehouseswith the lowest lean maturity development. We can conclude thatlean techniques have a positive impact on performance indicatorsbut resources have to be invested into in order to reach a certainlean maturity level before gaining from the benefits.

6.4.2 Review of Hypothesis II

Hypothesis II asserts that more lean has a more positive impact onperformance indicators. The slope of the trend line in 6.14 showsthat there is a positive relationship between the lean maturity leveland the productivity indicators. This means that if you invest moreto reach a higher lean maturity you will gain from an associatedhigher increase in productivity. The figure also shows that if youdevelop your lean maturity by 30 points, productivity development

114


will increase by a minimum of 5%. To summarize, more lean has amore positive impact on performance indicators.

6.4.3 Review of Hypothesis III

Hypothesis III states that there is a mathematical correlation be-tween the factors lean maturity and performance indicators and amathematical function can be used to describe this correlation. Arelationship between the two factors can be seen in figure 6.14 but afunction to describe this correlation could not be found. For exam-ple, the coefficient of determination is 0.3184 for a linear regressionwhich is far too low to describe that correlation. In conclusion, a re-lationship can be identified but a linear mathematical function couldnot be identified.

6.4.4 Review of Hypothesis IV

Hypothesis IV asserts that lean techniques have a higher positiveimpact on performance indicators than other approaches. The anal-ysis in section 6.2 showed us that the Warehouse Excellence groupimproved their lean maturity level significantly: much more thanthe control group. A large number of the warehouses in the controlgroup did not improve their lean maturity at all so we can assumethat the control group warehouses focused on other approaches.The analysis in subsection 6.3.2 also showed us that the develop-ment of the productivity result KPR was similar in both groupsin 2010. However, the productivity result KPR development of theWarehouse Excellence group was significantly better than the controlgroup in 2011. This means that something influenced the WarehouseExcellence group in the year 2011. We assume that this relates to theWarehouse Excellence group and that their approach was superiorto the other approaches in the control group.

115


116

7 Summary & Conclusion

The roots of lean techniques date back 50 years to the productionsystems of the Japanese automotive production industry. Severalin-depth studies have verified the positive impact of lean techniquesin production environments. The research methodology of thesestudies was based on three elements:

• Measurement of the lean maturity• Measurement of performance indicators• Comparison of the samples with each other

A high level of evidence about the positive lean impact on perfor-mance indicators in the production environment can be proven bycomparing the results of these elements with each other. Lean tech-niques have also found their way into the warehouse environment.Since the warehouse environment is different from the productionenvironment, there is no guarantee that lean techniques have thesame impact. Several studies exist on lean maturity, performanceindicators or a comparison of different samples with each other butno single study could be found that combines all three elements witheach other with the goal of gaining a higher level of evidence aboutthe impact of lean techniques on performance indicators in the ware-house environment. In conclusion, the level of evidence about thepositive impact of lean approaches on performance indicators hasbeen higher in the production environment than in the warehouseenvironment until now.

117


We carried out a study in an attempt to close this gap in evidence.This study consisted of 16 warehouses in the observation group and56 warehouses in the control group. The observation group were em-powered by an intensive program with precisely defined milestones.By reaching these milestones, it was ensured that the warehouseswould implement a structured continuous improvement cycle, whichwe identified as a key element of the lean philosophy. Bosch coinedthe terms System-CIP and Point-CIP for their interpretation anddefinition of the method for a structured continuous improvementcycle process. By providing training, workshops, and coaches; weensured that all of the warehouses in the observation group reachedthe set milestones. The control group was not influenced by theempowerment program.Tools were needed to measure the progress of the lean maturityand performance indicators for each warehouse. By evaluating theexisting available tools, we were able to determine that the leanmaturity assessments that are customized for the warehouse envi-ronment do not meet our requirements. For this reason, a new leanmaturity assessment was developed, tested, and implemented. TheBosch Logistic Warehouse Assessment was developed based on anew generation of lean maturity assessments which were in use inthe production environment.The performance indicator development was measured by the KPR/-KPI Tree approach. The KPR/KPI Tree ensures that several mea-surements can be taken and linked together at the same or differentoperational level. For example, if a fully developed KRP/KPI Treewas implemented in a warehouse, it would be possible to estimate theinfluence that the increase in productivity of a picker has on totalwarehouse productivity. Since this level of development is almostnever found within warehouse operations, our study implementedand focused on the result productivity KPR of the total warehouseand the monitoring productivity KPI of specific areas within thewarehouse.After determining what has to be measured and how, we measured

118

and analyzed the generated data with descriptive and inferentialstatistics. The development of the average assessment score of theobservation group was relatively higher than the development of thecontrol group. The two sample non-parametric Wilcoxon hypothesistests for dependent data were used to test a significantly higher leanmaturity development in the observation group compared to thecontrol group.Before the study began, the development of the total productivity ofthe observation group in 2010 was very similar to the control group.During the study, the development of the observation group in 2011was higher than the control group. A significant difference betweenthe groups in 2011 was verified using the two sample non-parametricKolmogorov-Smirnov hypothesis tests for independent data.The monitoring productivity KPI of the projects, which is wherethe strongest impact of the lean activities within the warehousesoccurred, also showed a high positive development. A significantfunctional correlation between the productivity KPR and the leanmaturity development could not be verified. Instead, a positive rela-tionship between higher lean maturity and higher productivity gaincould be shown. The graph also showed that a certain lean maturitylevel has to be reached before benefits can be gained from lean.In conclusion, we have contributed to the evidence that lean tech-niques have a positive impact on performance indicators. We havealso shown that an observation group with a concentrated lean em-powerment program performs better than a control group withoutthat focus. A functional correlation between lean techniques andproductivity increase could not be shown. This could be becausenone of the warehouses had a highly developed KPR/KPI Tree. Itmight be possible to show a correlation with better coverage anda better linking of performance indicators within the observationgroup and control group.

119


120

List of Figures

1.1 Hypothesis I . . . . . . . . . . . . . . . . . . . . . . 61.2 Hypothesis II . . . . . . . . . . . . . . . . . . . . . . 71.3 Hypothesis III . . . . . . . . . . . . . . . . . . . . . 81.4 Hypothesis IV . . . . . . . . . . . . . . . . . . . . . 91.5 Structure of the thesis . . . . . . . . . . . . . . . . . 11

3.1 Lean Maturity Assessment overview (Fullerton et al.,2003)(Hallam, 2003; Pérez and Sánchez, 2000; Paniz-zolo, 1998; Shah, 2003; Jordan and Michel, 2001; Doolenand Hacker, 2005; Shan, 2008; CMMI Product Team,2010; Overboom et al., 2010; Sobanski, 2009; RobertBosch GmbH, 2012) . . . . . . . . . . . . . . . . . . 27

3.2 KPR/KPI Tree example . . . . . . . . . . . . . . . . 31

4.1 Bosch Logistics Warehouse Assessment structure . . 374.2 Bosch Logistic Warehouse Assessment Topics Part 1 414.3 Bosch Logistic Warehouse Assessment Topics Part 2 424.4 BLWA link between execution and concept . . . . . 444.5 BLWA example for linked criteria . . . . . . . . . . . 45

5.1 Warehouse Excellence empowerment structure . . . 515.2 Warehouse Excellence project milestones . . . . . . 525.3 Proof structure of the thesis . . . . . . . . . . . . . 57

6.1 BLWA results: Warehouse Excellence group 2010 vs.Warehouse Excellence group 2011 (part 1) . . . . . . 66

121

List of Figures




6.5 BLWA results: Warehouse Excellence group 2010 vs.control group 2010 . . . . . . . . . . . . . . . . . . . 92

6.6 BLWA results: Warehouse Excellence group 2011 vs.control group 2011 . . . . . . . . . . . . . . . . . . . 93

6.7 BLWA results: Warehouse Excellence group 2010 vs.control group (18) 2010 . . . . . . . . . . . . . . . . 95




6.11 Warehouse Excellence KPR development . . . . . . 1046.12 Example of monitoring KPI development . . . . . . 1076.13 KPR comparison between the Warehouse Excellence

group and control group . . . . . . . . . . . . . . . 1106.14 Correlation between absolute BLWA System- and Point-

CIP development and monitoring KPI developement 113

C.1 1.1 System-CIP Concept (Furmans and Wlcek, 2012;Rother and Shook, 2008; Sobanski, 2009; Dehdari etal., 2011) . . . . . . . . . . . . . . . . . . . . . . . . 142

C.2 1.1 System-CIP Execution (Furmans andWlcek, 2012;Sobanski, 2009; Rother and Shook, 2008) . . . . . . 143

C.3 1.2 Point-CIP Concept (Dehdari et al., 2011; Soban-ski, 2009) . . . . . . . . . . . . . . . . . . . . . . . . 144

C.4 1.2 Point-CIP Execution (Sobanski, 2009; Dehdari etal., 2011) . . . . . . . . . . . . . . . . . . . . . . . . 145

122

List of Figures

C.5 2.1 Failure Prevention System (Hoyle, 2006; Vahrenkamp,2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

C.6 2.2 Employee Involvement (Jackson and Jones, 1996;Sobanski, 2009) . . . . . . . . . . . . . . . . . . . . . 147

C.7 2.3 Standardized Work (Sobanski, 2009; Graupp andWrona, 2006) . . . . . . . . . . . . . . . . . . . . . . 148

C.8 3.1 Overhead (Sobanski, 2009) . . . . . . . . . . . . 149C.9 3.2 Outgoing Goods (Dehdari and Schwab, 2012; Soban-

ski, 2009; Furmans and Wlcek, 2012) . . . . . . . . . 150C.10 3.3 Packaging . . . . . . . . . . . . . . . . . . . . . . 151C.11 3.4 Picking . . . . . . . . . . . . . . . . . . . . . . . 152C.12 3.5 Storage . . . . . . . . . . . . . . . . . . . . . . . 153C.13 3.6 Incoming Goods Concept (Dehdari and Schwab,

2012; Furmans and Wlcek, 2012) . . . . . . . . . . . 154C.14 3.6 Incoming Goods Excecution (Furmans andWlcek,

2012; Sobanski, 2009) . . . . . . . . . . . . . . . . . 155

H.1 Project development sheet Warehouse 1 . . . . . . . 176H.2 Project development sheet Warehouse 2 . . . . . . . 177H.3 Project development sheet Warehouse 4 . . . . . . . 178H.4 Project development sheet Warehouse 5 . . . . . . . 179H.5 Project development sheet Warehouse 6 . . . . . . . 180H.6 Project development sheet Warehouse 7 . . . . . . . 181H.7 Project development sheet Warehouse 8 . . . . . . . 182H.8 Project development sheet Warehouse 9 . . . . . . . 183H.9 Project development sheet Warehouse 10 . . . . . . 184H.10 Project development sheet Warehouse 11 . . . . . . 185H.11 Project development sheet Warehouse 12 . . . . . . 186H.12 Project development sheet Warehouse 13 . . . . . . 187H.13 Project development sheet Warehouse 14 . . . . . . 188H.14 Project development sheet Warehouse 15 . . . . . . 189H.15 Project development sheet Warehouse 16 . . . . . . 190H.16 Project development sheet Warehouse 16b . . . . . . 191

123

List of Figures

124

List of Tables

3.1 Lean Production efficiency studies (Author’s illustra-tion based on Doolen and Hacker, 2005) (Inman andMehra, 1993; Biggart, 1997; Claycomb et al., 1999;Fullerton and McWatters, 2001; Germain et al., 1996;Kinney and Wempe, 2002; Fullerton et al., 2003; Mat-sui, 2007; Jayaram et al., 2008; Fullerton and Wempe,2009; Yang et al., 2011; Hofer et al., 2012; ?) . . . . 23

6.1 BLWA results: Warehouse Excellence group 2010 vs.Warehouse Excellence group 2011 average scores (part1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

6.2 BLWA results: Warehouse Excellence group 2010 vs.Warehouse Excellence group 2011 average scores (part2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.3 BLWA results: total points . . . . . . . . . . . . . . 726.4 Shapiro-Wilk test for the total assessment results of

the Warehouse Excellence group . . . . . . . . . . . 756.5 Shapiro-Wilk test for the System-CIP and Point-CIP

assessment results of the warehouses in the Ware-house Excellence group . . . . . . . . . . . . . . . . 77

6.6 Shapirot-Wilk test for the assessment results of thecontrol group . . . . . . . . . . . . . . . . . . . . . . 78

6.7 Wilcoxon Rank table for the assessment results of thewarehouses in the Warehouse Excellence group . . . 81

6.8 Wilcoxon test statistics for the assessment results ofthe warehouses in the Warehouse Excellence group . 82

125

List of Tables

6.9 Wilcoxon Rank table for the assessment results forthe System-CIP and Point-CIP of the warehouses inthe Warehouse Excellence group . . . . . . . . . . . 83

6.10 Wilcoxon test statistics for the assessment results forthe System-CIP and Point-CIP of the warehouses inthe Warehouse Excellence group . . . . . . . . . . . 85

6.11 Wilcoxon Rank table for the assessment results for theSystem-CIP and Point-CIP of the warehouses C1-C30in the control group . . . . . . . . . . . . . . . . . . 86

6.12 Wilcoxon Rank table for the assessment results for theSystem-CIP and Point-CIP of the warehouses C31-C56 in the control group . . . . . . . . . . . . . . . . 87

6.13 Wilcoxon test statistics for the assessment results forthe System-CIP and Point-CIP of the warehouses C1-C28 in the control group . . . . . . . . . . . . . . . 89

6.14 Wilcoxon test statistics for the assessment results forthe System-CIP and Point-CIP of the warehouses C29-C56 in the control group . . . . . . . . . . . . . . . 90

6.15 Assessment results achieved . . . . . . . . . . . . . . 946.16 BLWA results points: Warehouse Excellence group

vs. control group (18) . . . . . . . . . . . . . . . . . 966.17 BLWA results points: Warehouse Excellence group

vs. control group (38) . . . . . . . . . . . . . . . . . 1016.18 Wilcoxon Rank table for the Warehouse Excellence

KPR index in the years 2010 and 2011 . . . . . . . . 1056.19 Wilcoxon test statistics for the Warehouse Excellence

KPR index in the years 2010 and 2011 . . . . . . . 1066.20 Monitoring KPI development overview . . . . . . . 1086.21 Kolmogorov-Smirnov-Test frequency of KPR WaEx

vs. CoGr . . . . . . . . . . . . . . . . . . . . . . . . 1116.22 Kolmogorov-Smirnov-Test of KPR WaEx vs. CoGr 111

A.1 Classification of warehouses in the Warehouse Excel-lence group . . . . . . . . . . . . . . . . . . . . . . . 138

126

List of Tables

B.1 Classification of warehouses in the control group . . 140

D.1 BLWA results per warehouse: Warehouse Excellencegroup 2010 . . . . . . . . . . . . . . . . . . . . . . . 158

D.2 BLWA results per warehouse: Warehouse Excellencegroup 2011 . . . . . . . . . . . . . . . . . . . . . . . 159

E.1 Warehouse Excellence Group KPR development 2010 162E.2 Warehouse Excellence KPR development 2011 . . . . 163

F.1 Assessment results per warehouse: control group 2010C1-C28 . . . . . . . . . . . . . . . . . . . . . . . . . 166

F.2 Assessment results per warehouse: control group 2010C29-C56 . . . . . . . . . . . . . . . . . . . . . . . . 167



G.1 Control group KPR development 2010 . . . . . . . . 172G.2 Control group KPR development 2011 . . . . . . . . 173

127

List of Tables

128

Bibliography

Anderson, T. W. and D. A. Darling (1954). A Test of Goodness ofFit. Journal of the American Statistical Association 49 (268), p.765–769.

Arnold, D., H. Isermann, A. Kuhn, H. Tempelmeier and K. Furmans(2008). Logistik-Handbuch. Berlin and Heidelberg: Springer BerlinHeidelberg.

Augustin, H. (2009). Lean warehousing: Mit Six Sigma und LeanManagement Lagerprozesse erfolgreich gestalten ; eine Trend-studie. München: Huss.

Backhaus, K., B. Erichson, W. Plinke and R. Weiber (2003).Multivariate Analysemethoden: Eine anwendungsorientierte Ein-führung (10 ed.). Berlin: Springer.

Bartholdi, J. J. and S. T. Hackman (2011). Warehouse & Distribu-tion Science.

Bell, S. and M. A. Orzen (2011). Lean IT: enabling and sustainingyour lean transformation. New York: Productivity Press.

Bidgoli, H. (eds.) (2004). Managing the Flow of Materials Acrossthe Supply Chain // The Internet encyclopedia. Hoboken and NJand USA: John Wiley & Sons, Inc and John Wiley & Sons.

Biggart, T. (1997). The Effects of Just-in-Time Inventory SystemAdoption on Firm Performance. Ph.D. thesis.

Bol, G. (2003). Induktive Statistik: Lehr- und Arbeitsbuch (3 ed.).München and Wien: Oldenbourg.

Bortz, J. and R. Weber (2005). Statistik für Human- und Sozial-wissenschaftler: Mit 242 Tabellen (6 ed.). Springer-Lehrbuch.

129

Bibliography

Heidelberg: Springer Medizin.Bösenberg, D. and H. Metzen (1993). Lean Management: Vorsprungdurch schlanke Konzepte (2 ed.). Landsberg/Lech: Verl. ModerneIndustrie.

Brosius, F. (2011). SPSS 19 (1 ed.). Heidelberg and München andLandsberg and Frechen and Hamburg: mitp-Verl.

Bruin, T. d., R. Freeze, U. Kaulkarni and M. Rosemann (eds.)(2005). Understanding the Main Phases of Developing a MaturityAssessment Model. Australasian Conference on Information Sys-tems. Sydney: Campbell, B. and Underwood, J. and Bunker, D.

Cirrone, G., S. Donadio, S. Guatelli, A. Mantero, B. Mascialino,S. Parlati, M. Pia, A. Pfeiffer, A. Ribon and P. Viarengo (2004). Agoodness-of-fit statistical toolkit. IEEE Transactions on NuclearScience 51 (5), p. 2056–2063.

Clauß, G., F.-R. Finze and L. Partzsch (1994). Statistik für Sozi-ologen, Pädagogen, Psychologen und Mediziner. Thun [u.a.]:Deutsch.

Claycomb, C., C. Dröge and R. Germain (1999). The Effect of Just-in-Time with Customers on Organizational Design and Perfor-mance. The International Journal of Logistics Management 10 (1),p. 37–58.

Claycomb, C., R. Germain and C. Dröge (1999). Total systemJIT outcomes: inventory, organization and financial effects. In-ternational Journal of Physical Distribution & Logistics Manage-ment 29 (10), p. 612–630.

CMMI Product Team (2010). CMMIr for Services, Version 1.3:CMMI-SVC, V1.3.

Cusumano, M. A. (1994). The Limits of Lean. Sloan ManagementReview Summer, p. 27–32.

Dehdari, P. and M. Schwab (2012). Produktionsumfeld vs. Lagerum-feld. In: K. Furmans and H. Wlcek (eds.), Arbeitskreisbericht,Volume 1, p. 16–20. DVV Media Group.

130

Bibliography

Dehdari, P., M. Schwab, K. Furmans and H. Wlcek (2011). KönnenLäger schlank sein? Von Lean Production über Lean Managementzum Lean Warehousing. In: T. Wimmer (eds.), Flexibel - sicher- nachhaltig. Hamburg: DVV Media Group.

Dehdari, P., M. Schwab, K. Furmans and H. Wlcek (2012). LeanAssessment für das Lagerumfeld: - „Entschuldigung, wo geht eshier zum Leuchtturm?“. Productivity Management 5, p. 23–27.

Doolen, T. L. and M. E. Hacker (2005). A review of lean assessmentin organizations: An exploratory study of lean practices by elec-tronics manufacturers. Journal of Manufacturing Systems 24 (1),p. 55–67.

Duller, C. (eds.) (2008). Einführung in die nichtparametrischeStatistik mit SAS und R: Ein anwendungsorientiertes Lehr- undArbeitsbuch. Heidelberg: Physica-Verlag Heidelberg.

Dykes, n. (1999). Debunking Popper: A Critique of Karl Popper’sCritical Rationalism. Reason Papers (24), p. 5–26.

EN 14943 (2005). Transport services - Logistics - Glossary of terms.Flinchbaugh, J. (2009). Untangling spaghetti. Assembly 52 (2), p.75.

Fullerton, R. R. and C. S. McWatters (2001). The production perfor-mance benefits from JIT implementation. Journal of OperationsManagement 19 (1), p. 81–96.

Fullerton, R. R., C. S. McWatters and C. Fawson (2003). An exami-nation of the relationships between JIT and financial performance.Journal of Operations Management 21 (4), p. 383–404.

Fullerton, R. R. andW. F. Wempe (2009). Lean manufacturing, non-financial performance measures, and financial performance. Inter-national Journal of Operations & Production Management 29 (3),p. 214–240.

Furmans, K. and H. Wlcek (eds.) (2012). Arbeitskreisbericht: LeanManagement in Lägern. DVV Media Group.

Genschel, U. and C. Becker (2005). Schließende Statistik: Grundle-

131

Bibliography

gende Methoden. Berlin/Heidelberg: Springer-Verlag.Germain, R., C. Droge and N. Spears (1996). The implicationsof just-in-time for logistics organization management and perfor-mance. Journal of Business Logistics 17 (2), p. 19–34.

Gibbons, J. D. (2003). Nonparametric statistical inference. NewYork: Marcel Dekker.

Graupp, P. and R. J. Wrona (2006). The TWI workbook: Essentialskills of supervisors. New York: Productivity Press.

Gudehus, T. and H. Kotzab (2012). Comprehensive logistics (2 ed.).Heidelberg and and New York: Springer-Verlag Berlin Heidelberg.

Hallam, C. R. A. (2003). Lean enterprise self-assessment as a leadingindicator for accelerating transformation in the aerospace indus-try. Ph.D. thesis, Massachusetts Institute of Technology (MIT),Massachusetts.

Hofer, C., C. Eroglu and A. Rossiter Hofer (2012). The effect oflean production on financial performance: The mediating roleof inventory leanness. International Journal of Production Eco-nomics 138 (2), p. 242–253.

Holweg, M. (2007). The genealogy of lean production. Journal ofOperations Management 25 (2), p. 420–437.

Hoyle, D. (2006). ISO 9000 quality systems handbook (5 ed.). OxfordU.K. and and Burlington MA: Butterworth-Heinemann.

Huber, C. (2011). Throughput Analysis of Manual Order PickingSystems with Congestion Consideration. Ph.D. thesis, Karlsruhe.

Inman, R. A. and S. Mehra (1993). Financial Justification of JIT Im-plementation. International Journal of Operations & ProductionManagement 13 (4), p. 32–39.

Jackson, T. L. and K. R. Jones (1996). Implementing a lean man-agement system. Portland Or: Productivity Press.

Janssen, J. (2005). Statistische Datenanalyse mit SPSS für Win-dows: Eine anwendungsorientierte Einführung in das Basissystemund das Modul Exakte Tests; mit 163 Tabellen (5 ed.). Berlin and

132

Bibliography

Heidelberg: Springer.Jayaram, J., S. Vickery and C. Droge (2008). Relationship building,lean strategy and firm performance: an exploratory study in theautomotive supplier industry. International Journal of ProductionResearch 46 (20), p. 5633–5649.

Jordan, J. A. and F. J. Michel (2001). The lean company: Makingthe right choices. Dearborn and MI: Society of ManufacturingEngineers.

Kinney, M. R. and W. F. Wempe (2002). Further Evidence on theExtent and Origins of JIT’s Profitability Effects. The AccountingReview 77 (1), p. 203–225.

Lass, R. (1984). Phonology: An introduction to basic concepts. Cam-bridge and and New York: Cambridge University Press.

Lebra, T. S. and W. P. Lebra (1986). Japanese culture and behavior:Selected readings (Rev. ed. ed.). Honolulu: University of HawaiiPress.

Lehmann, E. L. and J. P. Romano (2005). Testing statistical hy-potheses (3 ed.). Springer texts in statistics. New York: Springer.

Liker, J. K. (2004). The Toyota way: 14 management principlesfrom the world’s greatest manufacturer. New York: McGraw-Hill.

Mahfouz, A. (2011). An Integrated Framework to Assess ’Leanness’Performance in Distribution Centres. Ph.D. thesis, Dublin Insti-tute of Technology, Dublin.

Matsui, Y. (2007). An empirical analysis of just-in-time productionin Japanese manufacturing companies. International Journal ofProduction Economics 108 (1-2), p. 153–164.

Oeltjenbruns, H. (2000). Organisation der Produktion nach demVorbild Toyotas: Analyse Vorteile und detaillierte Vorausset-zungen sowie die Vorgehensweise zur erfolgreichen Einführungam Beispiel eines globalen Automobilkonzerns: Zugl.: Clausthal,Techn. Univ., Diss., 2000. Innovationen der Fabrikplanung und-organisation. Aachen: Shaker.

133

Bibliography

Ohno, T. (1988). Toyota production system: Beyond large-scale pro-duction. Cambridge and Massachusetts: Productivity Press.

Olhager, J. (2012). The Role of Decoupling Points in Value ChainManagement. In: H. Jodlbauer, J. Olhager, and R. J. Schonberger(eds.), The Role of Decoupling Points in Value Chain Manage-ment, p. 37–47. Heidelberg: Physica-Verlag HD.

Overboom, M. A., de Haan J. A. C. and Naus A. J. A. M. (eds.)(2010). Measuring the degree of leanness in logistics serviceproviders: Development of a measurement tool, Porto.

Panizzolo, R. (1998). Applying the lessons learned from 27 lean man-ufacturers. International Journal of Production Economics 55 (3),p. 223–240.

Pérez, M. P. and A. M. Sánchez (2000). Lean production and sup-plier relations: a survey of practices in the Aragonese automotiveindustry. Technovation 20 (12), p. 665–676.

Pfeiffer, W. and E. Weiß (1994). Lean Management. Grundlagen derFührung und Organisation lernender Unternehmen. 2., überarb.und erw. Aufl. Berlin: Schmidt.

Razali, N. M. and Y. B. Wah (2011). Power comparisions of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests.Journal of Statistical Modeling and Analytics 2 (1), p. 21–33.

Reuter, C. (2009). Logistikrelevante Lösungen auf der Basis vonLean-Management bei kleinen Losgrössen und hoher Varianten-vielfalt. Ph.D. thesis, Universität Stuttgart, Heimsheim.

Robert Bosch GmbH (2012). Bosch Production System AssessmentV. 3.1.

Rother, M. and S. Kinkel (2009). Die Kata des Weltmarktführers:Toyotas Erfolgsmethoden (1 ed.). Frankfurt: Campus Verl.

Rother, M. and J. Shook (2008). Learning to see: Value-streammapping to create value and eliminate muda (1.3 ed.). A lean toolkit method and workbook. Cambridge and Massachusetts: LeanEnterprise Inst.

134

Bibliography

Sá, J. P. M. d. (eds.) (2008). Applied Statistics Using SPSS, STATIS-TICA, MATLAB and R. Berlin and Heidelberg: Springer-Verlag.

Scheer, A.-W. (eds.) (2005). Corporate Performance Management:ARIS in der Praxis; mit 5 Tab. Berlin and Heidelberg: Springer.

Shah, R. (2003). Lean manufacturing: context, practice bundles,and performance. Journal of Operations Management 21 (2), p.129–149.

Shan, R. S. (2008). The role of assessment in a lean transforma-tion. Ph.D. thesis, Massachusetts Institute of Technology (MIT),Massachusetts.

Sobanski, E. B. (2009). Assessing Lean Warehousing: Developmentand validation of a lean assessment tool. Ph.D. thesis, OklahomaState University, Oklahoma.

Spee, D. and J. Beuth (2012). Lagerprozesse effizient gestalten: LeanWarehousing in der Praxis erfolgreich anwenden. München: Huss-Verl.

ten Hompel, M., T. Schmidt and L. Nagel (2007). Materialflusssys-teme: Förder- und Lagertechnik (3., vollst. neu bearb. Aufl ed.).Berlin: Springer.

Toutenburg, H., C. Heumann, M. Schomaker and M. Wißmann(eds.) (2009). Arbeitsbuch zur deskriptiven und induktiven Statis-tik (2 ed.). Springer-Lehrbuch. Berlin and Heidelberg: SpringerBerlin Heidelberg.

Vahrenkamp, R. (2010). Von Taylor zu Toyota: Rationalisierungs-debatten im 20. Jahrhundert (1 ed.). Lohmar and Köln: Eul.

VDI 3629 (2005). Basic organisational functions in warehousing.Wisser, J. (2009). Der Prozess Lagern und Kommissionieren imRahmen des Distribution Center Reference Model (DCRM). Wis-senschaftliche Berichte des Institutes für Fördertechnik und Lo-gistiksysteme der Universität Karlsruhe (TH). Karlsruhe: Uni-versitätsverlag.

Womack, J. P. (2007). The machine that changed the world: The

135

Bibliography

story of lean production - Toyotas secret weapon in the global carwars that is revolutionizing world industry (1 ed.). London: FreePress.

Womack, J. P. and D. T. Jones (2004). Lean thinking: Ballast ab-werfen, Unternehmensgewinne steigern ([Erw. und aktualisierteNeuausg.] ed.). Management. Frankfurt am Main: Campus-Verl.

Yang, M. G., P. Hong and S. B. Modi (2011). Impact of lean man-ufacturing and environmental management on business perfor-mance: An empirical study of manufacturing firms. InternationalJournal of Production Economics 129 (2), p. 251–261.

Zidel, T. (2006). A lean guide to transforming healthcare: How toimplement lean principles in hospitals, medical offices, clinics, andother healthcare organizations. Milwaukee and Wis: ASQ QualityPress.

136

A Appendix - WarehouseExcellence Group DataSheet

137

A Appendix - Warehouse Excellence Group Data Sheet

# Pallets# B

ins# Trucks

# Pallets# Trucks

# PalletsW

1E

ME

AU

BK

LSP

DY

es60

22.00017.000

15350

20250

W2

EM

EA

UB

KB

oschP

No

1602.520

21.6007

11040

350W

3E

ME

AU

BI

Bosch

DN

o 70

2.50046.000

1050

12150

W4

EM

EA

UB

GLS

PD

Yes

7540.000

16.00070

1.00040

800W

5E

ME

AU

BI

LSP

PY

es80

40.00030.000

401.000

401.000

W6

EM

EA

UB

KB

oschP

No

3304.500

10.50070

40050

300W

7E

ME

AU

BG

LSP

DY

es75

15.00020.000

40N

/A40

N/A

W8

EM

EA

UB

KLS

PP

Yes

3512.000

2.50025

40040

600W

9E

ME

AU

BK

Bosch

PN

o220

9.50015.000

40800

30600

W10

EM

EA

UB

K/U

BG

LSP

DN

o120

12.90030.000

4210

10220

W11

EM

EA

UB

KLS

PP

Yes

1001.000

10.000N

/A200

N/A

350W

12E

ME

AU

BK

LSP

PY

es45

15.000N

/A35

N/A

70N

/AW

13E

ME

AU

BG

LSP

PY

es150

25.00013.800

75700

45700

W14

EM

EA

UB

KB

oschP

No

363.300

6.00020

45015

350W

15E

ME

AU

BK

LSP

PN

o16

2.5006.000

3150

6180

W16

EM

EA

UB

K/U

BG

Bosch

DN

o125

14.00060.000

4-6400

4-6N

/A

*W

H = W

arehouse**

EM

EA

= Europe/M

iddle-East/A

frica***

D = D

istributionP

= Production

Service Provider

WH

*C

ode R

egion**B

usi-ness U

nit

Storage Capacity

Inbound Vol./Day

WH

Type***# Staff

Clients

beside B

osch

Outbound Vol./D

ay

TableA.1:C

lassificationofw

arehousesin

theWarehouse

Excellencegroup

138

B Appendix - Control GroupData Sheet

139

B Appendix - Control Group Data Sheet

# PalletsC1 Asia-Pacific UBK LSP Production 6.000C2 Asia-Pacific UBK LSP Production 9.000C3 Asia-Pacific UBI LSP Distribution 4.891C4 Asia-Pacific UBK LSP Distribution 2.922C5 Asia-Pacific UBK LSP Distribution 12.640C6 Asia-Pacific UBI LSP Distribution 12.000C7 Asia-Pacific UBG Bosch Distribution 4.500C8 Asia-Pacific UBK LSP Production 5.000C9 Asia-Pacific UBK Bosch Distribution 1.353

C10 Asia-Pacific UBK Bosch Distribution 5.672C11 Asia-Pacific UBK Bosch Distribution 911C12 Asia-Pacific UBI Bosch Production 3.709C13 Asia-Pacific UBK Bosch Distribution 430C14 Asia-Pacific UBK Bosch Distribution 950C15 Asia-Pacific UBK Bosch Distribution 595C16 Asia-Pacific UBK Bosch Distribution 207C17 Asia-Pacific UBK Bosch Distribution 383C18 Asia-Pacific UBK Bosch Distribution 947C19 Asia-Pacific UBK Bosch Distribution 1.166C20 Asia-Pacific UBK Bosch Distribution 1.984C21 Asia-Pacific UBK Bosch Distribution 1.118C22 Asia-Pacific UBK Bosch Distribution 896C23 Asia-Pacific UBK Bosch Distribution 633C24 Asia-Pacific UBK Bosch Distribution 721C25 Asia-Pacific UBK Bosch Distribution 4.058C26 Asia-Pacific UBK Bosch Distribution 1.732C27 Europe/Middle East/Africa UBK LSP Distribution 5.255C28 Europe/Middle East/Africa UBG LSP Distribution 3.900C29 Europe/Middle East/Africa UBG LSP Distribution 4.800C30 Europe/Middle East/Africa UBK LSP Production 3.000C31 Europe/Middle East/Africa UBK LSP Distribution 800C32 Europe/Middle East/Africa UBK LSP Distribution 6.652C33 Europe/Middle East/Africa UBK LSP Distribution 1.018C34 Europe/Middle East/Africa UBG LSP Distribution 12.800C35 Europe/Middle East/Africa UBK LSP Distribution N/AC36 Europe/Middle East/Africa UBK LSP Production 5.800C37 Europe/Middle East/Africa UBG LSP Distribution 14.100C38 Europe/Middle East/Africa UBI Bosch Production 10.433C39 Europe/Middle East/Africa UBG LSP Production 11.400C40 Europe/Middle East/Africa UBK Bosch Production 7.600C41 Europe/Middle East/Africa UBI LSP Distribution 9.300C42 Europe/Middle East/Africa UBG LSP Production 12.000C43 Europe/Middle East/Africa UBG LSP Production 18.000C44 Europe/Middle East/Africa UBK LSP Distribution 9.230C45 Europe/Middle East/Africa UBK LSP Distribution 3.000C46 Europe/Middle East/Africa UBG Bosch Production 7.550C47 Europe/Middle East/Africa UBG Bosch Distribution 5.250C48 Europe/Middle East/Africa UBG LSP Distribution 16.000C49 Europe/Middle East/Africa UBK LSP Distribution 1.180C50 Europe/Middle East/Africa UBK LSP Production 850C51 Latin America UBK Bosch Production 3.120C52 Latin America UBK LSP Distribution 600C53 North America UBI Bosch Distribution 3.344C54 North America UBK Bosch Production 10.000C55 North America UBK LSP Production 8.500C56 North America UBG Bosch Production 3.389

Storage Capacity

Warehouse Type

WarehouseCode

Service Provider

Business Unit

Region

Table B.1: Classification of warehouses in the control group

140

C Appendix - AssessmentQuestionaire

The development of the Bosch Logistic Warehouse Assessment (BLWA)is described in chapter4. We remember that the BLWA is based onthe Bosch Production System Assessment V. 3.1. Some parts ofthe Bosch Production System Assessment V. 3.1. were used as is,some parts derived, and some parts developed new for the BLWA.However, the Bosch Production System Assessment V. 3.1 is theintellectual property of Bosch and classified as strictly confidential.This means that parts that are used as is or derived cannot be pub-lished and only the parts that are totally new could be published.Nevertheless, we looked for literature sources that explain the mainpurpose of the parts that cannot be published. These parts wererated from bad to very good.

141

C Appendix - Assessment QuestionaireC

on

fide

ntia

lN

am

e o

f Wa

reh

ou

se

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

De

sc

riptio

nS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Bu

sin

ess re

qu

irem

en

ts

To

esta

blis

h a

vis

ion

sh

ou

ld b

e a

n in

teg

ral p

art o

f the

lea

n

pra

ctis

e in

a w

are

ho

use

. (So

ba

nski 2

00

9, p

.21

0) T

o re

ach

this

vis

ion

, KP

I-Tre

es c

an

be

use

d to

de

rive

go

als

from

the

bu

sin

ess

(cu

sto

me

r/ma

rke

t) for th

e w

are

ho

use

. (Fu

rma

ns, 2

01

2, p

. 79

)

ba

do

kg

oo

dve

ry g

oo

d0

Va

lue

stre

am

pla

nn

ing

So

ba

nski (2

00

9, p

.21

0) e

xp

lain

s th

e im

po

rtan

ce

of v

alu

e s

trea

m

ma

pp

ing

. A h

igh

er m

atu

rity c

an

be

rea

ch

ed

by h

avin

g v

alu

e

stre

am

ma

pp

ing

an

d v

alu

e s

trea

m d

esig

n fo

r all p

roce

sse

s w

ithin

the

wa

reh

ou

se

s. T

he

va

lue

stre

am

ma

ps s

ho

uld

inclu

de

the

info

rma

tion

an

d m

ate

rial flo

ws a

nd

ke

y p

erfo

rma

nce

ind

ica

tors

.

(Ro

the

r, 20

08

, pa

rt II). A y

ea

rly u

pd

ate

of th

e m

ap

s a

re n

ece

ssa

ry

(Ro

the

r, 20

08

, pa

rt V).

In a

wa

reh

ou

se

with

hig

he

r ma

turity

, the

role

of th

e v

alu

e s

trea

m

ma

na

ge

r sh

ou

ld b

e im

ple

me

nte

d a

nd

the

va

lue

stre

am

de

sig

n

sh

ou

ld le

ad

to a

pu

ll syste

m. (R

oth

er, 2

00

8, p

art I &

III)

ba

do

kg

oo

dve

ry g

oo

d0

Ide

ntific

atio

n o

f imp

rove

me

nt a

ctiv

ities

Ro

the

r (20

08

, pa

rt V) m

en

tion

s th

at th

e im

pro

ve

me

nt a

ctiv

ities

sh

ou

ld le

ad

from

va

lue

stre

am

ma

pp

ing

to v

alu

e s

trea

m d

esig

n.

Th

ese

activ

ities n

ee

d to

ha

ve

me

asu

rab

le g

oa

ls. A

lso

, a

de

riva

tion

from

the

bu

sin

ess re

qu

irem

en

t co

uld

he

lp id

en

tify

su

cce

ssfu

l imp

rove

me

nt a

ctiv

ities. (F

urm

an

s, 2

01

2, p

.80

)

ba

do

kg

oo

dve

ry g

oo

d0

De

finitio

n o

f targ

et c

on

ditio

ns fo

r Po

int-C

IP

De

hd

ari e

t al. (2

01

1) m

en

tion

tha

t a ta

rge

t co

nd

ition

with

a h

igh

ma

turity

co

nsis

t of a

sta

nd

ard

, a k

ey p

erfo

rma

nce

ind

ica

tor, a

nd

sta

bility

crite

ria.

ba

do

kg

oo

dve

ry g

oo

d0

Syste

m-C

IP p

roje

cts

Ro

the

r (20

08

, pa

rt V) d

escrib

es th

e fo

llow

ing

req

uire

me

nts

for

pro

jects

. Firs

t, it ha

s to

be

exa

ctly

de

scrib

ed

wh

at y

ou

pla

n to

do

,

wh

en

, an

d h

ow

(ste

p-b

y-s

tep

). Th

en

, me

asu

rab

le g

oa

ls a

re

ne

ed

ed

. Fin

ally

, cle

ar c

he

ckp

oin

ts w

ith re

al d

ea

dlin

es a

nd

na

me

d

revie

we

rs a

re re

qu

ired

. He

als

o h

igh

ligh

t als

o th

e im

po

rtan

ce

of

the

va

lue

stre

am

ma

na

ge

r with

in th

is c

on

text.

ba

do

kg

oo

dve

ry g

oo

d0

Po

int-C

IP a

rea

s:

- On

e P

oin

t-CIP

for w

ho

le

wa

reh

ou

se

Po

int C

IP a

rea

s:

- Po

int C

IP d

on

e fo

r

min

imu

m tw

o w

are

ho

use

se

ctio

ns

Po

int C

IP a

rea

s:

- Po

int C

IP d

on

e

se

pa

rate

ly fo

r eve

ry

wa

reh

ou

se

se

ctio

n (IG

,

Sto

rag

e, …

)

Po

int C

IP a

rea

s:

- Po

int C

IP d

on

e

se

pa

rate

ly fo

r eve

ry

wa

reh

ou

se

se

ctio

n, c

irca

10

op

era

tors

pe

r tea

m

0.0

0

System CIP

1.1

Co

mm

en

tsT

op

ics

Wa

reh

ou

se

An

aly

sis

1.0

CONCEPT

Po

ints

Ave

rag

e

Le

ve

l

IST

-AU

FN

AH

ME

Them

en

feld

er u

nd

Gew

ichtu

ng

1 K

ontin

uie

rliche V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reife

nde T

hem

en

We

rtstr

om

an

aly

se

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.1:1.1

System-C

IPConcept(Furm

ansandW

lcek,2012;Rotherand

Shook,2008;Soban-ski,2009;D

ehdarietal.,2011)

142

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

De

sc

rib

tio

nK

PI

KP

IK

PI

KP

I

Ta

rge

t d

eriva

tio

n

Th

e m

an

ag

em

en

t h

as to

se

t th

e g

oa

ls. (F

urm

an

s, 2

01

2, p

.82

)b

ad

ok

go

od

ve

ry g

oo

d0

Syste

m-C

IP c

ycle

s

A h

igh

er

ma

turity

ca

n b

e r

ea

ch

ed

by th

e n

um

be

r o

f fo

rma

l a

nn

ua

l

Syste

m-C

IP c

ycle

s (

Ka

ize

n)

eve

nts

co

nd

ucte

d a

t th

e f

acili

ty.

So

ba

nski (2

00

8, p

. 2

34

) sp

ea

ks f

rom

1 to

10

. b

ad

ok

go

od

ve

ry g

oo

d0

Imp

rove

me

nt fo

cu

s

Fu

rma

ns (

20

12

, p

. 8

1)

sa

ys th

at th

e im

pro

ve

me

nt sh

ou

ld b

e in

th

e

ke

y p

erf

orm

an

ce

in

dic

ato

rs o

f q

ua

lity, d

eliv

ery

pe

rfo

rma

nce

, a

nd

co

sts

.

ba

do

kg

oo

dve

ry g

oo

d0

Le

ad

ers

hip

in

vo

lve

me

nt

Ro

the

r (2

00

8, p

art

V)

me

ntio

ns th

at th

e r

ole

of

the

ma

na

ge

r is

to

kn

ow

th

e v

alu

e s

tre

am

an

d d

rive

th

e im

pro

ve

me

nt w

ork

fo

rwa

rd.

ba

do

kg

oo

dve

ry g

oo

d0

Va

lue

str

ea

m q

ua

lity

It is im

po

rta

nt to

use

sta

nd

ard

ize

d ico

ns to

ha

ve

th

e s

am

e

un

de

rsta

nd

ing

of

the

va

lue

str

ea

m. M

ore

in

form

atio

n lik

e p

roce

ss

KP

Is le

ad

s to

a m

ore

ma

ture

va

lue

str

ea

m q

ua

lity. (F

urm

an

s,

20

08

, p

.80

)

ba

do

kg

oo

dve

ry g

oo

d0

Ta

rge

t a

ch

ieve

me

nt:

Th

e g

oa

ls s

et b

y th

e m

an

ag

em

en

t h

ave

to

be

re

ach

ed

with

in a

ce

rta

in tim

e.

ba

do

kg

oo

dve

ry g

oo

d0

0.0

0

1.1

System CIP

EXECUTION

Ave

rag

e

IST

-AU

FN

AH

ME

Them

en

feld

er

und

Gew

ichtu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

an

aly

se

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.2:1

.1Sy

stem

-CIP

Execution(Furman

san

dW

lcek,2

012;

Soba

nski,2

009;

Rothe

ran

dSh

ook,

2008)

143

C Appendix - Assessment QuestionaireC

on

fide

ntia

lN

am

e o

f Wa

reh

ou

se

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Ta

rge

t co

nd

ition

De

hd

ari e

t al. (2

01

1) m

en

tion

tha

t a ta

rge

t co

nd

ition

with

a h

igh

ma

turity

co

nsis

t of a

sta

nd

ard

, a k

ey p

erfo

rma

nce

ind

ica

tor, a

nd

sta

bility

crite

ria. A

dd

ition

ally

, sta

nd

ard

s s

ho

uld

be

vis

ua

lize

d o

n

the

sh

op

floo

r.

ba

do

kg

oo

dve

ry g

oo

d0

Qu

ick re

actio

n s

yste

m

A q

uic

k re

actio

n s

yste

m is

a s

tan

da

rd th

at e

xp

lain

s h

ow

a

em

plo

ye

e s

ho

uld

rea

ct if u

ne

xp

ecte

d th

ing

s o

ccu

rs a

nd

de

fine

s

wh

at th

e e

sca

latio

n lim

itis fo

r rep

ortin

g it to

his

su

pe

rvis

or.

(De

hd

ari e

t al., 2

01

1)

ba

do

kg

oo

dve

ry g

oo

d0

Re

gu

lar c

om

mu

nic

atio

n

Re

gu

lar c

om

mu

nic

atio

n w

ith a

sso

cia

tes in

cre

ase

s a

wa

ren

ess o

f

wo

rk p

lan

s, in

div

idu

al a

nd

de

pa

rtme

nta

l pe

rform

an

ce

, go

als

,

assig

nm

en

ts,

imp

rove

me

nts

, an

d c

ha

ng

es. (S

ob

an

ski, 2

00

8, p

. 20

3). D

eh

da

ri

et a

l. (20

11

) me

ntio

n th

at th

e a

ge

nd

a, d

ura

tion

, an

d th

e fo

cu

s o

f

the

co

mm

un

ica

tion

sh

ou

ld b

e d

efin

ed

.

ba

do

kg

oo

dve

ry g

oo

d0

Su

sta

ina

ble

pro

ble

m s

olv

ing

Pro

ble

m s

olv

ing

activ

ities a

re o

rga

niz

ed

into

tea

m-b

ase

d

fun

ctio

ns. In

a h

igh

ly m

atu

re s

yste

m, e

mp

loye

es a

re e

mp

ow

ere

d

to, u

tilize

, pa

rticip

ate

, initia

te, a

nd

lea

d p

rob

lem

-so

lvin

g a

ctiv

ities

au

ton

om

ou

sly

, with

ou

t sig

nific

an

t ma

na

ge

me

nt in

vo

lve

me

nt.

(So

ba

nski, 2

00

8, p

.20

0) A

dd

ition

ally

, stru

ctu

red

pro

ble

m s

olv

ing

me

tho

do

log

ies s

ho

uld

be

use

d to

de

term

ine

the

roo

t ca

use

s o

f pro

ble

ms a

s th

ey a

rise

. (So

ba

nski, 2

00

8,

p.2

04

)

ba

do

kg

oo

dve

ry g

oo

d0

Pro

ce

ss c

on

firma

tion

Qu

ality

ve

rifica

tion

an

d in

sp

ectio

n p

roce

du

res in

fun

ctio

ns e

nsu

re

tha

t the

sta

nd

ard

op

era

ting

pro

ce

du

res fo

r ea

ch

pro

ce

ss a

re

pe

rform

ed

with

min

ima

l erro

rs. (S

ob

an

ski, 2

00

8, p

20

5)

Th

e d

iffere

nt h

iera

rch

y le

ve

ls a

re a

lso

invo

lve

d in

the

pro

ce

ss

co

nfirm

atio

n. (D

eh

da

ri et a

l, 20

11

)

ba

do

kg

oo

dve

ry g

oo

d0

0.0

0

Po

ints

Co

mm

en

ts

1.2

Point CIP

To

pic

s

Wa

reh

ou

se

An

aly

sis

1.0

Ave

rag

e

Le

ve

l

CONCEPT

IST

-AU

FN

AH

ME

Them

en

feld

er u

nd

Ge

wic

htu

ng

1 K

ontin

uie

rliche V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reife

nde T

hem

en

We

rtstro

mana

lyse

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.3:1.2

Point-CIP

Concept

(Dehdariet

al.,2011;Sobanski,2009)

144

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

KP

IK

PI

KP

IK

PI

KP

I

KP

I-e

ffe

ct

So

ba

nski (2

00

8, p

.22

2)

de

fin

es th

at a

mo

re m

atu

re s

yste

m n

ee

ds

less tim

e to

ach

ieve

ta

rge

ts.

ba

do

kg

oo

dve

ry g

oo

d0

Qu

alit

y o

f p

rob

lem

so

lvin

g

Th

e r

oo

t ca

use

an

aly

sis

ha

s to

be

do

ne

with

th

e r

igh

t to

ols

. T

he

co

un

term

ea

su

res s

ho

uld

ha

ve

a tro

ub

lesh

oo

tin

g e

ffe

ct. M

ore

ove

r

the

pro

ble

ms s

ho

uld

be

so

lve

d p

erm

an

en

tly a

nd

ch

ecke

d w

ith

a

follo

w u

p. (D

eh

da

ri e

t a

l, 2

01

1)

ba

do

kg

oo

dve

ry g

oo

d0

0.0

0

1.2

Point CIP

EXECUTION

Ave

rag

eIS

T-A

UF

NA

HM

E

Them

en

feld

er

und

Ge

wic

htu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

ana

lyse

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.4:1

.2Po

int-CIP

Execution(Sob

anski,2009;D

ehda

riet

al.,2011)

145

C Appendix - Assessment Questionaire Co

nfid

en

tial

Na

me

of W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Wo

rk c

on

ten

t

Firs

t, the

failu

re h

as to

de

tecte

d. If m

ea

su

res o

r

pro

ce

sse

s a

re in

sta

lled

tha

t su

pp

ort d

ete

ctio

n a

nd

eve

n p

reve

ntio

n, th

en

the

failu

re p

reve

ntio

n s

yste

m is

ma

ture

. (Ho

yle

, 20

06

, p.3

4)

Pa

rts id

en

tified

as b

ad

ha

ve

to b

e ta

ke

n rig

ht o

ut o

f

the

pro

ce

ss. (V

ah

ren

ka

mp

, 20

10

, p1

74

)

ba

do

kg

oo

dve

ry g

oo

d0

Vis

ua

liza

tion

:

- vis

ua

liza

tion

to s

up

po

rt

failu

re d

ete

ctio

n

imp

lem

en

ted

Vis

ua

liza

tion

:

- vis

ua

liza

tion

to s

up

po

rt

failu

re c

orre

ctio

n

imp

lem

en

ted

(e. g

.

vis

ua

lize

d re

actio

n lim

it,

vis

ua

lize

d u

rge

ncy p

lan

s)

Vis

ua

liza

tion

:

- vis

ua

liza

tion

to s

up

po

rt

failu

re p

reve

ntio

n (e

.g.

vis

ua

lize

d in

stru

ctio

ns fo

r

pa

ckin

g e

tc.)

Vis

ua

liza

tion

:

- vis

ua

liza

tion

of

me

asu

red

pro

ce

ss

pa

ram

ete

rs a

s a

n e

arly

wa

rnin

g s

yste

m0

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

Inte

rna

l erro

r rate

:

- dis

tinctio

n b

etw

ee

n

inte

rna

lly c

au

se

d a

nd

exte

rna

l (no

n-o

win

g)

failu

res

Inte

rna

l erro

r rate

:

- sta

ble

ach

ieve

me

nt o

r

po

sitiv

e tre

nd

of fa

ilure

for m

ore

tha

n 6

mo

nth

s

Inte

rna

l erro

r rate

:

- sta

ble

ach

ieve

me

nt o

r

po

sitiv

e tre

nd

of fa

ilure

for

mo

re th

an

1 y

ea

r

Inte

rna

l erro

r rate

:

- sta

ble

ach

ieve

me

nt o

r

po

sitiv

e tre

nd

of fa

ilure

for m

ore

tha

n 2

ye

ars

0

0.0

0

2.1

EXECUTION CONCEPT

Ave

rag

e

Ave

rag

e

Failure Prevention System

Co

mm

en

tsW

are

ho

use

An

aly

sis

1.0

Po

ints

Le

ve

l

To

pic

s

IST

-AU

FN

AH

ME

Them

en

feld

er u

nd

Ge

wic

htu

ng

1 K

ontin

uie

rliche V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reife

nde T

hem

en

We

rtstro

mana

lyse

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.5:2.1

FailurePrevention

System(H

oyle,2006;Vahrenkamp,2010)

146

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Invo

lve

me

nt:

Wo

rke

r in

vo

lve

me

nt is

im

po

rta

nt fo

r su

sta

inin

g

imp

rove

me

nts

an

d e

mp

ow

erin

g th

e

wo

rkfo

rce

. T

hu

s, th

e p

erc

en

tag

e o

f th

e a

ctivitie

s th

at

are

in

itia

ted

by th

e w

ork

ers

is tra

cke

d. (S

ob

an

ski,

20

08

, p

.16

8)

ba

do

kg

oo

dve

ry g

oo

d0

Ta

rge

t d

ep

loym

en

t

Wa

reh

ou

se

s ta

rge

ts h

ave

to

be

de

rive

d f

or

ea

ch

tea

m. T

he

te

am

le

ad

er

se

t th

e ta

rge

ts f

or

his

sp

ecific

tea

m d

eriviv

ed

fro

m h

is p

ers

on

al ta

rge

ts. (J

ackso

n,

19

96

, p

.10

9)

ba

do

kg

oo

dve

ry g

oo

d0

Qu

alif

ica

tio

n /

Tra

inin

g:

- b

ott

len

eck t

rea

tme

nt

by

fle

xib

le w

ork

forc

e

Qu

alif

ica

tio

n /

Tra

inin

g:

- re

gu

lar

tra

inin

g (

off

-th

e-

job

) fo

r te

am

le

ad

er

pro

vid

ed

to

fa

cili

tate

eff

ective

te

am

ing

,

co

mm

un

ica

tio

n s

kill

s,

co

ntin

uo

us im

pro

ve

me

nt

me

tho

ds (

e.

g.

Me

tho

ds

Sh

op

)

Qu

alif

ica

tio

n /

Tra

inin

g:

- re

gu

lar

tra

inin

g (

off

-th

e-

job

) fo

r e

mp

loye

es

pro

vid

ed

to

fa

cili

tate

eff

ective

te

am

ing

,

co

mm

un

ica

tio

n s

kill

s,

co

ntin

uo

us im

pro

ve

me

nt

me

tho

ds (

e.

g.

Me

tho

ds

Sh

op

)

Qu

alif

ica

tio

n /

Tra

inin

g:

- a

s le

ve

l 3

0

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

Mu

lti-skill

ed

op

era

tors

:

Op

era

tors

are

no

t tr

ain

ed

fo

r d

iffe

ren

t fu

nctio

ns

Mu

lti-skill

ed

op

era

tors

:

25

-49

% o

f th

e o

pe

rato

rs

are

tra

ine

d f

or

mo

re t

ha

n

on

e f

un

ctio

n

Mu

lti-skill

ed

op

era

tors

:

50

-74

% o

f th

e o

pe

rato

rs

are

tra

ine

d f

or

mo

re t

ha

n

on

e f

un

ctio

n

Mu

lti-skill

ed

op

era

tors

:

75

-90

% o

f th

e o

pe

rato

rs

are

tra

ine

d f

or

mo

re t

ha

n

on

e f

un

ctio

n

Mu

lti-skill

ed

op

era

tors

:

Mo

re t

ha

n 9

0 %

of

the

op

era

tors

are

tra

ine

d f

or

mo

re t

ha

n o

ne

fu

nctio

n0

Op

era

tor

invo

lve

me

nt

Em

plo

ye

es p

ractice

, e

xh

ibit th

e in

itia

tive

, a

nd

ad

he

re

to th

e le

an

in

itia

tive

, o

rig

ina

tin

g p

rob

lem

-so

lvin

g a

nd

reso

lutio

n a

ctivitie

s in

div

idu

ally

an

d a

uto

no

mo

usly

.

(So

ba

nski, 2

00

8, p

. 2

01

)

ba

do

kg

oo

dve

ry g

oo

d0 0

Le

ad

ers

hip

in

vo

lve

me

nt

Ma

na

ge

r o

r S

up

erv

iso

rs s

pe

nd

sig

nific

an

t tim

e o

n

the

sh

op

flo

or

de

ve

lop

ing

te

am

le

ad

ers

an

d

em

plo

ye

es a

nd

dire

ctin

g a

nd

fa

cili

tatin

g d

aily

activitie

s. (S

ob

an

ski, 2

00

8, p

. 2

01

)

ba

do

kg

oo

dve

ry g

oo

d0

0.0

0

Po

ints

Co

mm

en

tsW

are

ho

use

An

aly

sis

1.0

EXECUTION

Ave

rag

e

Le

ve

l

CONCEPT

Ave

rag

e

Employee Involvement

2.2

To

pic

s

IST

-AU

FN

AH

ME

Them

en

feld

er

und

Ge

wic

htu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

ana

lyse

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.6:2

.2Em

ployee

Involvem

ent(Jackson

andJo

nes,

1996;S

oban

ski,2009)

147


nfid

en

tial

Na

me

of W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Co

ve

rag

e o

f sta

nd

ard

ize

d w

ork

Th

e c

ove

rag

e c

an

be

ch

ecke

d b

y a

skin

g if th

ere

are

cu

rren

t sta

nd

ard

ize

d w

ork

sh

ee

ts fo

r ea

ch

ma

jor

op

era

tion

/pro

ce

ss in

ea

ch

fun

ctio

n. S

ma

ller

sta

nd

ard

ize

d w

ork

cycle

len

gth

s in

cre

ase

pro

ce

ss

reso

lutio

n, b

ring

pro

ble

ms to

su

rface

faste

r, red

uce

ba

tch

siz

es, q

ue

uin

g, a

nd

WIP

. (So

ba

nski, 2

00

8, p

.

19

4)

ba

do

kg

oo

dve

ry g

oo

d0

Vis

ua

liza

tion

Sta

nd

ard

ize

d w

ork

sh

ee

ts h

as to

be

po

ste

d o

n th

e

sh

op

floo

r. (So

ba

nski, 2

00

8, p

. 19

4) T

he

y h

ave

to b

e

als

o c

om

pre

he

nsiv

e a

nd

su

pp

orte

d b

y v

isu

als

.

(Gra

up

p, 2

00

6, p

. 54

)

ba

do

kg

oo

dve

ry g

oo

d0

Qu

alific

atio

n

Em

plo

ye

e u

nd

ers

tan

din

g is

incre

ase

d b

y tra

inin

g a

nd

pa

rticip

atio

n in

co

ntin

uo

us im

pro

ve

me

nt o

f da

ily w

ork

activ

ities. (S

ob

an

ski, 2

00

8, p

. 21

5)

ba

do

kg

oo

dve

ry g

oo

d0

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

5S

sta

tus

5S

me

tho

do

log

y fo

r de

ve

lop

ing

a p

lace

for e

ve

ryth

ing

an

d h

avin

g e

ve

ryth

ing

in its

pla

ce

in th

e fa

cility

.

(So

ba

nski, 2

00

8, p

. 21

7)

ba

do

kg

oo

dve

ry g

oo

d0

Sta

bility

Th

e p

erc

en

t of a

ctu

al c

ycle

co

un

ts p

erfo

rme

d d

aily

ve

rsu

s d

ep

artm

en

t go

als

? A

re th

ey tra

cke

d a

nd

go

als

se

t? (S

ob

an

ski, 2

00

8, p

. 20

6)

ba

do

kg

oo

dve

ry g

oo

d0

Pro

du

ctiv

ity

Pro

du

ctiv

ity ra

tes a

re tra

cke

d a

nd

dis

pla

ye

d re

gu

larly

ve

rsu

s fa

cility

an

d d

ep

artm

en

tal g

oa

ls?

Th

e a

ctu

al

pro

du

ctiv

ity ra

tes v

ers

us d

ep

artm

en

tal a

nd

facility

go

als

, wh

ere

a h

igh

er ra

tio is

be

tter?

(So

ba

nski,

20

08

, p. 2

12

)

ba

do

kg

oo

dve

ry g

oo

d0

.00

2.3

EXECUTION CONCEPT

Ave

rag

e

Ave

rag

e

Standardized Work

Co

mm

en

tsW

are

ho

use

An

aly

sis

1.0

Po

ints

Le

ve

l

To

pic

s

IST

-AU

FN

AH

ME

Them

en

feld

er u

nd

Ge

wic

htu

ng

1 K

ontin

uie

rliche V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reife

nde T

hem

en

We

rtstro

mana

lyse

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.7:2.3

StandardizedWork

(Sobanski,2009;Graupp

andWrona,2006)

148

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Qu

alif

ica

tio

n

A q

ua

lific

atio

n m

atr

ix a

llow

s m

an

ag

em

en

t

to a

sse

ss e

mp

loye

e a

bili

tie

s a

t a

gla

nce

to

leve

l flo

w a

nd

ma

np

ow

er

pla

n w

ith

in a

nd

be

twe

en

ea

ch

fu

nctio

n in

th

e f

acili

ty.

(So

ba

nski, 2

00

8, p

. 2

00

)

ba

do

kg

oo

dve

ry g

oo

d0

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

0

0.0

0

Ave

rag

e

EXECUTION

Ave

rag

e

Wa

reh

ou

se

An

aly

sis

1.0

Le

ve

lP

oin

tsC

om

me

nts

To

pic

s

3.1

Overhead

CONCEPT

IST

-AU

FN

AH

ME

Them

en

feld

er

und

Ge

wic

htu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

ana

lyse

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.8:3

.1Overhead(Sob

anski,2009)

149


nfid

en

tial

Na

me

of W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Org

an

iza

tion

Tim

e w

ind

ow

s c

an

he

lp le

ve

l the

wo

rklo

ad

for

de

fine

d s

hip

pin

g tim

es. If th

ese

time

win

do

ws a

re

als

o a

va

ilab

le fo

r the

do

wn

stre

am

pro

ce

sse

s, th

e

ma

turity

is h

igh

er. (D

eh

da

ri et a

l, 20

12

)

ba

do

kg

oo

dve

ry g

oo

d0

Te

ch

nic

al E

qu

ipm

en

t:

- OG

op

era

tor h

as to

ha

nd

le th

e e

ntire

pro

ce

ss

ma

nu

ally

Te

ch

nic

al E

qu

ipm

en

t:

- OG

op

era

tor its

su

pp

orte

d b

y s

em

i-

au

tom

atic

eq

uip

me

nt (e

.

g. s

ca

nn

er)

Te

ch

nic

al E

qu

ipm

en

t:

- as le

ve

l 3

Te

ch

nic

al E

qu

ipm

en

t:

- OG

op

era

tor is

su

pp

orte

d b

y a

uto

ma

tic

eq

uip

me

nt (e

. g. R

FID

ga

te)

0

Vis

ua

liza

tion

Vis

ua

l co

ntro

ls c

an

he

lp g

ua

ran

tee

the

time

win

do

ws. V

isu

al c

on

trol m

ech

an

ism

s e

nh

an

ce

pro

ce

ss in

teg

rity a

nd

red

uce

wa

ste

by e

limin

atin

g

se

arc

hin

g a

nd

sta

biliz

ing

pro

ce

sse

s. (S

ob

an

ski,

20

08

, p.2

15

) (Fu

rma

ns, 2

01

2, p

.49

)

ba

do

kg

oo

dve

ry g

oo

d00

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

Tim

e w

ind

ow

ad

he

ren

ce

Tra

ckin

g th

e tim

e w

ind

ow

ad

he

ren

ce

info

rma

tion

illustra

tes th

e p

erfo

rma

nce

ve

rsu

s th

e e

xp

ecta

tion

s.

(So

ba

nski, 2

00

8, p

. 21

2)

ba

do

kg

oo

dve

ry g

oo

d0

Ba

lan

cin

g o

f co

mp

lete

sh

ipp

ing

pro

ce

sse

s

To

ba

lan

ce

the

wo

rklo

ad

, it is im

po

rtan

t to k

no

w th

e

wo

rklo

ad

an

d a

va

ilab

le m

an

ho

urs

. An

imp

rove

me

nt

of th

e b

ala

nce

is d

esire

d. (F

urm

an

s, 2

01

2, p

. 82

) b

ad

ok

go

od

ve

ry g

oo

d0

Le

ad

time

of th

e s

hip

pin

g p

rep

ara

tion

pro

ce

ss

Th

e le

ad

time

of th

e s

hip

pin

g a

nd

sh

ipp

ing

pre

pa

ratio

n s

ho

uld

be

me

asu

red

. If it is s

tab

le

red

uce

d fo

r a c

erta

in tim

e th

an

this

is a

go

od

ind

ica

tion

. (Fu

rma

ns, 2

01

2, p

. 79

)

ba

do

kg

oo

dve

ry g

oo

d0

Dis

pa

tch

erro

r rate

:

- failu

res c

au

se

d in

the

dis

pa

tch

are

a a

re

me

asu

red

Dis

pa

tch

erro

r rate

:

- sta

ble

ach

ieve

me

nt o

r

po

sitiv

e tre

nd

of fa

ilure

for

mo

re th

an

6 m

on

ths

Dis

pa

tch

erro

r rate

:

- sta

ble

ach

ieve

me

nt o

r

po

sitiv

e tre

nd

of fa

ilure

for

mo

re th

an

1 y

ea

r

Dis

pa

tch

erro

r rate

:

- sta

ble

ach

ieve

me

nt o

r

po

sitiv

e tre

nd

of fa

ilure

for m

ore

tha

n 2

ye

ars

0

Ha

nd

ling

ste

ps

- ha

nd

ling

ste

ps a

re

co

un

ted

Ha

nd

ling

ste

ps:

- red

uctio

n in

ha

nd

ling

ste

ps in

the

last 6

mo

nth

s

by o

ptim

izin

g m

ea

su

res

(if ne

ce

ssa

ry m

inim

um

is

rea

ch

ed

, sta

ble

ach

ieve

me

nt c

ou

nts

as

red

uctio

n)

Ha

nd

ling

ste

ps:

- red

uctio

n in

ha

nd

ling

ste

ps in

the

last 3

mo

nth

s

by o

ptim

izin

g m

ea

su

res

(if ne

ce

ssa

ry m

inim

um

is

rea

ch

ed

, sta

ble

ach

ieve

me

nt c

ou

nts

as

red

uctio

n)

Ha

nd

ling

ste

ps:

- red

uctio

n in

ha

nd

ling

ste

ps in

the

last m

on

th

by o

ptim

izin

g m

ea

su

res

(if ne

ce

ssa

ry m

inim

um

is

rea

ch

ed

, sta

ble

ach

ieve

me

nt c

ou

nts

as

red

uctio

n)

0

0.0

0

Outgoing Goods

Co

mm

en

tsL

eve

lP

oin

ts

To

pic

s

Ave

rag

e

CONCEPT

Ave

rag

e

EXECUTION

Wa

reh

ou

se

An

aly

sis

1.0

3.2

IS

T-A

UF

NA

HM

E

Them

en

feld

er u

nd

G

ew

ichtu

ng

1 K

ontin

uie

rlic

he V

erbesseru

ng

3 La

gerp

ro

zesse

2 Ü

bergreifende T

hem

en

We

rtstrom

ana

lyse

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.9:3.2

Outgoing

Goods

(Dehdariand

Schwab,2012;Sobanski,2009;Furm

ansand

Wl-

cek,2012)

150

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Pa

cka

ge

d g

oo

d:

- p

acka

ge

d g

oo

ds a

re a

va

ilab

le in

sh

ort

wa

lkin

g d

ista

nce

(<

5 m

ete

rs)

Pa

cka

ge

d g

oo

d:

- p

acka

ge

d g

oo

ds a

re a

va

ilab

le f

or

the

pa

cka

gin

g o

pe

rato

r in

op

era

tin

g d

ista

nce

(with

ou

t w

alk

ing

)

Pa

cka

ge

d g

oo

d:

- p

acka

ge

d g

oo

ds a

re d

eliv

ere

d in

th

e r

igh

t

se

qu

en

ce

(re

ga

rdin

g d

isp

atc

h s

eq

ue

nce

)

Pa

cka

ge

d g

oo

d:

- a

s le

ve

l 3

0

Pa

cka

gin

g m

ate

ria

l:

- o

pe

rato

r h

as p

acka

gin

g m

ate

ria

l in

wa

lkin

g d

ista

nce

- m

ate

ria

l m

ust

be

re

sto

cke

d b

y p

acka

gin

g

op

era

tor

him

se

lf (

inte

rru

ptio

n o

f w

ork

)

Pa

cka

gin

g m

ate

ria

l:

- O

pe

rato

r h

as p

acka

gin

g m

ate

ria

l in

wa

lkin

g d

ista

nce

- M

ate

ria

l is

re

sto

cke

d r

eg

ula

rly

Pa

cka

gin

g m

ate

ria

l:

- o

pe

rato

r h

as p

acka

gin

g m

ate

ria

l in

op

era

tin

g d

ista

nce

(w

ith

ou

t w

alk

ing

)

- m

ate

ria

l is

re

sto

cke

d r

eg

ula

rly

- o

pe

rato

r h

as p

ossib

ility

fo

r e

me

rge

ncy

ca

ll in

ca

se

ma

teria

l ru

ns o

ut

Pa

cka

gin

g m

ate

ria

l:

- a

s le

ve

l 3

- sta

gin

g o

f m

ate

ria

l is

gu

ara

nte

ed

by a

we

ll-w

ork

ing

, c

on

su

mp

tio

n-d

rive

n s

yste

m

(e.

g.

milk

run

)

0

Pa

cka

gin

g p

roce

ss:

- e

rgo

no

mic

re

qu

ire

me

nts

are

fu

lfill

ed

reg

ard

ing

pa

cka

gin

g w

ork

pla

ce

Pa

cka

gin

g p

roce

ss:

- p

roce

ss-o

rie

nte

d w

ork

pla

ce

de

sig

n/la

yo

ut:

ce

ntr

al a

nd

su

ffic

ien

t d

isp

osa

l o

f a

ll

de

vic

es /

to

ols

/ m

ach

ine

s /

IT

-su

pp

ort

Pa

cka

gin

g p

roce

ss:

- a

s le

ve

l 2

- fle

xib

le w

ork

pla

ce

de

sig

n -

-> w

ork

sp

lace

ca

n b

e u

se

d f

or

diffe

ren

t fu

nctio

ns

- sta

nd

ard

s f

or

pa

cka

gin

g p

roce

ss a

re

de

fin

ed

an

d c

om

mu

nic

ate

d

Pa

cka

gin

g p

roce

ss:

- o

pe

rato

rs p

roce

ssin

g a

pa

cka

gin

g u

nit

are

wo

rkin

g >

90

% o

f th

eir w

ork

tim

e

acco

rdin

g t

o s

tan

da

rdiz

ed

wo

rk0

Vis

ua

liza

tio

n:

- cle

ar

vis

ua

liza

tio

n o

f w

hic

h p

rod

ucts

mu

st

be

pa

cke

d t

og

eth

er

- sta

nd

ard

ize

d a

nd

vis

ua

lize

d p

lace

wh

ere

to p

ut

the

de

live

ry n

ote

Vis

ua

liza

tio

n:

- a

s le

ve

l 1

- cle

ar

vis

ua

liza

tio

n o

f w

hic

h c

on

sig

ne

d

ord

ers

sh

ou

ld b

e w

ork

ed

on

ne

xt

(FIF

O,

rush

ord

ers

)

Vis

ua

liza

tio

n:

- a

s le

ve

l 2

- e

asily

vis

ible

co

mp

lete

ne

ss c

he

ck (

e.

g.

by w

eig

ht)

Vis

ua

liza

tio

n:

- a

s le

ve

l 3

- vis

ua

liza

tio

n,

if p

acka

gin

g o

pe

rato

rs a

re

still

on

tim

e o

r if c

on

sig

nm

nt

op

era

tors

are

wo

rkin

g f

aste

r --

> f

lexib

le e

mp

loye

e

ch

an

ge

- b

ase

d o

n v

olu

me

ca

lcu

latio

n o

f o

rde

r

vis

ua

liza

tio

n o

f a

de

qu

ate

pa

cka

gin

g

0 00

.00

KP

IK

PI

KP

IK

PI

KP

I

Le

ad

tim

e o

f th

e p

acka

gin

g p

roce

ss:

- le

ad

tim

e o

f th

e p

acka

gin

g p

roce

ss is

me

asu

red

Le

ad

tim

e o

f th

e p

acka

gin

g p

roce

ss:

- p

ositiv

e t

ren

d o

r a

t sta

ble

ta

rge

t le

ve

l o

f

ave

rag

e le

ad

tim

e o

f a

ll p

ackin

g u

nits

sin

ce

min

. 6

mo

nth

s

Le

ad

tim

e o

f th

e p

acka

gin

g p

roce

ss:

- p

ositiv

e t

ren

d o

f a

ve

rag

e le

ad

tim

e s

ince

the

la

st

12

mo

nth

s o

r sta

ble

Le

ad

tim

e o

f th

e p

acka

gin

g p

roce

ss:

- p

ositiv

e t

ren

d o

f a

ve

rag

e le

ad

tim

e s

ince

the

la

st

24

mo

nth

s o

r o

n o

uts

tan

din

g le

ve

l0

Pa

cka

gin

g e

rro

r ra

te:

- fa

ilure

s c

au

se

d in

th

e p

acka

gin

g a

rea

are

me

asu

red

Pa

cka

gin

g e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f

failu

re f

or

mo

re t

ha

n 6

mo

nth

s

Pa

cka

gin

g e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f

failu

re f

or

mo

re t

ha

n 1

ye

ar

Pa

cka

gin

g e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f

failu

re f

or

mo

re t

ha

n 2

ye

ars

0

0.0

0

Wa

reh

ou

se

An

aly

sis

1.0

Co

mm

en

tsL

eve

lP

oin

ts

To

pic

s

EXECUTION

3.3

Ave

rag

e

CONCEPT

Ave

rag

e

Packaging

IST

-AU

FN

AH

ME

Them

en

feld

er

und

Ge

wic

htu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

ana

lyse

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.10:

3.3Pa

ckaging

151

C Appendix - Assessment Questionaire

Co

nfid

en

tial

Na

me

of W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Pic

kin

g p

roce

ss:

- all n

ece

ssa

ry M

AE

are

ava

ilab

le

su

fficie

ntly

- co

nsig

me

nt o

pe

rato

rs a

re s

up

po

rted

by

ma

nu

al m

ea

su

res (e

.g. p

icklis

t)

- pic

kin

g w

ork

pla

ce

s fu

lfill erg

on

om

ic

req

uire

me

nts

Pic

kin

g p

roce

ss:

- as le

ve

l 1

- pic

kin

g o

pe

rato

rs a

re s

up

po

rted

by

syste

ma

tic a

nd

au

tom

atic

me

asu

res (e

.g.

pic

k-b

y-s

ca

n)

- op

era

tor s

elf c

on

trol

- pic

kin

g w

ork

pla

ce

s a

re p

roce

ss o

rien

ted

Pic

kin

g p

roce

ss:

- as le

ve

l 2

- pic

kin

g o

pe

rato

rs a

re s

up

po

rted

by

syste

ma

tic a

nd

au

tom

atic

me

asu

res th

at

allo

w tw

o fre

e h

an

ds (e

.g. p

ick b

y

vo

ice

/ligh

t)

Pic

kin

g p

roce

ss:

- as le

ve

l 3

- op

era

tors

pro

ce

ssin

g a

pa

ckin

g u

nit a

re

wo

rkin

g >

90

% o

f the

ir wo

rk tim

e a

cco

rdin

g

to s

tan

da

rdiz

ed

wo

rk0

Org

an

iza

tion

al s

yste

m:

- de

fine

d p

roce

ss fo

r trea

tme

nt o

f fix d

ate

an

d ru

sh

ord

er e

xis

ts

Org

an

iza

tion

al s

yste

m:

- pic

kin

g te

ch

niq

ue

de

pe

nd

s o

n o

rde

r

freq

ue

ncy, o

rde

r vo

lum

e/s

ize

, sta

bility

of

go

od

s, p

acka

gin

g m

ate

rial

- syste

m c

alc

ula

tes o

ptim

al m

eth

od

- job

co

ntro

l follo

ws a

cle

arly

de

fine

d

stra

teg

y

Org

an

iza

tion

al s

yste

m:

- as le

ve

l 2

- pic

kin

g s

eq

ue

nce

is o

ptim

ize

d b

y s

yste

m

- ord

er d

isp

atc

h d

ep

en

ds o

n a

va

ilab

le

pic

kin

g p

erfo

rma

nce

, cu

rren

t wo

rkin

g

pro

gre

ss a

nd

cu

rren

t syste

m s

tatu

s

Org

an

iza

tion

al s

yste

m:

- as le

ve

l 3

- pic

kin

g ra

cks a

re fle

xib

le

- syste

ma

tic a

nd

exte

nsiv

e p

lan

nin

g

ap

pro

ach

to le

ve

l ca

pa

city

sh

ort te

rm in

ca

se

of flu

ctu

atio

n in

de

ma

nd

0

Info

rma

tion

syste

m:

- ma

nu

al o

rde

r en

try

Info

rma

tion

syste

m:

- failu

re p

reve

ntio

n b

y a

uto

ma

tic

reg

istra

tion

of p

ickin

g p

ositio

ns (e

.g. b

y

sca

nn

ing

)

Info

rma

tion

syste

m:

- au

tom

atic

al o

rde

r en

try

- vo

uch

erle

ss tra

nsfe

r of p

ickin

g o

rde

r

Info

rma

tion

syste

m:

- as le

ve

l 30

Vis

ua

liza

tion

:

- vis

ua

liza

tion

to s

up

po

rt ma

teria

l flow

syste

m (tra

nsp

ort, ta

kin

g, h

an

d-o

ve

r), e. g

.

pic

kin

g a

rea

s, …

Vis

ua

liza

tion

:

- vis

ua

liza

tion

to s

up

po

rt org

an

iza

tion

al

syste

m (o

rde

r se

qu

en

ce

, pic

kin

g a

mo

un

t,

inte

rna

l de

live

ry p

erfo

rma

nce

, ...)

Vis

ua

liza

tion

:

- as le

ve

l 2

Vis

ua

liza

tion

:

- as le

ve

l 300

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

Le

ad

time

of th

e p

ickin

g p

roce

ss:

- lea

d tim

e o

f the

pic

kin

g p

roce

ss is

me

asu

red

Le

ad

time

of th

e p

ickin

g p

roce

ss:

- po

sitiv

e tre

nd

or a

t sta

ble

targ

et le

ve

l of

ave

rag

e le

ad

time

of a

ll pa

cka

gin

g u

nits

sin

ce

min

. 6 m

on

ths

Le

ad

time

of th

e p

ickin

g p

roce

ss:

- po

sitiv

e tre

nd

of a

ve

rag

e le

ad

time

sin

ce

the

last 1

2 m

on

ths o

r sta

ble

Le

ad

time

of th

e p

ickin

g p

roce

ss:

- po

sitiv

e tre

nd

of a

ve

rag

e le

ad

time

sin

ce

the

last 2

4 m

on

ths o

r on

ou

tsta

nd

ing

leve

l0

Pic

kin

g e

rror ra

te:

- failu

res c

au

se

d in

the

pic

kin

g a

rea

are

me

asu

red

Pic

kin

g e

rror ra

te:

- sta

ble

ach

ieve

me

nt o

r po

sitiv

e tre

nd

of

failu

re fo

r mo

re th

an

6 m

on

ths

Pic

kin

g e

rror ra

te:

- sta

ble

ach

ieve

me

nt o

r po

sitiv

e tre

nd

of

failu

re fo

r mo

re th

an

1 y

ea

r

Pic

kin

g e

rror ra

te:

- sta

ble

ach

ieve

me

nt o

r po

sitiv

e tre

nd

of

failu

re fo

r mo

re th

an

2 y

ea

rs

0

0.0

0

3.4

To

pic

s

Wa

reh

ou

se

An

aly

sis

1.0

EXECUTION CONCEPT

Co

mm

en

tsP

oin

ts

Picking

Ave

rag

e

Ave

rag

e

Le

ve

l

IST

-AU

FN

AH

ME

Them

en

feld

er u

nd

Ge

wic

htu

ng

1 K

ontin

uie

rliche V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reife

nde T

hem

en

We

rtstro

mana

lyse

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.11:3.4

Picking

152

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Sto

rag

e t

ech

nic

/la

yo

ut:

- sa

fety

-re

late

d a

nd

ju

dic

ial g

uid

elin

es a

re

co

nsid

ere

d

Sto

rag

e t

ech

nic

/la

yo

ut:

- a

s le

ve

l 0

- sto

rag

e t

ech

niq

ue

de

pe

nd

s o

n g

oo

ds

(siz

e,

we

igh

t, p

acka

gin

g,

sta

bili

ty,

am

ou

nt,

…)

Sto

rag

e t

ech

nic

/la

yo

ut:

- a

s le

ve

l 1

- sto

rag

e t

ech

niq

ue

de

pe

nd

s o

n e

co

no

mic

facto

rs (

op

tim

al la

nd

an

d r

oo

m u

se

)

Sto

rag

e t

ech

nic

/la

yo

ut:

- a

s le

ve

l 2

- sto

rag

e t

ech

nic

is u

pd

ate

d m

in.

2x/y

ea

r

- a

uto

ma

tiza

tio

n le

ve

l fits

to

aim

ed

pro

fita

bili

ty a

nd

fle

xib

ility

(ca

n b

e

arg

um

en

ted

by w

are

ho

use

ma

na

ge

r)

Sto

rag

e t

ech

nic

/la

yo

ut:

- a

s le

ve

l 2

- sto

rag

e t

ech

nic

is u

pd

ate

d m

in.

6x/y

ea

r

- a

s le

ve

l 3

0

Sto

rag

e c

rite

ria

:

- fo

r sto

rag

e s

afe

ty-r

ela

ted

an

d ju

dic

ial

gu

ide

line

s a

re c

on

sid

ere

d (

esp

ecia

lly in

ca

se

of

ha

za

rdo

us m

ate

ria

ls)

Sto

rag

e c

rite

ria

:

- a

s le

ve

l 0

- te

ch

nic

al re

qu

ire

me

nts

are

co

nsid

ere

d

(op

tim

al vo

lum

e u

se

, e

qu

al lo

ad

, …

)

Sto

rag

e c

rite

ria

:

- a

s le

ve

l 1

- e

co

no

mic

fa

cts

are

co

nsid

ere

d (

inve

nto

ry

turn

ove

r, a

va

ilab

ility

, o

ptim

al d

ista

nce

s .

..)

Sto

rag

e c

rite

ria

:

- te

ch

nic

al a

nd

eco

no

mic

fa

cts

are

up

da

ted

min

. 2

x/y

ea

r

Sto

rag

e c

rite

ria

:

- te

ch

nic

al a

nd

eco

no

mic

fa

cts

are

up

tad

et

min

. 6

x/y

ea

r0

Inve

nto

ry m

an

ag

em

en

t:

- cle

ar

an

d s

yste

ma

tic a

dm

inis

tra

tio

n o

f a

ll

inve

nto

rie

s in

th

e w

are

ho

use

syste

m

Inve

nto

ry m

an

ag

em

en

t:

- a

s le

ve

l 1

- lit

tle

/no

tim

e w

ind

ow

be

twe

en

mo

vin

g

go

od

s a

nd

bo

okin

g p

roce

ss

Inve

nto

ry m

an

ag

em

en

t:

- a

s le

ve

l 2

- p

erm

an

en

t in

ve

nto

ry t

akin

g b

y h

igh

ly

de

ve

lop

ed

ED

V s

yste

ms

Inve

nto

ry m

an

ag

em

en

t:

- a

s s

tora

ge

pro

ce

ss d

oe

sn

't a

llow

an

y

inve

nto

ry d

evia

tio

ns

0

Vis

ua

liza

tio

n:

- b

in lo

ca

tio

ns a

re c

lea

rly v

isu

aliz

ed

(e

.g.

nu

mb

er,

sca

n c

od

e)

Vis

ua

liza

tio

n:

- a

s le

ve

l 1

- m

in/m

ax in

ve

nto

rie

s a

re v

isu

aliz

ed

(if

exis

tin

g)

Vis

ua

liza

tio

n:

- a

s le

ve

l 2

- in

ve

nto

rie

s a

nd

pe

rfo

rma

nce

of

the

wa

reh

ou

se

are

vis

ible

fo

r e

ve

ryo

ne

(e

. g

.

scre

en

with

cu

rre

nt

KP

Is)

Vis

ua

liza

tio

n:

- a

s le

ve

l 3

0 0 0

Fro

m-b

in t

ran

sfe

r:

Fro

m-b

in t

ran

sfe

r:

- T

ran

sfe

r to

co

nsig

nm

en

t a

rea

is

co

nsu

mp

tio

n d

rive

n (

Pu

ll)

Fro

m-b

in t

ran

sfe

r:

- a

s le

ve

l 2

Fro

m-b

in t

ran

sfe

r:

- a

s le

ve

l 3

0

0.0

0

KP

IK

PI

KP

IK

PI

KP

I

Le

ad

tim

e o

f th

e s

tora

ge

pro

ce

ss:

- le

ad

tim

e o

f th

e s

tora

ge

pro

ce

ss is

me

asu

red

Le

ad

tim

e o

f th

e s

tora

ge

pro

ce

ss:

- p

ositiv

e t

ren

d o

r a

t sta

ble

ta

rge

t le

ve

l o

f

ave

rag

e le

ad

tim

e o

f a

ll p

acka

gin

g u

nits

sin

ce

min

. 6

mo

nth

s

Le

ad

tim

e o

f th

e s

tora

ge

pro

ce

ss:

- p

ositiv

e t

ren

d o

f a

ve

rag

e le

ad

tim

e s

ince

the

la

st

12

mo

nth

s o

r sta

ble

Le

ad

tim

e o

f th

e s

tora

ge

pro

ce

ss:

- p

ositiv

e t

ren

d o

f a

ve

rag

e le

ad

tim

e s

ince

the

la

st

24

mo

nth

s o

r o

n o

uts

tan

din

g le

ve

l0

Sto

rag

e e

rro

r ra

te:

- fa

ilure

s c

au

se

d in

th

e a

rea

of

sto

rag

e a

re

me

asu

red

Sto

rag

e e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f

failu

re f

or

mo

re t

ha

n 6

mo

nth

s

Sto

rag

e e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f

failu

re f

or

mo

re t

ha

n 1

ye

ar

Sto

rag

e e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f

failu

re f

or

mo

re t

ha

n 2

ye

ars

0

0.0

0

Ave

rag

e

Ave

rag

e

Co

mm

en

tsL

eve

lP

oin

ts

In c

ase

sto

rag

e is s

ep

era

ted

fro

m c

on

sig

nm

en

t a

rea

Storage

3.5

To

pic

s

Wa

reh

ou

se

An

aly

sis

1.0

EXECUTIONCONCEPT

IST

-AU

FN

AH

ME

Them

en

feld

er

und

Ge

wic

htu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

ana

lyse

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.12:

3.5Storage

153


nfid

en

tial

Na

me

of W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

No

.0

12

34

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

sS

tan

da

rds

Sta

nd

ard

s

Org

an

iza

tion

Tim

e w

ind

ow

s c

an

he

lp le

ve

l the

wo

rklo

ad

for

de

fine

d re

ce

ivin

g tim

es. If th

ese

time

win

do

ws a

re

als

o a

va

ilab

le fo

r the

up

stre

am

pro

ce

sse

s, th

e

ma

turity

is h

igh

er. (D

eh

da

ri et a

l, 20

12

)

ba

do

kg

oo

dve

ry g

oo

d0

En

try / b

oo

kin

g:

- two

-leve

l En

try:

the

po

stin

g o

f the

go

od

s

to th

e in

ve

nto

ry a

nd

the

en

try o

f the

inco

min

g

go

od

s a

re d

on

e

se

pe

rate

ly

En

try / b

oo

kin

g:

- as le

ve

l 1

- en

try in

the

syste

m is

do

ne

no

t mo

re th

an

on

e

da

y a

fter g

oo

ds a

re

rece

ive

d

En

try / b

oo

kin

g:

- sin

gle

-leve

l En

try:

the

po

stin

g o

f the

go

od

s

to th

e in

ve

nto

ry is

do

ne

tog

eth

er w

ith th

e e

ntry

of

the

inco

min

g g

oo

ds (a

t

lea

st in

terim

bo

okin

g o

f

Avis

-Info

rma

tion

to

sto

rag

e s

yste

m =

Ein

bu

ch

un

g u

nte

r

Vo

rbe

ha

lt)

- en

try in

the

syste

m is

En

try / b

oo

kin

g:

- as le

ve

l 3

- en

try in

the

syste

m is

do

ne

ne

arly

at th

e s

am

e

time

as g

oo

ds a

re

rece

ive

d (n

ot m

ore

tha

n 2

ho

urs

late

r)

0

Insp

ectio

n:

- insp

ectio

n in

ten

sity

de

pe

nd

s o

n fa

ilure

rate

of

su

pp

lier o

r co

mp

ara

ble

crite

ria

Insp

ectio

n:

- su

pp

lier d

eliv

er a

lrea

dy

filled

ou

t an

d s

ign

ed

co

ntro

l do

cu

me

nts

with

the

ir go

od

s

Insp

ectio

n:

- free

ticke

t (Fre

ipa

ss) fo

r

au

dite

d s

up

plie

rs (e

. g.

skip

-qu

ota

afte

r a d

efin

ed

sch

em

e)

Insp

ectio

n:

- as le

ve

l 3

0

Vis

ua

liza

tion

Vis

ua

l co

ntro

ls c

an

he

lp g

ua

ran

tee

the

time

win

do

ws. V

isu

al c

on

trol m

ech

an

ism

s e

nh

an

ce

pro

ce

ss in

teg

rity a

nd

red

uce

wa

ste

by e

limin

atin

g

se

arc

hin

g a

nd

sta

biliz

ing

pro

ce

sse

s. (S

ob

an

ski,

20

08

, p.2

15

) (Fu

rma

ns, 2

01

2, p

.49

)

ba

do

kg

oo

dve

ry g

oo

d00

0.0

0

CONCEPT

Ave

rag

e

Incoming Goods

3.6

Co

mm

en

ts

To

pic

s

Wa

reh

ou

se

An

aly

sis

1.0

Le

ve

lP

oin

ts

IST

-AU

FN

AH

ME

Them

en

feld

er u

nd

Ge

wic

htu

ng

1 K

ontin

uie

rliche V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reife

nde T

hem

en

We

rtstro

mana

lyse

Mo

de

rato

r1, M

od

era

tor2

1M

M/D

D/Y

YY

Y

FigureC.13:3.6

Incoming

Goods

Concept

(Dehdari

andSchw

ab,2012;

Furmans

andW

lcek,2012)

154

Co

nfid

en

tia

lN

am

e o

f W

are

ho

use

Wa

reh

ou

se

An

aly

sis

1.0

KP

IK

PI

KP

IK

PI

KP

I

Tim

e w

ind

ow

ad

he

ren

ce

Tra

ckin

g th

e tim

e w

ind

ow

ad

he

ren

ce

in

form

atio

n

illu

str

ate

s th

e p

erf

orm

an

ce

ve

rsu

s th

e e

xp

ecta

tio

ns.

(So

ba

nski, 2

00

8, p

. 2

12

)

ba

do

kg

oo

dve

ry g

oo

d0

Ba

lan

cin

g o

f co

mp

lete

in

co

min

g p

roce

sse

s

To

ba

lan

ce

th

e w

ork

loa

d, it is im

po

rta

nt to

kn

ow

th

e

wo

rklo

ad

an

d a

va

ilab

le m

an

ho

urs

. A

n im

pro

ve

me

nt

of

the

ba

lan

ce

is d

esire

d. (F

urm

an

s, 2

01

2, p

. 8

2)

ba

do

kg

oo

dve

ry g

oo

d0

3.6

Le

ad

tim

e o

f th

e in

co

min

g p

roce

ss

Th

e le

ad

tim

e o

f th

e in

co

min

g a

nd

in

co

min

g h

an

dlin

g

sh

ou

ld b

e m

ea

su

red

. If

it is

sta

bile

re

du

ce

d f

or

a

ce

rta

in tim

e th

an

th

is is a

go

od

in

dic

atio

n. (F

urm

an

s,

20

12

, p

. 7

9)

ba

do

kg

oo

dve

ry g

oo

d0

Re

ce

ivin

g e

rro

r ra

te:

- fa

ilure

s c

au

se

d in

th

e

are

a o

f in

co

min

g g

oo

ds

are

me

asu

red

Re

ce

ivin

g e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f fa

ilure

for

mo

re t

ha

n 6

mo

nth

s

Re

ce

ivin

g e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f fa

ilure

for

mo

re t

ha

n 1

ye

ar

Re

ce

ivin

g e

rro

r ra

te:

- sta

ble

ach

ieve

me

nt

or

po

sitiv

e t

ren

d o

f fa

ilure

for

mo

re t

ha

n 2

ye

ars

0

Ha

nd

ling

ste

ps

- h

an

dlin

g s

tep

s a

re

co

un

ted

Ha

nd

ling

ste

ps:

- re

du

ctio

n o

f h

an

dlin

g

ste

ps in

th

e la

st

6

mo

nth

s b

y o

ptim

izin

g

me

asu

res

(if

ne

ce

ssa

ry m

inim

um

is

rea

ch

ed

, sta

ble

ach

ieve

me

nt

co

un

ts a

s

red

uctio

n)

Ha

nd

ling

ste

ps:

- re

du

ctio

n o

f h

an

dlin

g

ste

ps in

th

e la

st

3 m

on

ths

by o

ptim

izin

g m

ea

su

res

(if

ne

ce

ssa

ry m

inim

um

is

rea

ch

ed

, sta

ble

ach

ieve

me

nt

co

un

ts a

s

red

uctio

n)

Ha

nd

ling

ste

ps:

- re

du

ctio

n o

f h

an

dlin

g

ste

ps in

th

e la

st

mo

nth

by

op

tim

izin

g m

ea

su

res

(if

ne

ce

ssa

ry m

inim

um

is

rea

ch

ed

, sta

ble

ach

ieve

me

nt

co

un

ts a

s

red

uctio

n)

0

0.0

0

EXECUTION

Ave

rag

e

Incoming Goods

IST

-AU

FN

AH

ME

Them

en

feld

er

und

Ge

wic

htu

ng

1 K

ontinu

ierlic

he V

erb

esseru

ng

3 L

agerp

rozesse

2 Ü

berg

reifende T

hem

en

We

rtstr

om

ana

lyse

Mo

de

rato

r1,

Mo

de

rato

r21

MM

/DD

/YY

YY

Figu

reC.14:

3.6IncomingGoo

dsEx

cecu

tion(Furman

san

dW

lcek,2

012;

Soba

nski,2

009)

155

C Appendix - Assessment Questionaire

156

D Appendix - WarehouseExcellence GroupAssessment Results

157

D Appendix - Warehouse Excellence Group Assessment Results

2010C

riteriaM

inM

axA

verageW

1W

2W

3W

4W

5W

6W

7W

8W

9W

10W

11W

12W

13W

14W

15W

161.1 System

-CIP C

onceptB

usiness Requirem

ents0

40.750

02

00

04

00

20

20

20

00

Value S

tream P

lanning0

10.375

01

10

01

10

00

11

00

00


ent Activities

01

0.1250

10

00

10

00

00

00

00

0D

efinition of Target Conditions

02

0.1250

20

00

00

00

00

00

00

0S

ystem-C

IP P

rojects0

10.188

01

00

01

00

01

00

00

00

Point-C

IP A

reas0

41.125

04

12

01

10

11

10

11

40

1.1 System-C

IP ExecutionTarget D

erivation0

10.688

01

11

01

11

11

10

10

10

System

-CIP

Cycles

01

0.2500

10

00

10

01

01

00

00

0Im

provement Focus

02

0.9380

02

11

11

11

12

01

20

1Leadership Involvem

ent0

20.313

01

00

02

00

00

20

00

00

VS

M-Q

uality0

40.750

01

40

04

10

00

11

00

00

Target Achievem

ent0

20.125

00

00

00

00

00

00

20

00

1.2 Point-CIP C

onceptTarget C

ondition0

20.438

02

10

01

10

01

10

00

00

Quick R

eaction System

04

0.4380

00

00

20

01

04

00

00

0R

egular Com

munication

03

1.3751

21

11

21

02

12

01

33

1S

ustainable Problem

Solving

02

0.4380

00

00

20

00

02

01

02

0P

rocess Confirm

ation0

20.313

00

00

01

00

10

00

01

20

1.2 Point-CIP Execution

KP

I-Effect

03

0.1880

00

00

00

00

03

00

00

0Q

uality of Problem

Solving

02

0.2500

00

00

20

00

02

00

00

0Sum

of Reached M

aturity Level1

1911

52

277

210

625

29

712

2Sum

of Average

9.188Standard D

eviation8.109

Coefficient of Variation

88.266

TableD.1:B

LWA

resultsper

warehouse:

Warehouse

Excellencegroup

2010

158

2011

Crit

eria

Min

Max

Aver

age

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

W13

W14

W15

W16

1.1

Syst

em-C

IP C

once

ptB

usin

ess

Req

uire

men

ts1

42.

000

14

21

24

11

41

11

42

21

Val

ue S

tream

Pla

nnin

g0

41.

875

12

22

34

11

32

31

10

31

Iden

tific

atio

n of

Impr

ovem

ent A

ctiv

ities

04

1.37

52

21

14

20

04

01

00

14

0D

efin

ition

of T

arge

t Con

ditio

ns0

31.

438

13

12

12

22

12

11

10

21

Sys

tem

-CIP

Pro

ject

s0

21.

375

12

10

12

21

12

21

20

22

Poi

nt-C

IP A

reas

14

2.12

52

42

22

32

12

21

12

24

21.

1 Sy

stem

-CIP

Exe

cutio

nTa

rget

Der

ivat

ion

14

1.37

51

41

11

11

14

11

11

11

1S

yste

m-C

IP C

ycle

s0

21.

000

12

11

11

11

11

10

10

12

Impr

ovem

ent F

ocus

14

2.56

32

22

43

41

12

42

24

24

2Le

ader

ship

Invo

lvem

ent

04

2.18

81

20

04

42

24

24

02

04

4V

SM

-Qua

lity

14

1.93

81

14

11

41

14

11

11

14

4Ta

rget

Ach

ieve

men

t0

31.

688

11

03

21

21

32

13

30

22

1.2

Poin

t-CIP

Con

cept

Targ

et C

ondi

tion

12

1.43

81

21

11

12

21

21

12

12

2Q

uick

Rea

ctio

n S

yste

m0

43.

250

42

14

44

14

40

44

44

44

Reg

ular

Com

mun

icat

ion

14

2.81

34

21

43

23

23

42

32

33

4S

usta

inab

le P

robl

em S

olvi

ng0

31.

313

02

03

33

10

00

22

10

22

Pro

cess

Con

firm

atio

n0

21.

063

11

21

02

01

11

11

01

22

1.2

Poin

t-CIP

Exe

cutio

nK

PI-E

ffect

03

1.56

30

10

22

13

13

21

31

02

3Q

ualit

y of

Pro

blem

Sol

ving

02

0.43

80

10

20

20

00

00

11

00

0Su

m o

f Rea

ched

Mat

urity

Lev

el25

4022

3538

4726

2345

2930

2733

1848

39Su

m o

f Ave

rage

32.8

13St

anda

rd D

evia

tion

9.30

4C

oeffi

cien

t of V

aria

tion

28.3

55

TableD.2:B

LWA

results

perwareh

ouse:Wareh

ouse

Excelle

ncegrou

p2011

159

D Appendix - Warehouse Excellence Group Assessment Results

160

E Appendix - WarehouseExcellence Group KPR

161

E Appendix - Warehouse Excellence Group KPR

Description

2010Lean-Index

2011 Lean-Index

Delta

Slope2010

Average2011

AverageJan10

Feb10

Mar

10Apr10

May

10Jun10

Jul10

Aug10

Sep10

Oct

10N

ov10

Dec

10…

W1

1.00025.000

21.0000.002

N/A

110.029N

/AN

/AN

/AN

/AN

/AN

/AN

/AN

/AN

/AN

/AN

/AN

/A…

W2

19.00040.000

20.0000.023

126.279136.333

100.000119.359

118.073126.857

129.741124.416

125.169120.653

135.050135.098

143.011137.917

…W

311.000

22.0007.000

0.018100.522

106.876100.000

103.54398.057

94.107108.449

103.961102.367

105.83693.689

95.86698.265

102.120…

W4

5.00035.000

30.0000.013

103.876110.017

100.00094.208

112.149117.247

104.018105.892

98.84498.199

104.154104.867

107.94198.990

…W

52.000

38.00036.000

0.076107.323

136.543100.000

103.012110.100

103.78998.115

99.963104.624

104.005114.530

111.794112.779

125.158…

W6

27.00047.000

16.0000.055

109.987135.913

100.000110.879

103.724116.915

111.403109.172

107.776119.968

103.406105.370

104.260126.971

…W

77.000

26.00019.000

0.010109.088

114.664100.000

105.941110.839

120.476109.851

109.957109.957

109.957109.957

108.294114.353

99.478…

W8

2.00023.000

21.0000.045

92.803111.874

100.00089.745

90.28489.599

92.32196.060

86.61192.491

101.04399.378

93.10183.008

…W

910.000

45.00036.000

0.047100.000

123.865100.000

100.000100.000

100.000100.000

100.000100.000

100.000100.000

100.000100.000

100.000…

W10

6.00029.000

23.0000.011

104.618106.438

100.00095.059

101.267103.184

110.584110.306

101.609113.607

110.030101.730

109.01999.019

…W

1125.000

30.0003.000

0.03596.724

107.438100.000

98.65685.541

98.03799.141

97.29992.516

77.927100.022

98.020114.107

99.417…

W12

2.00027.000

25.000-0.021

98.54390.645

100.00095.678

95.67899.804

103.733100.982

104.322103.340

96.071100.000

98.03584.872

…W

139.000

33.00023.000

0.01399.081

104.704100.000

103.166104.127

98.78397.565

90.23091.251

99.524100.145

102.789102.737

98.650…

W14

7.00018.000

10.0000.214

108.948194.287

100.000102.511

106.540104.389

100.880107.706

99.690135.415

119.667117.291

110.206103.083

…W

1512.000

48.00038.000

0.00197.827

100.702100.000

95.074106.828

97.519107.210

106.90984.092

79.620107.510

104.368106.904

77.891…

W16

2.00039.000

37.0000.001

104.353105.146

100.000106.149

107.574103.754

108.967100.951

102.366113.170

105.651106.348

107.72989.572

…Average

9.18832.813

22.8130.035

103.998118.467

100.000101.532

103.385104.964

105.465104.254

100.746104.914

106.728106.081

108.163101.743

…StandardD

eviation8.109

9.304

TableE.1:W

arehouseExcellence

Group

KPR

development

2010

162

Des

crip

tion

…Ja

n11

Feb

11M

ar 11Ap

r11

May 11

Jun

11Ju

l11

Aug

11Se

p11

Oct 11

Nov 11

Dec 11

W1

…10

0.00

011

3.14

510

5.99

611

3.92

010

7.36

212

3.23

410

3.03

212

1.91

311

7.06

810

5.09

010

2.78

210

6.80

6W

2…

134.

006

153.

294

151.

558

158.

436

144.

674

139.

184

117.

316

113.

959

126.

342

115.

271

157.

896

124.

063

W3

…96

.552

102.

014

102.

683

105.

324

107.

971

102.

820

105.

616

116.

740

107.

548

108.

633

109.

956

116.

657

W4

…10

6.25

210

7.76

711

3.66

110

5.93

911

4.81

510

7.41

211

2.16

111

1.05

910

8.70

910

6.30

111

0.72

511

5.40

5W

5…

127.

533

130.

672

134.

933

129.

112

127.

030

118.

556

140.

215

142.

221

143.

088

143.

241

150.

185

151.

728

W6

…13

2.19

813

5.97

514

2.49

313

9.10

113

7.18

814

2.77

113

4.47

012

6.13

313

9.25

413

1.76

313

1.00

413

8.60

4W

7…

113.

997

120.

000

111.

113

116.

220

122.

342

113.

416

117.

804

111.

965

108.

123

112.

420

115.

502

113.

070

W8

…10

3.99

810

2.26

411

3.34

710

1.86

110

4.89

211

9.48

510

3.78

511

3.84

811

8.88

312

2.88

911

9.41

111

7.82

9W

9…

128.

902

138.

158

132.

555

117.

495

124.

410

122.

193

107.

952

108.

377

109.

437

131.

758

134.

954

130.

186

W10

…99

.092

97.3

3699

.952

101.

364

107.

231

106.

816

114.

855

114.

352

108.

899

108.

464

111.

428

107.

466

W11

…95

.066

93.5

0997

.092

98.7

1985

.117

116.

911

114.

831

112.

400

120.

904

133.

292

117.

118

104.

295

W12

…89

.980

90.7

6692

.338

101.

965

96.4

6487

.220

86.1

6394

.636

92.0

1086

.745

90.0

2279

.434

W13

…10

4.05

110

0.32

210

5.79

210

4.79

210

0.77

010

5.22

110

5.08

810

7.11

410

2.28

410

4.44

210

6.18

211

0.39

1W

14…

121.

168

126.

660

117.

900

195.

811

219.

883

221.

813

186.

839

337.

651

215.

681

209.

188

211.

305

167.

545

W15

…10

1.12

610

5.54

710

3.66

094

.869

111.

113

102.

375

85.3

5782

.176

121.

011

107.

383

116.

488

77.3

24W

16…

102.

370

101.

258

108.

785

92.1

0211

2.67

410

8.82

110

5.43

510

4.78

211

5.26

911

0.48

110

5.38

894

.390

Aver

age

…10

9.76

811

3.66

811

4.61

611

7.31

412

0.24

612

1.14

011

5.05

712

6.20

812

2.15

712

1.08

512

4.39

711

5.94

9

TableE.2:

Wareh

ouse

Excelle

nceKPR

developm

ent2011

163

E Appendix - Warehouse Excellence Group KPR

164

F Appendix - Control GroupAssessment Results

165

F Appendix - Control Group Assessment Results

2010C

riteriaM

inM

axA

verageC

1C

2C

3C

4C

5C

6C

7C

8C

9C

10C

11C

12C

13C

14C

15C

16C

17C

18C

19C

20C

21C

22C

23C

24C

25C

26C

27C

28…

1.1 System-C

IP Concept

Business Requirem

ents0

20.286

00

01

10

12

00

00

00

00

00

00

00

00

00

21

…Value Stream

Planning0

10.036

10

00

00

00

00

00

00

00

00

00

00

00

00

00


ent Activities0

00.000

00

00

00

00

00

00

00

00

00

00

00

00

00

00

…D


02

0.2500

00

20

21

00

00

00

00

00

00

00

00

00

01

1…

System-C

IP Projects0

00.000

00

00

00

00

00

00

00

00

00

00

00

00

00

00

…Point-C

IP Areas0

30.429

31

01

12

30

00

00

00

00

00

00

00

00

00

10

…1.1 System

-CIP Execution

Target Derivation

04

0.4640

01

11

40

40

00

00

00

00

00

00

00

00

01

1…

System-C

IP Cycles

00

0.0000

00

00

00

00

00

00

00

00

00

00

00

00

00

0…

Improvem

ent Focus0

40.714

20

34

41

01

00

00

00

00

00

00

00

00

00

23

…Leadership Involvem

ent0

00.000

00

00

00

00

00

00

00

00

00

00

00

00

00

00

…VSM

-Quality

00

0.0000

00

00

00

00

00

00

00

00

00

00

00

00

00

0…

Target Achievement

00

0.0000

00

00

00

00

00

00

00

00

00

00

00

00

00

0…

1.2 Point-CIP C

onceptTarget C

ondition0

20.357

20

02

20

10

00

00

00

00

00

00

00

00

00

21

…Q

uick Reaction System

02

0.2502

00

22

01

00

00

00

00

00

00

00

00

00

00

0…

Regular C

omm

unication0

40.750

33

01

24

00

00

04

00

00

00

00

00

00

00

13

…Sustainable Problem

Solving0

40.750

33

00

00

40

00

04

00

00

00

00

00

00

00

43

…Process C

onfirmation

02

0.1792

10

00

01

00

00

00

00

00

00

00

00

00

01

0…


KPI-Effect0

20.250

00

22

20

10

00

00

00

00

00

00

00

00

00

00

…Q

uality of Problem Solving

04

0.7502

21

33

31

00

00

00

00

00

00

00

00

00

02

4…

Sum of R

eached Maturity Level

2010

719

1816

147

00

08

00

00

00

00

00

00

00

1717

...Sum

of Average C

1-C56

5.464Standard D

eviation of Sum C

1-C56

7.488C

oefficient of Variation C1-C

56137.028

TableF.1:A

ssessment

resultsper

warehouse:

controlgroup2010

C1-C

28

166

2010

Crit

eria

…C

29C

30C

31C

32C

33C

34C

35C

36C

37C

38C

39C

40C

41C

42C

43C

44C

45C

46C

47C

48C

49C

50C

51C

52C

53C

54C

55C

561.

1 Sy

stem

-CIP

Con

cept

Busi

ness

Req

uire

men

ts…

02

20

10

02

21

20

02

22

10

22

00

00

20

20

Valu

e St

ream

Pla

nnin

g…

00

00

20

00

01

10

00

00

00

10

00

00

00

10

Iden

tific

atio

n of

Impr

ovem

ent A

ctiv

ities

…0

00

00

00

00

00

00

00

00

04

00

00

00

00

0D

efin

ition

of T

arge

t Con

ditio

ns…

20

02

00

04

00

00

10

02

11

04

00

10

00

00

Syst

em-C

IP P

roje

cts

…0

00

00

00

00

40

00

00

00

00

00

00

00

00

0Po

int-C

IP A

reas

…0

00

01

00

30

20

21

00

01

11

31

03

00

02

11.

1 Sy

stem

-CIP

Exe

cutio

nTa

rget

Der

ivat

ion

…0

10

00

00

10

00

01

00

10

00

41

01

00

00

0Sy

stem

-CIP

Cyc

les

…0

00

01

00

00

10

00

00

00

00

00

00

00

00

0Im

prov

emen

t Foc

us…

02

04

44

02

42

41

04

42

23

12

20

00

00

01

Lead

ersh

ip In

volv

emen

t…

00

00

00

00

00

00

00

00

00

10

00

00

00

10

VSM

-Qua

lity

…0

00

00

00

00

00

00

00

00

00

00

00

00

01

0Ta

rget

Ach

ieve

men

t…

00

00

10

00

01

00

00

00

00

00

00

00

00

00

1.2

Poin

t-CIP

Con

cept

Targ

et C

ondi

tion

…1

20

10

11

21

21

22

11

11

10

02

00

02

00

0Q

uick

Rea

ctio

n Sy

stem

…0

00

00

20

22

02

02

22

00

02

40

04

00

00

0R

egul

ar C

omm

unic

atio

n…

24

00

30

20

43

11

11

11

00

00

40

10

00

21

Sust

aina

ble

Prob

lem

Sol

ving

…4

30

01

00

20

00

14

00

03

00

40

04

03

04

3Pr

oces

s C

onfir

mat

ion

…1

00

02

00

00

20

02

00

10

22

00

00

00

00

01.

2 Po

int-C

IP E

xecu

tion

KPI-E

ffect

…0

00

04

00

00

20

00

00

01

11

11

00

00

00

0Q

ualit

y of

Pro

blem

Sol

ving

…4

40

10

14

11

21

03

11

12

01

41

02

03

04

1Su

m o

f Rea

ched

Mat

urity

Lev

el…

1418

28

208

719

1423

127

1711

1111

129

1628

120

160

100

177

TableF.2:

Assessm

entresults

perwareh

ouse:controlg

roup

2010

C29-C

56

167


2011C

riteriaM

inM

axA

verageC

1C

2C

3C

4C

5C

6C

7C

8C

9C

10C

11C

12C

13C

14C

15C

16C

17C

18C

19C

20C

21C

22C

23C

24C

25C

26C

27C

28…

1.1 System-C

IP Concept

Business Requirem

ents0

30.821

10

21

32

12

00

00

00

00

00

00

00

00

00

11

…Value Stream

Planning0

40.482

10

03

40

00

00

00

00

00

00

00

00

00

00

00


ent Activities0

40.339

10

04

40

00

00

00

00

00

00

00

00

00

00

00

…D


04

0.7322

01

44

11

00

10

00

00

00

00

00

00

00

00

0…

System-C

IP Projects0

40.696

00

03

40

00

00

00

00

00

00

00

00

00

00

00

…Point-C

IP Areas0

30.786

30

02

32

30

03

00

00

00

00

00

00

00

00

00

…1.1 System

-CIP Execution

Target Derivation

04

0.3390

00

44

00

00

10

00

00

00

00

00

00

00

01

0…

System-C

IP Cycles

04

0.2860

00

11

00

00

00

00

00

00

00

00

00

00

00

0…

Improvem

ent Focus0

40.893

30

03

11

01

01

00

00

00

00

00

00

00

00

40

…Leadership Involvem

ent0

40.446

00

01

40

00

00

00

00

00

00

00

00

00

00

00

…VSM

-Quality

04

0.3042

00

04

00

00

00

00

00

00

00

00

00

00

00

0…

Target Achievement

02

0.0710

00

11

00

00

00

00

00

00

00

00

00

00

00

0…

1.2 Point-CIP C

onceptTarget C

ondition0

40.839

10

23

41

10

02

00

00

00

00

00

00

00

00

01

…Q


04

0.9460

02

44

41

40

40

20

00

00

00

00

00

00

00

0…

Regular C

omm

unication0

41.143

13

04

14

04

01

04

00

00

00

00

00

00

00

00

…Sustainable Problem

Solving0

41.411

20

04

43

44

00

01

00

00

00

00

00

00

00

24

…Process C

onfirmation

02

0.4461

01

11

11

00

10

10

00

00

00

00

00

00

00

0…


KPI-effect0

40.661

30

22

12

10

00

00

00

00

00

00

00

00

00

00

…Q

uality of problem solving

04

1.1793

03

44

31

10

10

30

00

00

00

00

00

00

01

0…

Sum of R

eached Maturity Level

243

1349

5624

1416

015

011

00

00

00

00

00

00

00

96

...Sum

of Average C

1-C56

12.821Standard D

eviation of Sum C

1-C56

13.945C

oefficient of Variation C1-C

56108.767

TableF.3:A

ssessment

resultsper

warehouse:

controlgroup2011

C1-C

28

168

2011

Crit

eria

…C

29C

30C

31C

32C

33C

34C

35C

36C

37C

38C

39C

40C

41C

42C

43C

44C

45C

46C

47C

48C

49C

50C

51C

52C

53C

54C

55C

561.

1 Sy

stem

-CIP

Con

cept

Busi

ness

Req

uire

men

ts…

11

00

12

21

21

21

22

21

11

03

10

31

00

01

Valu

e St

ream

Pla

nnin

g…

00

00

21

00

10

11

01

12

00

24

10

10

00

10

Iden

tific

atio

n of

Impr

ovem

ent A

ctiv

ities

…0

00

01

00

00

00

00

00

40

00

21

02

00

00

0D

efin

ition

of T

arge

t Con

ditio

ns…

00

00

01

00

10

21

12

24

11

04

20

00

00

41

Syst

em-C

IP P

roje

cts

…0

00

00

40

04

04

00

44

40

00

30

03

00

02

0Po

int-C

IP A

reas

…0

00

02

00

30

21

13

22

00

01

31

03

00

21

11.

1 Sy

stem

-CIP

Exe

cutio

nTa

rget

Der

ivat

ion

…0

10

01

00

00

00

01

00

11

00

40

00

00

00

0Sy

stem

-CIP

Cyc

les

…0

00

00

10

01

01

00

11

10

00

10

03

00

04

0Im

prov

emen

t Foc

us…

02

11

14

00

42

41

04

41

40

01

10

00

00

10

Lead

ersh

ip In

volv

emen

t…

00

00

40

00

10

40

00

04

00

14

00

10

00

10

VSM

-Qua

lity

…0

00

00

10

01

01

00

11

00

00

40

01

00

01

0Ta

rget

Ach

ieve

men

t…

00

00

00

00

00

00

00

00

00

00

00

00

00

20

1.2

Poin

t-CIP

Con

cept

Targ

et C

ondi

tion

…1

21

01

11

21

21

11

11

12

00

42

02

01

10

2Q

uick

Rea

ctio

n Sy

stem

…0

02

00

20

02

02

00

02

42

00

40

02

00

02

4R

egul

ar C

omm

unic

atio

n…

04

00

01

24

31

22

41

11

00

14

10

30

04

30

Sust

aina

ble

Prob

lem

Sol

ving

…4

30

03

00

40

30

24

00

44

00

44

02

02

13

4Pr

oces

s C

onfir

mat

ion

…0

00

21

00

10

20

12

00

10

00

21

02

00

02

01.

2 Po

int-C

IP E

xecu

tion

KPI-E

ffect

…0

00

44

10

11

21

10

22

00

00

11

02

00

03

0Q

ualit

y of

Pro

blem

Sol

ving

…0

42

21

14

21

01

01

11

01

00

41

42

02

04

3Su

m o

f Rea

ched

Mat

urity

Lev

el…

617

69

2220

918

2315

2712

1922

2433

162

556

174

321

58

3416

TableF.4:

Assessm

entresults

perwareh

ouse:controlg

roup

2011

C1-C28

169


170

G Appendix - Control GroupKPR

171

G Appendix - Control Group KPR

Description

2010Lean-Index

2011 Lean-Index

Delta

Slope2010

Average

2011A

verageJan10

Feb10

Mar

10A

pr10

May

10Jun10

Jul10

Aug

10Sep10

Oct

10N

ov10

Dec

10…

C1

20.00024.000

4.0000.015

90.00093.750

100.00080.000

80.00075.000

75.00085.000

105.00095.000

100.000100.000

100.00085.000

…C

518.000

56.00038.000

-0.028101.613

95.699100.000

100.00096.774

100.000103.226

100.000103.226

106.452103.226

103.226103.226

100.000…

C6

16.00024.000

8.0000.045

144.792160.349

100.000112.500

125.000141.129

163.306149.194

160.887159.677

143.548147.581

178.226156.452

…C

90.000

0.0000.000

0.022103.561

130.743100.000

108.782118.095

101.31392.026

109.351112.999

16.919126.359

124.602130.701

101.587…

C10

0.00015.000

15.0000.001

100.085101.984

100.00099.660

105.102101.020

109.18493.878

101.36199.320

92.17789.796

105.102104.422

…C

2717.000

9.000-8.000

0.008108.133

111.667100.000

109.600120.000

129.600129.600

100.000109.600

100.000109.600

100.000100.000

89.600…

C32

8.0009.000

1.0000.002

90.74191.204

100.00088.889

83.33394.444

94.44483.333

83.333100.000

88.88988.889

83.333100.000

…C

348.000

20.00012.000

0.016104.832

111.069100.000

97.318110.217

110.856108.046

108.30198.851

102.427106.258

105.747110.473

99.489…

C35

7.0009.000

2.000-0.014

79.82184.676

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

100.00084.115

55.348…

C37

14.00023.000

9.000-0.023

110.140100.987

100.000103.318

111.256111.374

109.597109.597

113.744117.891

114.455110.427

107.938112.085

…C

3823.000

15.000-8.000

0.013103.419

109.756100.000

108.362102.439

105.052107.317

105.139101.220

100.261102.787

98.084102.003

108.362…

C39

12.00027.000

15.0000.003

110.527110.059

100.000107.063

113.644113.162

111.236112.841

121.990115.409

111.236112.520

110.75496.469

…C

407.000

12.0005.000

-0.030100.000

85.366100.000

100.000100.000

100.000100.000

100.000100.000

100.000100.000

100.000100.000

100.000…

C42

11.00024.000

13.0000.034

111.742122.184

100.000100.455

105.758110.455

113.788115.303

113.788116.364

115.606116.364

121.212111.818

…C

4311.000

22.00011.000

-0.03597.037

83.870100.000

93.55694.222

98.44499.556

106.667102.667

94.88991.111

94.00095.556

93.778…

C44

11.00033.000

22.0000.030

95.897108.708

100.000103.311

96.77497.284

91.42697.878

98.55777.844

98.89698.472

98.89691.426

…C

4912.000

17.0005.000

0.030132.292

138.356100.000

94.444123.611

125.000158.333

148.611133.333

140.278145.833

144.444151.389

122.222…

C54

27.0008.000

-19.0000.032

91.170108.319

100.00093.612

78.10974.446

80.57988.245

96.59390.119

102.129103.237

102.21584.753

…A

verage12.333

19.2786.944

0.005105.258

108.264100.000

100.051103.784

105.211108.627

106.667109.244

101.932108.948

107.633110.285

100.712…

Standard D

eviation7.121

12.356TableG.1:C

ontrolgroupKPR

development

2010

172

Des

crip

tion

…Ja

n11

Feb

11M

ar 11Ap

r11

May 11

Jun

11Ju

l11

Aug

11Se

p11

Oct 11

Nov 11

Dec 11

C1

…11

5.00

090

.000

95.0

0090

.000

75.0

0085

.000

85.0

0090

.000

85.0

0010

0.00

010

0.00

011

5.00

0C

5…

103.

226

106.

452

106.

452

109.

677

106.

452

112.

903

109.

677

109.

677

106.

452

54.8

3958

.065

64.5

16C

6…

152.

016

154.

839

169.

758

178.

226

163.

306

168.

145

180.

242

181.

855

150.

403

138.

306

150.

806

136.

290

C9

…17

3.89

318

4.51

017

6.09

013

2.54

016

0.63

114

1.14

613

5.85

37.

487

134.

598

143.

880

113.

715

64.5

71C

10…

96.5

9911

3.26

510

9.52

410

2.38

199

.660

102.

381

97.9

5988

.095

100.

000

110.

884

114.

626

88.4

35C

27…

88.8

0097

.600

118.

400

132.

000

81.6

0012

9.60

010

0.80

013

1.20

096

.800

94.4

0013

8.40

013

0.40

0C

32…

83.3

3388

.889

88.8

8988

.889

94.4

4494

.444

100.

000

100.

000

88.8

8988

.889

83.3

3394

.444

C34

…10

5.61

910

7.91

810

6.64

111

2.26

111

1.49

411

0.85

610

6.13

011

5.70

911

5.07

011

5.19

811

5.96

410

9.96

2C

35…

92.8

5466

.818

108.

876

73.6

0010

2.77

773

.964

97.4

9781

.111

84.7

5285

.708

89.8

0458

.352

C37

…11

7.06

211

1.73

010

1.89

611

4.45

510

9.36

010

5.68

774

.526

78.1

9998

.815

93.8

3910

2.84

410

3.43

6C

38…

114.

111

108.

885

109.

756

106.

272

107.

056

111.

063

106.

882

109.

756

108.

188

113.

937

105.

923

115.

244

C39

…11

3.00

211

5.24

911

2.36

010

5.29

796

.308

94.8

6410

0.48

210

7.38

411

7.81

711

8.78

012

0.22

511

8.94

1C

40…

85.3

6685

.366

85.3

6685

.366

85.3

6685

.366

85.3

6685

.366

85.3

6685

.366

85.3

6685

.366

C42

…11

0.75

811

7.27

311

6.97

011

6.36

411

7.42

412

6.97

012

3.78

812

9.24

212

8.18

212

2.27

313

1.06

112

5.90

9C

43…

99.1

1198

.667

65.5

5680

.667

74.6

6798

.889

96.0

0091

.778

86.6

6768

.444

75.3

3370

.667

C44

…98

.048

105.

093

105.

688

109.

423

105.

263

115.

110

111.

460

92.7

8410

7.30

111

5.78

911

7.06

312

1.47

7C

49…

129.

306

137.

639

102.

917

155.

972

136.

944

147.

222

143.

611

155.

556

140.

139

139.

028

130.

417

141.

528

C54

…10

3.91

810

5.62

211

6.69

513

2.87

911

7.54

711

8.39

995

.400

107.

325

108.

177

108.

177

86.8

8298

.807

Aver

age

…11

0.11

211

0.87

911

0.93

511

2.57

010

8.07

211

2.33

410

8.37

110

3.47

410

7.92

310

5.43

010

6.65

710

2.40

8

TableG.2:C

ontrol

grou

pKPR

developm

ent2011

173

G Appendix - Control Group KPR

174

H Appendix - WarehouseExcellence ProjectsOverview

175

H Appendix - Warehouse Excellence Projects Overview

Area: Pick and PackKPI definition:O

rder Lines Picked & Packed / M

an Hour

Maturity developm

ent

Leadership

W1

Value Stream

Planning (VSM

/VSD)

Factors of Success

6 7

Productivity Pick/Pack AreaTarget Condition


Regular Comm

unication

CONCEPT

Point-CIPBusiness Requirem

ents



ent …

Dfi

itifT

tCditi

CONCEPT

System-CIP

Visualization

Workforce

Standardization

Workplace

Design

2 3 4 5 6Q


Regular Comm

unication


Process Confirmation

01

23

4

EPT

KPI-Effect

EXECU


ent …


System-CIP Projects

Point-CIP Areas

01

23

4

EPT

Target DerivationSystem

-CIP CyclesIm

provement Focus

EXECU

Average2,939

2,669Resultsofthe

BoschLogisticsW

arehouseAssessm

ent2010

Customer

Collaboration

Sustainable Problem

Solving

Workforce

Managem

ent

0 1 2Jan 11Feb 11

Mar 11

Apr 11M

ay 11Jun 11

Jul 11Aug 11

Sep 11O

ct 11N

ov 11Dec 11

Jan 12Feb 12

KPI-Effect

Quality of Problem

Solving

01

23

4

ECUTION

Start PIA*

System-CIP Cycles

Improvem

ent FocusLeadership Involvem

entVSM

-Quality

Target Achievement

01

23

4

ECUTION

Average2,939

2,669Results of the Bosch Logistics W

arehouse Assessment 2010

Deviation

0,6780,630

Results of the Bosch Logistics Warehouse Assessm

ent 2011D

evelopment

-9,18% (from

Start PIA* with all data before)

*Start Project in the Area

FigureH.1:Project

development

sheetWarehouse

1

176

Area

: Inc

omin

g G

oods

KPI-D

efin

ition

: Ord

er L

ines

/ M

an H

ours

Mat

urity

Dev

elop

men

tW

2

Wor

kpla

ce

Des

ign

Visu

aliz

atio

n

Lead

ersh

ip

Valu

e St

ream

Pl

anni

ng (V

SM/V

SD)

Stan

dard

izat

ion

Fact

ors o

f Suc

cess

Wor

kfor

ce

Man

agem

ent

468101214161820

Prod

uctiv

ity In

com

ing

Goo

dsTa

rget

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ditio

n

Qui

ck R

eact

ion

Syst

em

Regu

lar C

omm

unic

atio

n

Sust

aina

ble

Prob

lem

Sol

ving

Proc

ess C

onfir

mat

ion

01

23

4

C O N C E P T

Poin

t-CI

P

KPI-E

ffect

E X E C U T

Busin

ess

Requ

irem

ents

Valu

e St

ream

Pla

nnin

g

Iden

tific

atio

n of

Impr

ovem

ent A

ctiv

ities

Defin

ition

of T

arge

t Con

ditio

ns

Syst

em-C

IP P

roje

cts

Poin

t-CI

P Ar

eas

01

23

4

C O N C E P T

Syst

em-C

IP

Targ

et D

eriv

atio

n

Syst

em-C

IP C

ycle

s

Impr

ovem

ent F

ocus

E X E C U T

Aver

age

11,7

7913

,027

Resu

lts o

f the

Bos

ch L

ogist

ics W

areh

ouse

Ass

essm

ent 2

010

Dev

iatio

n2,

311

1,72

0Re

sults

of t

he B

osch

Log

istic

s War

ehou

se A

sses

smen

t 201

1D

evel

opm

ent

10,6

0% (f

rom

star

t PIA

* w

ith a

ll da

ta b

efor

e)*

War

ehou

se E

xcel

lenc

e Pr

ojec

t**

Pro

ject

in th

e Ar

ea

Sust

aina

ble

Pr

oble

m S

olvi

ng

Cust

omer

Co

llabo

ratio

n0246

Jan

10Fe

b 10

Mar

10

Apr

10M

ay

10Ju

n 10

Jul

10Au

g 10

Sep

10O

ct

10N

ov

10De

c 10

Jan

11Fe

b 11

Mar

11

Apr

11M

ay

11Ju

n 11

Jul

11Au

g 11

Sep

11O

ct

11N

ov

11De

c 11

Jan

12Fe

b 12

Qua

lity

of P

robl

em S

olvi

ng

01

23

4

C U T I O N

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01

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C U T I O N

Figu

reH.2:P

roject

developm

entsheetWareh

ouse

2

177


Area: Outgoing G

oodsKPI-D

efinition: Order Lines / M

an Hours

Maturity D

evelopment

W4

Leadership

Value Stream

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)

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2

2,5

Productivity Outgoing G

oodsTarget Condition


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unication

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ents



ent …

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itifT

tCditi

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System-CIP

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unication



01

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KPI-Effect

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ent …


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01

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Target Derivation

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entFocus

EXECU

Average0,934

1,0241,228


ent 2010i

il

fh

hh

Sustainable Problem

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ent

0

0,5Jun 11Jul 11

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KPI-Effect

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01

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4

ECUTION

Start PIA*

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ent Focus


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uality

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01

23

4

ECUTION

Deviation

0,2710,149



Developm

ent9,63%

19,97%D

evelopment

25,39%(from

start PIA* with all data before)

* Project in the Area**Refocused

FigureH.3:Project

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4

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ay

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ay

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ffect

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lity

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evel

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ehou

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xcel

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ojec

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ject

in th

e Ar

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28,2

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Figu

reH.4:P

roject

developm

entsheetWareh

ouse

5

179


Area: Outgoing G

oodsKPI-D


an Hours

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evelopment

W6

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ent …


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PT

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entFocus

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Average1,799



3,155

Workforce

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ent

Sustainable Problem

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0 1 2Jun 11Jul 11

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Oct 11

Nov 11

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KPI-Effect

Quality of Problem

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01

23

4

ECUTION

Start PIA*

System-CIP Cycles

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ent Focus


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uality

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01

23

4

ECUTION

Average1,799



Deviation

0,3520,483


ent 2011D

evelopment

75,43%-4,41%

Developm

ent73,64%

(from start PIA* w

ith all data before)* Project Start inthe Area

0,7323,155

FigureH.5:Project

development

sheetWarehouse

6

180

Area

: Pac

kagi

ngKP

I-Def

initi

on: O

rder

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es (E

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ffect

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0

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Jan.

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Feb.

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Mrz

. 10

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ai.

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n.

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l. 10

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Okt

. 10

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. 10

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Jan.

11

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11

Mrz

. 11

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ai.

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n.

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Okt

. 11

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. 11

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Jan.

12

Feb.

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KPI-E

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lity

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ng

01

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evel

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ent

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elop

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,94%

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m S

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ith a

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War

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se E

xcel

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ject

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e Ar

ea

153,

48%

01

23

40

12

34

Figu

reH.6:P

roject

developm

entsheetWareh

ouse

7

181


Area: Incoming G

oodsKPI-D


an Hours

Maturity D

evelopment

Leadership

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Planning (VSM/VSD

)

W8

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50 60

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unication

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ent …


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munication



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4

PT

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ent …


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01

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4

PT

Target Derivation

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ItF

EXECU

Average29,549

31,696Resultsofthe

BoschLogisticsW

arehouseAssessm

ent201034,770

Sustainable Problem

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Workforce

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ent

0 10 20Jul 11Aug 11

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ct 11N

ov 11Dec 11

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KPI-Effect

Quality of Problem

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01

23

4

CUTION

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ent Focus


VSM-Q

uality

Target Achievement

01

23

4

CUTION

Average29,549



Deviation

3,3894,471


ent 2011D

evelopment

7,27%9,70%

Developm

ent10,51%

(from Start PIA* w

ith all data before)* Start Project in the Area

34,7702,960

FigureH.7:Project

development

sheetWarehouse

8

182

Area

: Inc

omin

g G

oods

KPI-D

efin

ition

: Pac

kage

s / M

an H

ours

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men

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9

Lead

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ip

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anni

ng

(VSM

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n

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rt WFM**

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ay

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ov

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b 11

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ay

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n 11

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KPI-E

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Figu

reH.8:P

roject

developm

entsheetWareh

ouse

9

183


Area: Incoming G

oodsKPI-D


an Hours

Maturity D

evelopment

Factors of Success

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W10

Value StreamPlanning (VSM

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100




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unication

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ents



ent …

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4

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g


ent …


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01

23

4

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Customer

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ent

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0 10 20 30 40

Jan 10

Feb 10

Mar

10Apr 10

May

10Jun 10

Jul 10

Aug 10

Sep 10

Oct

10N

ov 10

Dec 10

Jan 11

Feb 11

Mar

11Apr 11

May

11Jun 11

Jul 11

Aug 11

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Oct

11N

ov 11

Dec 11

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Feb 12

KPI-Effect

Quality of Problem

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01

23

4

ECUTION

Start PIA*

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ent Focus


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uality

Target Achievement

01

23

4

ECUTION

Average49,444



Devitation

7,3326,933


ent 2011D

evelopment

15,88% (from


* Warehouse Excellence Project

** Project in the Area

Jan 10

Feb 10

Mar

10Apr 10

May

10Jun 10

Jul 10

Aug 10

Sep 10

Oct

10N

ov 10

Dec 10

Jan 11

Feb 11

Mar

11Apr 11

May

11Jun 11

Jul 11

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ov 11

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Feb 12

01

23

40

12

34

FigureH.9:Project

development

sheetWarehouse

10

184

Area

: Inc

omin

g G

oods

KPI-D

efin

ition

: Ord

er L

ines

/ M

an H

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Fact

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KPI-E

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4Re

sults

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s War

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evel

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from

Sta

rt P

IA*

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ore)

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Figu

reH.10:

Projectde

velopm

entsheetWareh

ouse

11

185


Area: Internal TransportKPI-D


an Hours

Maturity D

evelopment

Factors of Success

Leadership

Value StreamPlanning (VSM

/VSD)

W12

20 25

Productivity Internal TransportTarget Condition


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unication

CONCEPT


ents



ent …

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System-CIP

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unication



01

23

4

EPT

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g


ent …


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01

23

4

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ent

Sustainable Problem

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0 5 10

Jan 10

Feb 10

Mar

10Apr 10

May

10Jun 10

Jul 10

Aug 10

Sep 10

Oct

10N

ov 10

Dec 10

Jan 11

Feb 11

Mar

11Apr 11

May

11Jun 11

Jul 11

Aug 11

Sep 11

Oct

11N

ov 11

Dec 11

Jan 12

Feb 12

KPI-Effect

Quality of Problem

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01

23

4

ECUTION

Start PiA*

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ent Focus


VSM-Q

uality

Target Achievement

01

23

4

ECUTION

Average14,832



Deviation

0,5460,741


ent 2011D

evelopment

27,57% (from


* Project in the Area

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Measuring the Impact of Lean Techniques on Performance ...

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