PROCEEDINGS - repository.ipb.ac.id · Agung Sutrisno, Indra Gunawan and Stenly Tangkuman. Modified FMEA Model for Acessing ... Gilang Almaghribi Sarkara Putra and Rendra Agus Triyono.

IESS '2015 THE 300 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND SERVICE SCIENCE M elia Purosani Hotel - Yogyakarta, 1 - 3 September 2015

PROCEEDINGS

PROCEEDINGS CREATING VALUE THROUGH INNOVATION

Organized by:

Department of Industrial Engineering tnstilul Teknotogi Seputuh Nopember

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Organized by: Industrial Engineering Department, Institut Teknologi Sepuluh Nopember (ITS) Surabaya and

Universitas Atma Jaya Yogyakarta - Indonesia

Supported by: Rajamangala University of Technology Suvamabhumi (RMUTSB) - Thailand Universitas Islam Indonesia (UII) - Indonesia

ISSN:.2338-7939 . @20.l5 :

Edited by:

Iwan Vanany Nani Kurniati Dyah Santhi Dewi

©Copyright Department oflndustrial Engineering, Institut Teknologi Sepuluh Nopember (ITS)

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Cover page

Preface

Table of Contents

• Welcome Speech from Conference Chair

• Welcome Message from Rector of ITS Surabaya

• Welcome Message from Rector of Universitas Atma Jaya Yogyakarta

• Keynote Speakers

• Industrial Engineering ITS

• Universitas Atma Jaya Yogyakarta

Committee

Conference Sponsor

List of Papers

Industrial Engineering - Decision Making

I. Annisaa Novieningtyas and Pri Hennawan. Group Model Building for Policy Making (Case Study: Beef Cattle at West Sumatra)

2. Pri' Hennawan, Yuliati Komar,and Soehartati Gondhowiardjo. A Network-~ased Collaborative Decision Making Model in Reducing Cervical Cancer Spread in Indonesia. Ij

31' Erika Fat~a .. Developl!1ent ofSus.tainable Tuna Proce~.sing Industry ~hroug~ System Dyna~ics . Simulation •... ' '. ',:' ." " . .' '.':. ".: . '. , ..;. .' .

4. I Made Ronyastra, I Ketut Gunarta and Udisubakti Ciptomulyono. A Multi Criteria Decision Analysis for Reinvestment Action Portfolio Selection Problem in an Indonesian Real Estate

20

Company 27

S. Stefan us Eko Wiratno, Em Latimanti and Kevin Karmadi Wirawan. Selection of Business Funding Proposals Using Analytical Network Process: A Case Study at a Venture Capital Company 36

Industrial Engineering - Logistics

I. Farlda Pulansari, Dwi Donoriyanto and Iriani. Perfonnance Assessment Mechanism for Reverse logistics Maturity Implementation toward Sustainable Manufacturing Systems: A Conceptual Framework 42

2. Adi Budlpriyanto, Budisantoso Wirjodirdjo, Nyoman Pujawan and Saut Gurning. Berth Allocation Problem under Uncertainty: A Conceptual Model Using Collaborative Approach S I

3. Nur Ulfa Hidayatullah and Ali Musyafa. Hazop Study on Fuel Distribution System Based on Anfis Layer of Protection Analysis in Surabaya Installation Group PT Pertamina Tanjung Perak S9

4. Ardian Rlzaldi, Meditya Wasesa and M Noviar Rahman. Yard Cranes Coordination Schemes for Automated Container Tenninals: An Agent-Based Approach 66

S. Meditya Wases&, M Noviar Rachman, Ardian Rlzaldi and M Mashuri. Relocating Multiple-Tenants Logistics Center: Lesson Learned from an Air Cargo Tenninal Relocation Project 74

6. Siti Nurminarsih, Ahmad Rusdiansyah and Nurhadi Siswanto. Inventory Ship Routing Problem (ISRP) Model Considering Port Dwelling Time Infonnation 80

7. Sonny Sanjaya and Tomy Perdana. Logistics System Model Development on Supply Chain Management of Tomato Commodities for Structured Market 89

Industrial Engineering - Manufacturing system

1. Wlwin Widiasih, Putu Dana Kamingsih and Udisubakti Ciptomulyono. Development of Integrated Model for Managing Risk in Lean Manufacturing Implementation: A Case Study in an Indonesian Manufacturing Company 95

2. Joko Sulistio and Tri Astuti Rini. A Structural Literature Review on Models and Methods Analysis of Green Supply Chain Management 103

3. Sri Indrawati and Muhammad Ridwansyah. Manufacturing Continuous Improvement Using Lean Six Sigma: An Iron Ores Industry Case Application III

4. Sri Hartini and Udisubakti Ciptomulyono. The Relathionship between Lean and Sustainable Manufacturing on Performance: literature review 117

S. Maria Anityasari and Aulia Nadia Rachmat. Lesson Learnt from Top-Down Medium Enterprises Selection for Green Industry Pilot Project in Surabaya 126

6 Putu Karningsih, Dewanti Anggrahini and Muhammad Syafi'i, Concurent Engineering Implementation Assesment. Case Study in an Indonesia Manufacturing Company 133

7 Nani Kurniati, Ruey-Huei Yeh and Jong-Jang Lin. Quality Inspection and maintenance: the framework of interaction 140

Industrial Engineering -' Operation management

t. Suhendi Irawan. The Effect of Choosing a Transportation Vendor and the Performance of Transpon:ation Vendor on the Performance'ofShipping Goods to Consumer: A Case Study of DB

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3.

4.

5.

6.

7.

8.

9.

, Schenker Freigh Forwarder

Jugkrit ,Mahoran, Sukanya Wonglakron; Sumale~ Namaehote-llnd·Narup'hpn.Oan~mon. Risk., Management ofVilhige Funds hi Muang' District, Suphanburi ProvinCe.' " :. . ~.

Susanto Sudiro and Shn'Ri Mohd Yusof. Managing WIP buffer with combination of feeding materials scenario and conventional control theory of single type of hospital bed production

Agung Sutrisno, Indra Gunawan and Stenly Tangkuman. Modified FMEA Model for Acessing the Risk of Maintenance Waste

Filemon Yoga Adhisatya, The .Jln Ai and Dah-Chuan Gong. Economic Lot Scheduling Problem with Two Imperfect Key Modules .

Bupe Mwanza and Charles Mbohwa. An Assessment of the Effectiveness of Equipment Maintenance Practices in Public Hospitals.

Bupe Mwanza and Charles Mbohwa. Design ofa Total Productive Maintenance Model for Effective Implementation: A case study ofa Chemical Manufacturing Company

Hafid Budiman. Increasing Compressor Reliability with The Weibull Distribution Analysis

Paulus Wlsnu Anggoro and Baju Bawono. Reverse Engineering Technology in Redesign Process Ceramics: Application for CNN Plate

10. Endang Retno Wedowati, Moses Laksono Singgih and I Ketut Gunarta. Integrated Production Planning and Scheduling for Mass Customization in Food Industry: A Conceptual Framework

11. Taufik Djatna and Wenny Dwl Kurnlati. A System Analysis and Design for Packaging Design of Powder Shaped Fresheners Based on Kansei Engineering

12. Taufik Djatna and Muhammad Raja Ihsan. A Fuzzy Associative Memory Modeling for Production Equipment Status Assessment

13. Taufik Djatna and FaJar Munlchputranto. An Analysis and Design of Mobile Business Intelligence System for Productivity Measurement and Evaluation in Tire Curing Production Line

147

152

159

167

173

179

185

194

199

205

213

220

225

14. Sazli Tutur Risyahadi. Scheduling Model of Harvesting Strawberry Considering Product Decay During Storage 231

IS. Chaterine Alvina Prima Hapsari. Deny Ratna Yuniartha and Ignatius Luddy Indra Pumama. Tour and Break Scheduling for Shift Operators in Hard Disk Drive Manufacturer 239

16. Jwannaraksu Phen. An influence of packaging design on customer purchase intention 247

17. Dewanti Anggrahini, Putu Dana Kamingsih and Martian Sulistiyono. Managing Quality Risk In A Frozen Shrimp Supply Chain (A Case Study) 25 I

18. Anny Maryani, Sritomo Wignjosoebroto and Sri Gunani Partiwi. A System Dynamics Approach for Modeling Construction Accidents 259

19. Naning Aranti Wessiani and Satria Oktaufanus Sarwoko. Risk Analysis of Poultry Feed Production Using Fuzzy FMEA 265

20. Mohamad Faisal Mohamad Sobri, Hawa Hishamuddin, Noraida Azura Md Darom' Disruption OM I Pccovery for a Single Stage Production-Inventory System with Optimal Safety Stock

Industrial Engineering - Operation research

I. Wahyuda and Budi Santosa. Dynamic Pricing in Electricity: Research Potential in Indonesia

2. Chirag Sancheti, Aditya Balu and Amit Kumar Gupta. Simulation based optimization of productivity using Flexsim

3. Budl Santosa and 1 Gusti Ngurah Agung Kresna. Simulated Annealing Algorithm to Solve Single S.tage Capacitated Warehouse Location Problem (Case Study: PT. Petrokimia Gresik)

4. Yuanita Handayati, Togar Simatupang and TomyPerdana. Value Co-Creation in Agri-Chains Network: A Hard Agent Based Simulation

. 5 .. Budl Santosa·and Ade Lia Safitri. Biogeography-based Optimization. Algorithm for Single . Machine TbtalWeighte<q'ardioess Problem" . . '. . .,. . .. ':. . . ..'

6. Gilang Almaghribi Sarkara Putra and Rendra Agus Triyono. Proposing a Neural Network Method for Instrumentation and Control Cost Estimation of the EPC Companies Bidding

276

283

289

298

, 307

Proposal 313

7. Sattarpoom Thaiparnit, Baramee Osateerakul and Danupon Kumpanya. Algorithm Design in Leaf Surface Separation by Degree in HS" Color ;\'Iodel and Estimation of Leaf Area by Linear Regression 320'

8. Danupon Kumpanya and Sattarpoom Thaipamit. Parameter Identification ofBLDC Motor Model via Metaheuristic Optimization Techniques 326

9. Sinta Dewi, Imam Baihaqi and Erwin Widodo. Modeling Strategy of Purchasing Consortium to Optimize Total Purchasing Cost Considering the Dynamic Condition ofOrganizaion 332

10. Taufik Djatna and Imam Muharram AUtu. An Application of Association Rule Mining in Total Productive Maintenance Strategy: An Analysis and Modelling for Wooden Door Manufacturing Industry 340

Industrial Engineering - Product Development

1. Yosephine Suharyanti, Subagyo. Nur Aini Masruroh and Indra Bastian. The Scheme of Product Development Process as a Trigger to Product Success: A Theoretical Framework 347

2. Ishardita Pambudi Tama and Wifqi Azlia. Development of Customer Oriented Product Design Using Kansei Engineering and Kano Model (Case study of Ceramic Souvenir) 355

3. Taufik Djatna. Lub Putu Wrasiati and Ida Bagus Dharma Yoga Santosa. Balinese Aromatherapy Product Development Based On Kansei Engineering And Customer Personality 362 Type

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4. Dyah Santhi Dewi, Bambang Syairudin and Eka Nahdliyatun Nikmah. Risk Management in New Product Development Process for Fashion Industry (Case Study: Hijab Industry) 368

Industrial Engineering - Project Management

I. Jeyanthi Ramasamy and Sha'Ri Mohd Yusof. A Literature Review of Subsea Asset Integrity Framework for Project Execution Phase 376

2. Baju Bawono and Paulus Wisnu Anggoro. Utilization Of Rapid Prototyping Technology to Improve Quality Souvenir Product 384

Industrial Engineering - Supply Chain Engineering

1. lrwan Syahrir, Supamo and (wan Vanany. Healthcare and Disaster Supply Chain: Literature Review and Future Research

2. Yudi Fernando and Sofri Yahya. Challenges in Implementing Renewable Energy Supply Chain in Service Economy Era

3. Araya Uengpaiboonkit. The Marketing's Factors that Effect to Consumers Decisions of Organic Rice in Surin, Thailand

4. Layung Prasetyanti and Togar Simatupang. Proposed Framework for Service-Dominant-Logic Based Supply Chain

5. Taufik Djatna and Rohmah Luthfiyanti. An Analysi~ and Design of Responsive Supply Chain for Pineapple Multi Products. SME Based On Digital Bus~ness Ecosystem (DBE)

6. Erwin Widodo. A Model Reflecting the Impact of Producer Substitution in Dual-Channel Supply-Chain Inventory Policy

7 .. Taufik .Dj~t~~ a~d He~ :H~~da~ani· Hidaya~ .. An' Optimized S~p.~iy thain· Model for . . . .. Detennination of Distribution Center and Inventory Level in A Coconut'Water Agro-Industry

8. Siamet Setio Wigati and The Jin Ai. An Integrated Production System Model for Multi Supplier Single Buyer with Non Confonning Item and Product Warranty

9. Yoshua Perwira Hartono, Ririn Diar Astanti and The Jin Ai. Enabler to Successful Implementation of Lean Supply Chain in A Book Publisher

10. Sutrisno and Pumawan Adi W:Caksono. Optimal Strategy for Multi-product Inventory System with Supplier Selection By Using Model Predictive Control

11. [wan Vanany, Anny Maryani and Bilqis Amaliah. Blood Traceability System for Indonesian Blood Supply Chain

Industrial Engineering - Safety & Ergonomic

I. Natalie Carol Skeepers and Charles Mbohwa. A Study on the leadership behaviour, safety

390

398

404

408

416

423

430

436

443

450

457

leadership and safety perfonnance in the Construction industry in South Africa 464

2. Herry Christian Palit and Debora Anne Yang Aysia. The Effect of Pop Musical Tempo during Post Treadmill Exercise Recovery Time 470

3. Eko Nurmianto, Udisubakti Ciptomulyono, Supamo and Sudiyono Kromodihardjo. Manual Handling Problem Identification in Mining Industry: the Ergonomic Perspective 475

4. Wiyono Sutari, Mumi Dwi Astuti, YusufNugroho Doyobekti and Yuvie Mutiarasari. Analysis of Working Posture Effect on Muscular Skeleton Disorder of Operator in Stamp Scraping in Batik Stamp Industry 483

5. Rino Andias Anugraha. Wlyono Sutarl and lima Mufidah, The Design of Working Desk of Batik Scraper by Using the Principles of Ergonomy 488

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6. Budi Praptono, YusufNugroho Doyo Yekti, I Gede Wisuda Pura and Fransiskus Tatas Dwi Atmadji. Prevention ofMusculo Skeletal Disorders of Green Beans Farmer Through Application of Ergonomics in Order to Developing Manual Handling Equipment 496

7. Manik Mahachandrn, Yassierli and Erdo Garnaby. The effectiveness of in-vehicle peppermint fragrance to maintain car drivers' alertness 500

8. Yassierli, Manik Mahachandra and Iftikar Sutalaksana. Fatigue Evaluation of Fuel Truck Drivers 506

9. Ayu Bidfawati and Eva Suryani. Improving the Work Position of Worker's Based on Quick Exposure Check Method to Reduce the Risk of Work Related Musculoskeletal Disorders 512

10. Bernadus Kristyanto, Brillianta Budi Nugraha, Anugrah Kusumo P and Kristanto Agung N. Head and Neck Movement: Simulation And Kinematics Analysis 518

II. Ronny Norlyati, Wisnu Rozaaq, Ali Musyafa and Adi Supriyanto. Hazard & Operability Study And Determining Safety Integrity Level On Sulfur Furnace Unit: A Case Study In Fertilizer Industry 525

12. Dyah Santhi Dewi and Tyasiliah Septiana. Workforce Scheduling Considering Physical And Mental Workload: A Case Study Of Domestic Freight Forwarding 531

Industrial Engineering - Suporting topics in Industrial Egineering

I. Hatma Suryoharyo and Niken Larasati. Sustainable Livelihood Framework As An Approach To Build Community Based Security 539

2.· Ngurah Wira, Amelia KUf!liawati and Umar Yunan. The Design of Best Practice on The Media Transfer Activities and Preservation Based on Knowledge Conversion with SECI Method 546

, , 3,. Sri GU,na!,!1 Partiwi" EUy Ag~tiani an~ Anny Maryani. Preparation for I?esigning ~usiness Strategy.of'Bamboo Cultivation in Bondowos,o' '. '.' ..

4. Yosephfne Suharyantl and Alva EdyTontowi. Market Response as a function of Design, Competition, and Socio-political Condition: An Empirical Model

Service Science - Service Business Design & Strategy

1. Tri Ramadhan, Dermawan Wibisono, Reza Ashari Nasution and Santi Novani. Design of Self

552 .

558

Service Technology on Passenger Shipping Transportation Service System in Indonesia 566

2. Ratna Hidayati and Santi Novani. A Conceptual Complaint Model for Value co-Creation Process 574

3. Mikhael Tjlll, Jann Hidajat Tjakraatmadja and Santi Novani. Designing value co-creation process in organic food product distribution Case srudy in Bandung 579

4. Nurtami Prihadi and Santi Novani, Value Co-Creation among Stakeholders in Solo Tourism Development: Service System Science Perspective 591

S. Arlavianyssa Pradiva Arru and Santi Novani. Value Co-Creation in Solo Tourism by Using Soft System Dynamics Methodology 60 I

6. Rizki S. Nurfitria and Mursyid H. Basri. Developing Clinical Pathway Model in Public Hospital as Basic Component of Casemix System 609

7. Watcllaree Phetwong and Krisorn Sawangsire. The Development of Computer Game for Historic Sites Learning in Suphanburi. 617

8. Lidia Mayangsari and Santi Novani. Multi-stakeholder Co-Creation Analysis in Smart City Management: An Experience from Bandung 622

Service Science - Service Delivery & Operations

1. Imam Djati Widodo and Hanvati Sutanto. AR MBA: Linkage Pattern of Visited Tourism Object 628

2. Liane Okdinawati, Togar M. Simatupang and Yos Sunitiyoso. Value Co-creation Map in Collaborative Transportation 635

3. Americo Azevedo and Maratus Sholihah. Innovative Costing System Framework in Industrial Product-Service System Environment 642

4. Iwan Vanany, Udisubakti Ciptomulyono, Muhammad Khoiri, Dody Hartanto and Putri Nur Imani. Willingness to Pay for Surabaya Mass Rapid Transit (SMART) Options 649

Service Science - Service Quality

1. Agus Mansur and Rizky Oestiana Hapsari. Analysis of the Public Transportation Service Quality on Trans logja Transportation 658

2. Zya Labiba and Mulih Wijaya. Improvement Quality ofIndustrial Training Center Through Service Quality Based on Participation Perspective 663

Service Science - Supporting topics in service science

1. Gembong Baskoro. The concept of balancing Higher Education Institution (HEI) organization towards global and regional challenges 670

2. Samhuri Ikbal Pradana, Amelia Kurniawati and Nia Ambarsari. Knowledge Management System Implementation Readiness Measurement in ·POll LIPI Based On People and Organizational Structure Factors . 674

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I'rocecflillt; IIf Im/II.Hr;1I1 Ellgillet!rill~ flllIl SCrI'ic:c Scicllce , 2015

An Analysis Intelligence Measurement and Production Line

and Design System

Evaluation

of for

Taufik Djfllllaa', Fajar MunichpUITOl lllOb

Mobile Business P,"oductivity Tire Curing •

In

" l'os l gr~du~lC I'rogr:Ull, i\gro-industrial Technology Department. Ilogur Agriculture U ni"c!'~i l y. Bogor. Indoncsi3, t:IU ­

filalj:lln:l '.<!. lpb.:Il' .Id. OR 1 J 19726:135 bGradlwtc l'rogram, Agro-induslriaJ Tcd1nology Department. Bogor Agriculture University, Bogor, Indonesia. fa­.hl!:!110991(t1 ,jpi!0P'b.>lc.id. OS 1 J 9697687

ABSTRACT

/Jlls/ness illlelligcllcc (lJ/) system as an architecrure of competellcies. processes, leellll%gies. applications lind pl'llclice.~ to SIIPpO!'1 prodllctidry measurement. If obl'iolls~\" needs (/ IJI [hat .wppon organizatiol/ to dec/are lIll)' prot/llc/ioll COlls/raillt.l· tlltll cllrrelll!y or a!I'em!y oC/,;UI'rr.w ill .weh tiS (ire ClIl'illg il/dll~''')'. Overall Equipmellt 1:.f/ecril'l!./I(!~·.~ (OEE) is used (IS (I I/II(/lititatil'e prOdllCfil'il), mca.wn!lIIellT (llId becollle Ihe beHe of compally COli­fill/lOIiS imprOI'cmf'1II alld cralll(lliv/!. The objecfires of fhis ,wu/)' (lrc lO idell/ify crilical paramcfers of produc­tioll lille ill cJJecfil·ell es.~ measuremenT and machine lIIili;:(l/ioll. (II/(I'y::e flu: reqlliremem of illfol'llwtioll sy.~tcm liS fill Alldroid hased !I1obile BI System (llId fo illlcgr(lle III(.' de~'igll illlo a mobile system. !:,)'slem requirement (11/(/­

Iy::ed each illterdcpelldcmll1em'll/'c ill lite rcal \l'orld of COlllplexil), by IIsillg BPMN 2.0. The,~e mClI.wrcs (Ire part of c/ashbo(ll'd CQmpOllelll.f ill tlte proposed Ill. The acqllired d(l{(I from II Na tiOllal forefrv lIf lire illdusfI)' shows Ihe OE£ ill three ralio /IIcasurcmellf of amilabililY. peltOl'mallCe, alld quality with 78%. 82.5% and 99.8% of st;orecard respcclively. III order /() dctermille tftc statllS 0/ pmdllctioll. the dcploy mclI/ 0/ k-lIcare.1"l neighbor (k. NN) gh'e~' 67.5% ojaccllmcy rille, Th c aifical pm'olllclel's idemijicmioll re.w/fS to 8 (eight) ~'igllificwlI co//­,Slraim,l', which ca lculated /lsillg di,I'/(lIIce-/)met/ RELIEF mfribllte selectioll. E"enlllally litis approach res lllt~· big fOllr cOllS/milll.I' W be /loted: (a) /IIold repair, (b) mold .I·clling, (1..) grecn lire shortage alld (d) defect cwe,

Ki')'ll'ords: bllSilless illfelligcllce, tire cllring, prodllctil'it)'

I. Introduction

Productivity is one of the company success indicators in prod ucing goods and impro ving service quality to consumers or related pan ics. Productivity is a measurement level to obtain the results with a variety of aV,1 i lable resources l I , . To regain the ir com petit iveness wit h in th e market, com pan ies should apply th e Total Production Managemen t (TPM) fo r cost efficiency and higher profit margi n . Most of multinational tire man­ufacturers arc Original Equ ipment Manufacturer (OEr,'I) for automotive industry: hence the critical parame­ters arc pointed on how they produce goods wilh right quantity at the right place and at the right time. or termed as just·i n-tim e [21. Just- in-t ime (J IT) is llOW often termed as lea n manufacturing that wou ld have become iss ues for product ion s trategy. Recen tl y. business need ,I method 10 repair and reveals the problem as so lution due 10 dynamic eve nts and inc ident s in production. Core problems co uld be revenled llsing business intelligence (81) by applying dashboard to measure and monitor performance of production. Productivity measurement within 131 system s is able to comb ine data ga thering. data s torage, and knowledge management with analytical too ls 10 present complex internal and co mpetiti ve information to planne rs and decision mak­ers. Some organizatiolls use BI to gain data-driven insights on anything rdated to business performance, Enterprise can build busi ness intelli gent (I3I) system usi ng analytics illsight to trigger business even I [3]. Any measurement from analytics insig ht is shown at specific parameters on dashboard that reflect the definition o fproducli vit y scorecard to su pport dec is ion making,

III thi s papcr, we focu s on productivi ty data min ing usi ng Overall E<l uipm cllt Effec ti ve ness (DEE) as indica ­tors. DEE is an appropriate method to measure tire curing as products arc discrete and able to revea ls the six big losses in productivity. According to Maran el.al (2012) six constraint s arc divided into three categories

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I\n An:llysis and Design of ~'I nbilc Business Intell igcnce Systcm for Productivity MC:I$UrCmcnt and Evaluation ill Tire Curing I'roduction Line

which provide major influences on productivity [41. Constraints could be minimized by predicting and de­term ining the priority constraints to be solved from a variety of constraints that may occur during thc pro­ducti on process. Predictin g ami sequencing th e cons tra ints nre likely to support management to deci de pre­vcn tive ac tion for coming period - especially with a mobile and fr iend ly-practical dashboard to monitor the reports. This paper is merging the powerful busi ness analytics and fundamental OEE measu remen t (using data mining) to revea l Ihe sil ve r linin g of produ ction and reduce business friction with mobile 131 dashboard to triggcr bu siness acti on in real time.

l3ased on the motivation above. the objecti ves of thi s paper are to identify critical parameters o f production line in effecti vencss measurement and machine util ization, anal yze the requirement of informati on system as a mobile BI system and to integrate the design into a mobile sys tem. Critical parameters obtai ned by produc­tion constraints OEE valucs of so mc production lines . OEE prod m;tion lines with the lowest value will be anal yzed furthcr in the form of prediction s for the nex t period of production status using algorithm k-nearest-nei ghbor [5}. Priority is determined using RELIEF a lgori thm [6J. Both algorithms are used as a loss of transparency in the productiou process so as to facilitate efforts to reduce such losses.

2. Research Method

This pape r is wrillcn in on-fie ld experi ence, which all the data was acquired from one of the biggest tire multinationa l company in Indonesia. The facts of production '1c tivity lead to build the systemat ic methodol­ogy to solve the prOd ucti on constr<li nt s. Frequency of constra ints cou ld be minimi zed with an ticipat ion and preventi ve action . Categoriza tion of constrai nts wil l case the tlser to determinc right treatment for the con­strai nt s and computational calculation. Co nstraints should be prioritized and first priority or main constraints should be placed 0 11 top of all production constraint s which effec tively reduce total both major and minor constraints and brie ny in crease the producti on quali ty. All classifica ti on and sequencing rcsult should be monitored (dashboard) to trigger management dec ision in order to main tain production l[uality. ['Ience, the sequences abovc arc elaborated in the methodology cxplanation below.

2.1 C onstraint Analysis

:I . Ovc rall Eq uipm en t E ffecti ve ness (DEE) i\ h 'as llr"cment

OEE is a combined measurement of the effectiveness of the time (availability), machine perfOmlallCe (performance) and product quality (quality) [7]. The genera l formula used to calculate the OEE va lue is written in equati on.

OEE • tIl"CIifubili(I' )( IIcrfiJl"ll!flllCC )( IJI/fllil)' ( I )

The effectiveness of the time (availabili ty) shows the ability of a machine to produce product production. As sccn in equation ( 1.2). this paramctcr is the result of a comparison between the times spent during the pro­duction takes place with a tota l time avai lable for such acti vi ties.

Avai!:tbility=RuntimclLoading Time (2)

Engine perfonnance shows the ability of a mach ine to produce product product ion (pcrformanee). This parameter is the result of a comparison betwecn the achievements of production Ihat should result in a net time used fo r production activi ties take place. Calculation engine perfor1l1!1nce le vels seen in e(jllation.

I'erfonllance"- lotal prodllctionilarget production (3)

While the level of product quali ty (quality) on OEE is the ra tio between the total products is acceptable (good) wi th the tota l product produced. Thi s measurement is close ly rel ated to the number of defective products (defects I scrap) .

Qua1ity=good product/total production (4)

b. Pr"cdi etion of Pruduct io n St:llU S

Prediction product ion status wi th k-nearest neighbor algorithm aims to prcdict the classi lication statuS of production in th e next period. Principle of this algorithm is to find a pattem of a dat'l se t (tmining tuples) and the class ifi cation of data of unknown e lass iticrtlion (u nknow n tupl e) 15J. Classilication is done by measuring the

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An Analysis lind Design of 1t,'lobi lc Business Intelligenec System for Productivity f','!casurcmcnt and [\,;Ilu:uion in Tire Curing Production Liuc

distance to the k nearest training tuples where itcm data as rderence and lix data in the problem domain and un ­known tuples which indicate data to be class ified with in thc proposed solution, In this s tudy. the closest distance is calculated by Euclidian dis tance (1.5).

I~ . . 2 2. (XII-.QI ) ,XI i=!

II':l ining tuple; x 2 "" unknown wpl es (5 )

Company's hi storical yie lds data indi cate the mnount of production losses caused by any co nstraint s ha ve di ffercnt ranges. Normalization of data needs to be do ne so that any outcome classi fication has a valid va lue. The itcm of nonnalized data represented as \.' where nonll:l lization proccss of data on cach :llIribute constr:lints made poss ible using the following equa tion .

v", unnonnali zcd claw,;/= value on dataset

(6)

c, Product ion Co nst r:l ints sequ ence

Recursive Elimination of Features or RELI EF technique in att ribute sekction algorithm is used to give weight to each constraint to a production status. Weight values will be sorte(1 as priorit y constraints most exhib­itcd siglli fieantl y caus ing losses in producti on. RELI EF is the princip le o r tile technique using the Eucl idea n dis­tance to the data (i nstancc) of the othcr data in the same attributc, Instance classi fied into two, namely Ncar-hi t and Ncar-mi ss. Ncar-hit arc thc instances thai arc in one classification while Ncar-miss has a difTcrcllI classifica­tion [6J . In this s tudy, the classificat ion is dClefmincd by k-nearest nei ghbor algori thm ,

3. Result and Discuss ion

3, 1 Prod uction Co nst ra int Analys is

Study case was conducted using multinational tire company production data which has been coll ec ted in sevcral wecks. The OEE was calculated using thc three ratios include Ihe level of effectiveness o f thc time (availabi[ity), eng ine performance (pcrfoTlI1:mcc) and product qU:l lity (quali!y). l3ased on the data availablc, the results arc shown in Figure I as follow s. Data was compared with world standard ofO EE in manufacturing.

100

80

60

40

20

o Availability

IiiI PT.X

IiiiI Worid Class

Performance Quality

[n relation to thc six big losses components, the production constra ints in tire manufacturcr were directly ob­served on ficld. The resu lt of 8 (eight) main constraints of tire manufacturing (p lease 110te that constraints were only focused on mach inery issues) and c:o;planation will be spelled out in Tab le L MIG Trollb le is olle of the biggest breakdowlls of a machine bCCHuse its failure in tire cllfing. The Backward I" Cllre and Green Tire Shortage arc constraints eauscd by miSmatch in scheduling or undeliverable production target which will impact the next production activiti es. Mold Selling and MoM Repair arc some constraints that impacted the performance of production which causcd by adjusullents of tire size that 10 be c urcd. Bladder Changes is act ivity to change the bladder due its durability to maintain quality of tire curing which cou ld easily downgrade quality tire or so callcd defect.

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An An:alysis and I) esign of Mobile Business Intell igence System for Producth'ity Me:1surcment and Evaluation in Tire Curing Production Line

Tabll' I. Six Hi~ l.oJses Idcntincnlion

..... 0. Con~ lr~ inIS Sj:r llig I.ossrs Calfj!ory

I. /l.I/C Tro ll b l ~ IlrCJkdowliS ,. Backward I " Cure

J . Green T; r~ Shonasc (GTS) ,. Mold Sellmg ,. Mold Rq •• lIr Selup and AdJuslmfnlS

,. Cleaning Tools

7. Rladdcr Changes

,. Ikfe" Ikjeci

As mentioned above. the constraints should be cl ilssilicd and sequenced in order to conclude the production and trigger management action. The classification process is using k· nearest neighbor (KNN) as one of da ta mi ning technique. The princip le o f K· Neilrest Nei ghbor (KNN) is look ing fo r the shonest distancc between Ihe data to be eval ualed wit h K neighbor (neighbor) in the closest training data. K value can be determined experi. menta lly and we used k- 2 with 67.5% accuracy rate. Then do the classifier data test b:1sed learning data to obtain production status as call be sccn in the T able 2.

This class ification helped user to deten11i ne the dala classificmion of unknown tuples. Each tuple represents dai ly production. Thc class ifi cat ion will be interpretcd in dashboard to measure the quality of production as key performance index (Kil l) i.e. 85% is classifi ed as G or good, 75%·85% will be classifi ed as A or acceptable and the rest wil l be classifi ed as NO or /lot good. This method is the lIIost :1ppropriatc compared to other techn ique whie:1 has 67. 5% accuracy ra te. higher tho II other method i.e. N:1 i"ve Bayes cl assifi er or suppon vector machine (SVM).

While there arc ma llY NG clussifications, Ihere shoul d be an in-depth analys i.~ to identify the cri tic,1) paT<1m­eters of lire curing. Rcfer 10 fra meworks Ihat have mentioned above, the conslra ints should be sequenccd or weighted to ident ify the most illlp:1cting constraint in tire curing. Constr:1inls on the production of the weight calcu lation to detennine the conslraims that have the most impact on production fa ilures. [n Ihis study the melh· od used 10 calculale the RELI EF weight of each consimili!. The princi ple of the ealcu lalion is 10 calculate the miss and hil each att ribute 10 each classifi cat ion [S J. This lechni<lue rcsu[ ted weight and ranked to kllOw the most afTccting attribute (constmints) on the fie ld. Gr!lph of ranked weight is depicted in Figure 3.

Tahle 2, Cla ~ s in c:"ion Slllllili c of l' rIllJ \I ~lio ll S l:illI .~

i\ IIC T rou. III:ultin GTS

1" C urr i\t(l ld i\t otd C tc~n l n 1: \) e r~C I Others Cb u ifi ca lion

01. C h:lner Ib el.wanl Sm lne lt e ~Dlr TO(lts C urt

0.735 0.552 1.000 0.000 0.000 0,769 0.000 0,245 0.746 A

1.000 0.966 0.1135 0.000 0.000 0.000 0.800 0.51 1 0 .302 A

0.000 0,793 0.6211 0.000 0.000 0.000 0.000 0,367 0.127 NG

0,295 1.000 0,728 0.000 0.000 0,}08 0.000 0,327 0.4 .14 NG

0.344 1.000 0.50-1 0.000 0.500 0.000 0,000 0,0112 0.667 A

0.0-19 1.000 0.4.\.\ 0.000 0.000 0.000 0.000 0.000 0.508 NG

0.328 0. t 88 0.8 13 0.000 0.000 0.000 '.000 0.000 0.41 3 NG

0.590 0.906 0,,104 0.000 0.000 0,000 0.000 0. 14) 0.S7t NG

0.000 0,20-1 0.733 0.179 O.IJ(J{) 0.179 0.000 t ,OOO 0.000 A

0.098 0.463 0.182 0.000 0.000 0.000 1.000 0.347 0.000 A

0.000 0.778 0.42 1 0.000 0.000 0.000 0.000 0. t63 0.000 NG

Nute ' A ..,. An:l.'plllbll' ,vG .. NOI G(J(J(I

Those calculations above wi ll become the core computationa l ca[culat ion ror the monitori ng dashboard of business int ellige nce appli cation. The model and system arch itecture will exp lained be low.

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An Ana lysis aud Design of Mobi le Uusiucss Intelligence System for Producti vi ty i'>·lcasuremcnt and Evaluat ion iu Ti re Curing Production Line

" 2.' .,

2 , " L' 0 0 u I. "0 -0.' < .~ 3

0

MIC T.ouble

GTS lst Cure Mold Mundur Settlnl:

Mold Cle3ning aladder Defect Repair Tools Ch~nge Cu.c

Fi):u re l. Crilic:1I Conslr:tin ls on Tire C uring

Othe.s

3.2 Business Intelligence S)'Sle lll i\ lodel and i\ lobil c-Bascd Soft wa re fo r Prod ucti vi ty Eva lua tion

The relation of productivity mcasurcmel1l and business intellige nce arc interpreted in KP[ and dashboard. The numbers Ihat have been calcu lated using lllethods above become the parameters to determine the quality of production. The dashboard itse! f helps management to decide dynami c-based eventS that usually happens during production. All the d[lIa were collected in data warehouse from any stakeholders. Data were processed in com­putational exerc ises that consist of data mining technique to extr.tct infonnation and conclusion over the scal­lered data. The il lustrati on of rea l lime 13 1 concept is Fig urc 4 below.

- _--::-..,. - H - ~ !~

J

f -Figure 2. Business Intelligcllu' Com:ell '

The requirement will be elaborated using business process mode l. Business process model is :1 model to help identifying, illustrating and elaborating a problem in a business. Business process is one or more procedurcs to revea l bus i ne~~ objectives. usually in thc contcxt of organizational Slructurc thai define the function.!1 ro le and its re lat ion [9J. The mode l is usi ng Business Process Model Notation 2.0 thnt de fines the process of information and become the basis o f business intellige nce system architecture. Business process model is illustrated in F ig. ure 5 below. The highlighted process is the critical busincss intell igence components thai will tran slated into dashboard.

Figun' J. Process Bl.sis Fra~rne" t of Business Intelligence

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An Analysis and Design of Mobi le Business IllIclligcncc System for J' roduc ti vi ty /l.lca5urCnlCnt and [v:llua tion in Tire Curing Product ion Line

Design an infonnation system into mobile-based package werc then compiled wilh object-oriented programming using Unifi ed Modeling Language (UML)_ UML is used 10 meet lhe visual modeling needs in spec ifying, illus_ trating. build ing a]](1 documentation of software system. UML is also termed as visu:11 language for mOdeling and communication in relation to a system !Ising supporting diagram [IOj. In this paper, the so ft wlIre was built using Andro id platfonn, which could be lIpplicd in any recent devices. The software is real -t ime connected to the server and other Pc.

AVAtLAO IUTY P(RfORMANC[ QUAlfTY

" . -----l -I _

I -I..

' 0

E I ·.

I: •• ! -

OVERAll EQU IP M EtlT EHECTIVEtl£SS

, ... J .. 1-

PRODUCTION STATUS , M AI N CONSTRAINTS

Mold Sc1tln~ Mejd Repai.

De'.cl Cure G"

..

, I I

PROOUC110N OE SCRIPTION

Da, • . 1 - 01 · l01S S~ ifl : 2 PK : Mr.X

AC1ror~ PLAN

FI I!UH' ~. Bl D:.shho;.nh for Prod uclivity ,\ le:,slIremt'nl

4. Conclusion This paper proposed a construction 10 build Android based mobile Business Intell ige nce application by fi rst

identify ing cri tica l parameters o f producti on line in e ffectivcness measureme nt and machine utilization, and then by ana lyzing the requiremcnt of information system as a mobil e 01 system and cventua ll y by im cgrating the des ign in to a mobile sys te m. These computational exercises we re elaboratcd as Key Pcrfoml;lr1ce Index (KPI ) to be monilored on dashboard and like ly to trigge r management decision which naturall y to cover the dy­namic-based production evcnts production. By compiling end to end crit ica l factors in tire curing production, the ;­dashboard would inform operators and management the rea l time sitli ll ti on in production !inc as the core of business int elligence (01) concept. This mob ile BI-system of production evaluation is the inili,,1 s teps and ex­pected to deve lop whole tire production process or other manufacturing compan)'. Fu rthennort:. in order to en­hance the foca l point of business inte lligence and OE E as an integrated system of en tcrprise resource planning (E RP) with Overall Financial Effectiveness (O FE) to measure the cost reduction and asset deprcc iation of ma­chincry and equipment.

5. Referenc es 11 J Herjanto, E., '\/(lIIagemelll O,Jeraliom', Gra~indo . Jakarta. 2007.

121 [JI

Reid, Dan R. and Nada R. S .. Opcl"lI/iol1$ A-/wwgelllcl1/ 11ll11l/('gI"IIICl/ Appl"()ac!l. John Wiley & Sons. Inc .. 2011, p.233.

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