Lean Manufacturing and Operational Efficiency of Nestle ......lean manufacturing system is a manufacturing system that aims at achieving more with less in such a way that value is

IORMS Journal of Management & Social Sciences(IORMS - JMSS)

ISSN : XXXX-XXXX Volume 1, Issue 1 (Sept-Oct. 2018), PP 01-28

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Lean Manufacturing and Operational Efficiency of Nestle Nigeria

Plc. Using Data Envelopment Analysis (DEA)

AMOS, Nneoma Benita1 , ADEBOLA, Solomon Ajayi

2, ASIKHIA, Ubaisifo

Olalekan3, ABIODUN, Joachim

4

1Business Administration & Marketing, Babcock University, Ilishan Remo, Ogun state, Nigeria.

2Vice chancellor‘s office, Adeleke University, Ede, Osun State, Nigeria.

3Business Administration & Marketing, Babcock University, Ilishan Remo, Ogun state, Nigeria.

4Business Administration & Marketing, University of Abeokuta, Ogun state, Nigeria.

Abstract : The food and beverages (F&B) industry is believed to be the most thriving in the manufacturing

sector in Nigeria, and as such is expected to contribute significantly to economic growth and national

development, but analysis of available statistical data reveals myriads of operational inefficiency that have

hindered optimum performance in the sector. The study examined how the implementation of Lean

Manufacturing System (LMS) affects the operational efficiency of a leading company in the F&B industry

(Nestle Nigeria Plc.). The study employed the usage of Data Envelopment Analysis (DEA) to access the

operational efficiency of the system. It was discovered that the average operational efficiency score improved to

98% after the implementation of the lean system as against the 90% average performance before the

implementation. The study discovered that a positive and a significant relationship exist between the Lean

Manufacturing System (LMS) and the operational efficiency of the system as seen by the respective P-values of

0.11 and 0.026 before and after the implementation of the lean system. It was therefore recommended that the

sampled company and others along the same value chain should seek to become a lean enterprise in order to

improve their operational efficiency.

Keywords: DEA, LMS, Operational Efficiency, Optimality.

I. INTRODUCTION The concept of lean manufacturing is increasingly gaining a global prominence both in theory and in practice

across several sectors like the Automobile, Manufacturing, Construction and the Service sector. The reasons

adduced for this development are obvious: firms want to optimize values, gain and sustain competitive

advantage in the intensely competitive global economic space (Grant, 2010). Indeed, the increasing level of

competition is driving firms to seek survival strategies, to keep abreast of the changing economic landscape, as

well as stay competitive (Amin & Karim, 2013). The Lean Manufacturing System (LMS) gained prominence

after the work of Womack and Jones in 1990 on the book ―The machine that changed the world‖ which

explained how the Toyota company imbibed and recorded tremendous success from the adoption of the ―Toyota

Production System‖ (TPS) which is also known as the LMS.

Atkinson (2004) defined the Lean system as a concept, a process, a set of tools, techniques and methodologies

that allows for successes in bringing about effective resource allocation. He argues that although lean

manufacturing is a cost reduction mechanism, this should not be the sole aim of adopting the lean strategy else it

will never take its rightful role as a preventive methodology. According to Amin and Karim (2013), a lean

manufacturing system is defined as a multi-dimensional approach that includes a variety of effective

manufacturing practices, such as just-in-time (JIT), Total Quality Management (TQM), standard work

processes, work groups, manufacturing cells, Total Productive Maintenance (TPM), and supplies involvement in

an integrated environment. Lean manufacturing has become a widely recognized philosophy that aims at

Lean Manufacturing and Operational Efficiency of Nestle Nigeria Plc. Using Data Envelopment

Analysis (DEA)


reducing waste and non-value added activities to improve performance in cost-efficiency, conformance quality,

productivity and reduce inventory levels and throughput times (Deflorin & Scherrer-Rathje, 2012). Therefore, a

lean manufacturing system is a manufacturing system that aims at achieving more with less in such a way that

value is optimized for the customer, organization, suppliers, and the society at large.

Manufacturing firms across the globe are faced with the challenge of managing waste and sustaining the

operational efficiency of their system. Openda (2013) assert that the operational performance of the

manufacturing or service sector is greatly affected by the manufacturing practices adopted which can either

result in strategic gain or strategic loss for the firm.

Studies have been inconclusive on how the lean system affects the operational efficiency of firms adopting it.

Several researchers have investigated the nexus between LMS and efficiency, especially in organizations that

are manufacturing based. Evidence obtained from extant reviews of literature pointed out a unanimous support

for the notion that lean manufacturing supports Manufacturing Efficiency. (Abioye and Bello, 2012, Okpala,

2013, Wince-Smith, Echevarria and Allen (2013), Karim, Alam & Amin, 2010, Enoch 2013, Moori, Pescamona

and Kimaru 2013). However, there were some dissenting opinion as seen in a case cited by Camuffo and

Volpato, 1995 where the organization in question had failed to appropriately implement the lean strategy which

led to a grave loss for the firm. Wamalwa, Onkware and Musiega (2014) also discovered no change in factory

time efficiency as a result of the introduction of the lean culture.

The Nigerian food and beverages industry of which Nestle Nigeria Plc is a major player, though touted as the

most stable in the manufacturing sector, have grappled with series of challenges that have negatively affected

the operational efficiency of the system. Statistics from the Central Bank statistical bulletin reveals that there has

been a consistent decline in the contribution of this subsector over the years to overall manufacturing GDP of

64.23%, 58.92%, 56.25%, 52.73% and 48.83%, 47.5%, 45.8% and 45.1% between 2010 and 2017 (CBN

Statistical Bulletin, 2014; NBS, 2017). This decline is apparently connected with the relegation of agriculture to

the background over the years by successive governments giving rise to a rural-urban drift which has placed a

strain on the infrastructure in the city, discouraged backward integration, and resulted in heavy dependence on

imported raw materials. This, coupled with the lack of modern technology, low application of innovation and

inefficient usage of available resources has put the Nigerian food and beverages industry in a very

uncompetitive situation (KPMG, 2014; FIIRO, 2012).

Fatunbarin (2014) outlined the challenges facing the food-producing plants in Nigeria to include over-

exploitation, natural enemies, anthropogenic influences, natural disasters and climate change which has posed a

serious source of waste particularly at the source of supply point. Heymans (2016) asserts that the biggest

obstacles the food and beverages processors have faced in terms of Lean manufacturing adoption to

performance optimization are lack of persistent and challenging leadership, lack of a clear vision of the future

and of what is possible to be achieved, failure to link the processes in kaizen with normal work which is often

seen as a separate program and not part of everyone's formal work, lack of patience and follow through, failure


Analysis (DEA)


to perceive that lean is a viable strategy to help achieve competitive advantage, failure to engage and involve

employees at all levels in the process from an early stage, and a lack of constant visibility by management on the

shop floor or Gemba. In line with the foregoing discussion, the study examines how the implementation of the

lean system by Nestle Nigeria Plc improved the operational efficiency of the system and how it contributed

reducing the slack and promoting the growth of the firm.

The remainder of the paper is organised as follows. Section 2 provides a review of relevant literature on the

subject of discuss. Section 3 gives an overview of the case study. Section 4 deals with the methodological

framework of the study. Section 5 describes the data used and relevant preliminary statistics. Section 6, 7 and 8

reports the result of the Data Envelopment Analysis and Section 9 concludes the paper.

2. Literature Review

2.1 The Concept of Lean Manufacturing

Lean manufacturing have been a subject of interest in production and operations management literature since the

pioneering research on Womack and Jones (1990) on the book ―The machine that Changed the world‖ The Lean

Manufacturing System (LMS) is a Japanese concept which started off initially with the work of Ford when he

first designed his production line for the model T- Ford but became known after the success story of Toyota

which led to the adoption of the Toyota Production system as an alternative name for the LMS (Womack

&Jones, 1996). The LMS was introduced as an alternative to mass production technique in the Toyota factory

which gave rise to increased productivity, improved quality, and greater flexibility, with minimum waste in the

production system. The implementation of lean practices involves using less of everything (raw materials,

labour, time and other resources) in an optimal manner to improve the production system (Cusumano, 1994;

Oliver, Delbridge, & Lowe, 1996; Womack & Jones, 1990).

Amin and Karim (2013) define LMS as a multi-dimensional approach that includes a variety of effective

manufacturing practices, such as Just-In-Time (JIT), Total Quality Management (TQM), standard work process,

work groups, manufacturing cells, total productive maintenance (TPM), and suppliers‘ involvement in an

integrated environment. Atkinson (2004) sees LMS as more than a mere concept. It is a complete methodology

that is aimed at achieving more with less. It is about carefully analyzing how best to achieve a given result with

the purpose of utilizing resources to their best advantage. The LMS is an operational strategy oriented toward

achieving the shortest possible cycle time by eliminating waste. It is an optimal way of producing goods through

the removal of waste and it is based on the application of five principles to guide management‘s action toward

success (Badurdeen, 2007).

Stevenson (2013) asserts that the ―Lean system‖ is both a philosophy and a methodology that focuses on

eliminating waste (non - value - added activities) and streamlining operations by closely coordinating all

activities. The Lean systems have three basic elements: They are demand driven, are focused on waste reduction

and have a culture that is dedicated to excellence and continuous improvement. The ultimate goal of a lean


Analysis (DEA)


system is to achieve a balanced and a smooth flow of operations with the following key benefits: reduced

inventory levels, high quality, flexibility, reduced lead times, increased productivity and equipment utilization,

reduced amount of scrap and rework and reduced space requirement. The building blocks of a lean production

system are product design, process design, personnel and organization, and manufacturing planning and control.

Kachru (2007) expanded the concept of Lean manufacturing by asserting that the lean system integrates the

routine work of producing and delivering products, services and information with problem identification and

process improvement. It is an extension of the supply chain concept based on a systematic elimination of

unproductive activities identified as wastes. Lean manufacturing is further seen as a philosophical and a team

based continuous process designed for the long-term maximization of company resources. The resounding and

overall principle of lean manufacturing is to minimize cost through continuous improvement that will ultimately

reduce the cost of services and products, thereby, increasing the profitability and competitiveness of firms

(Womack & Jones, 1990).

Mostafa, Dumrak, and Solten (2013) affirm that the current roadmap and framework existing for the selection of

lean strategy is grossly inadequate and responsible for the failure of the system. Anand and Kodali (2010) in

their study on ―Analysis of lean manufacturing framework‖ made an attempt to propose a new conceptual

framework for LMS which would resolve some of the limitations inherent in other frameworks. The framework

utilized 65 LMS elements which are categorized according to the decision levels and the role of internal

stakeholders in an organization although this framework is highly conceptual. The authors concluded that for the

productivity of a firm to be enhanced, the lean value stream mapping must be implemented by the firm that

wants to optimize performance.

Lehtinen and Torkko (2005) carried out a study on how the lean concept can be applied to a food-manufacturing

company. The study examined a contract manufacturer that has no product of its own with the aim of analyzing

how material and information flow within the company and its demand chains, in order to find best practices

and targets for further development. The effectiveness of internal material and information flow was studied by

using three value stream mapping tools: process-activity mapping, supply-chain response matrix, and demand

amplification mapping. The study reports that the lean concept is appropriate for food companies because it will

facilitate the analyzing and elimination of unnecessary inventories and other forms of waste along the supply

chain. The implementation of LMS by a food company can either increase customer value through cost

reduction or through provision of additional value-enhanced services such as shorter lead times.

2.2 Operational Efficiency

Perhaps, one of the most significant areas of gain in performance optimization for companies in the

manufacturing sector, and particularly in the Food and Beverage sub-sector that adopt and implement lean

manufacturing strategies would be in the area of operational efficiency.

While manufacturers may not be able to achieve the ideal of 100% efficiency, entities that have nipped their

inefficiencies in the LMS bud have proven to realize significant cost savings in terms of inventory, turnaround

times, and labor costs (Coelli, Prasada-Rao, O‘Donnell, Battese, 2005).


Analysis (DEA)


A paper by leading Accounting and Advisory firm, PwC (2015) holds that there is a significant opportunity for

waste and redundancy in the innovation, design, development, manufacturing, and testing phases of a product;

essentially at every stage of the product lifecycle leading to production.

The simple definition of manufacturing efficiency is to fulfill customer orders as quickly and reliably as possible

using the least amount of inventory and Work in Progress (WIP). However, efficiency goes a lot beyond that.

An overall efficient system requires paying attention to all areas of production; procurement, fabrication,

assembly, testing, packaging and distribution, and keeping in check the ‗non-essentials‘. In essence, a drive

towards efficiency in production systems requires paying attention to only what is essential, in order to eliminate

waste and redundancies. (Modi & Mishra, 2011).

Subramamiam, Husin, Yusop and Hamidon (2009) propose that factors contributing to manufacturing efficiency

are manpower utilization and machine efficiency, which enhances management‘s real time identification of

production faults and inadequacies through the analysis and interpretation of relevant production data in order to

improve manufacturing efficiency. The researchers posited that the following factors that affect the efficiency of

manufacturing lines as follows:

Figure 2.1.3: Factors Affecting Manufacturing Efficiency

Source : Subramamiam et al. (2009)

According to Ringen, Aschehoug, Holtskog, Ingvlasden (2014), one of the major factors which is more often

than not neglected by management, but could lead to significant normal and abnormal losses, reduce yield and

impact adversely on profitability is the efficiency of machines employed in the production process. As Koelsch

(2008) rightly put it, waste not on your machine, in order not to experience want on your bottom-line. A similar

concept, sometimes referred to as Overall Equipment Efficiency (OEE), quantifies how well a manufacturing

unit performs relative to its designed capacity, during periods it is scheduled to run (Scodanibbio, 2009).

Machine efficiency can certainly be improved if enough attention is paid to routine maintenance, to prevent

stoppages and downtimes that come with breakdown of machines. Subramamiam et al. (2009)

Production Line

Manpower Utilization

Supporting Department

Operators/Workers

Machine Efficiency


Analysis (DEA)


In the same vein, humans have been touted as the single most important element in the manufacturing process,

without which objectives of the organization would not be achieved. (Banjoko, 2012) Even in the age of semi-

automation and automation, the role of the human can still not be undermined in the aspects of

preventive/routine maintenance, production planning, scheduling, administrative and general management. The

odds are clear; manufacturing organizations need even humans to be efficient, in order to succeed. Manpower in

a manufacturing environment could be categorized into either worker /operator on the industrial shop floor, or

workers in the supporting departments, as pointed out Figure 2.1.3 (Subramamiam et al. 2009).

2.3 Nexus Between Lean Manufacturing and Operational Efficiency

Several researchers have investigated the nexus between LMS and efficiency, especially in organizations that

are manufacturing based. Evidence obtained from extant reviews of literature pointed out a unanimous support

for the notion that lean manufacturing supports Manufacturing Efficiency. (Abioye and Bello, 2012, Okpala,

2013, Wince-Smith, Echevarria and Allen (2013), Karim, Alam & Amin, 2010, Enoch 2013, Moori, Pescamona

and Kimaru 2013).

Further, Abioye and Bello (2012) echoed the importance of lean tools such as Teamwork and Kaizen in

boosting employee involvement and consequently morale. For them, taking ideas from shop-floor workers

during decision making, regular staff training, among others, result in increased employee morale and skills

which often boost production efficiency. Additionally, Tiwari, Turner, and Sackett, (2007) posit that there are

many lean tools and techniques which help manufacturing organizations to implement lean manufacturing

practices. They are interrelated in their ability to reduce cost through enhanced efficiency, which contributes to

their influence on operational performance. Inman and Green (2018) carried out a study on how the lean system

interrelates with green practices to affect both environmental and operational performance. It was discovered

that lean manufacturing practices are positively associated with environmental performance and operational

performances. In the same vein, green supply chain management practices are positively associated with

environmental performance and environmental performance positively affects operational performance. Ondiek

and Kisombe (2013) conducted a study on the adoption of LMS practices in some sugar processing factories in

Kenya. They discovered that some factories were rated as ―low to moderate‖ adopters of LMS and the degree of

implementation varied significantly among three categories of companies; government, public and private, their

regression analysis showed that few lean practices have significant impact on factory time efficiency dependent

on the extent of implementation of the practice.

However, there were some dissenting opinion as seen in a case cited by Camuffo and Volpato, 1995 where the

organization in question had failed to appropriately implement the lean strategy and this led to a complete

disruption of work and affected the efficiency of the system. Similarly, Wamalwa, Onkware and Musiega

(2014) carried out a research on the effects of Lean Manufacturing technology strategy implementation on

Factory Time Efficiency. The result showed evidence that there was no prominent benefit realized from factory

time efficiency as a result of the introduction of the lean culture, which greatly affected the profit of the

business. Womack and Jones (2005) state that focusing solely on manufacturing efficiency is not enough to

create long-term success for a business. Therefore, the objective is to build not just a ―lean organization‖ but

also ―lean solutions‖ to achieve long-term success. It is therefore, worth investigating how the LMS affects the


Analysis (DEA)


Evaluate

adding or

no value

adding

activities

efficiency of a firm. We therefore hypothesize that the LMS has no significant effect on the operational

efficiency of Nestle Nigeria Plc.

2.4 An overview of Nestle Nigeria Plc.

Nestle Nigeria Plc is a Nigeria-based food manufacturing and marketing company which was listed on the

Nigerian Stock Exchange on the 20th

of April 1979. The Company operates in two segments: Food and

Beverages. The Food segment includes the production and sale of Maggi, Cerelac, Nutrend, Nan, Lactogen and

Golden Morn. Beverages include the production and sale of Milo, Chocomilo, Nido, Nescafe and Nestle Pure

Life. The Company has a reputation for strong brand, excellent management and multinational backing which

ensure strong work force, large market share of seasoning and beverage market, product breadth and innovation,

excellent term of trade with distributors and suppliers, good profitability which ensures strong equity and

researches, strong cash flow, and adequate working capital. The major challenges encountered by this firm in

Nigeria are declining purchasing power, increased cost of production, threat of foreign (smuggled product),

inadequate power supply.

The vision of the company is to be a leading, competitive, Nutrition, Health and Wellness company delivering

improved shareholder value by being a preferred corporate citizen, preferred employer, and preferred supplier

selling preferred products. In pursuit of its mission, the company embarked on the adoption of the Lean

Thinking process in year 2008 with the introduction of the Nestle Continuous Excellence (NCE) initiative. This

initiative is an all- round focus on excellence beginning from their source of supply point to the factory floor and

the distribution of finished product stage which is tagged excellence ―from the fore to the fork‖. The goal of the

NCE is to become a lean enterprise. The objectives of this initiative are to: adopt a common model throughout

the company, place an emphasis on sustainability, make use of best practice, eliminate duplication which is a

major source of waste, enable learning from implementation, move beyond cost savings to consumers‘ delight,

gain competitive advantage and comply with excellence. The diagram below summarizes the NCE initiative and

strategy:

NCE

Figure 2.1.5: Nestle Continuous

Excellence (NCE) Initiative and Strategy

Source: Nestle Management Report, 2010

Engage People

Continually

improve the value

stream

Understand

value

Eliminate Non

adding activities


Analysis (DEA)


The NCE initiative has three major foundations. The first foundation is the Nestle Integrated Management

System (NIMS) which is aimed at ensuring the health and safety of their customers are a priority and protecting

the interest of their shareholders. The second stage is the Leadership Development stage that is centred on

pooling and developing existing talent through mentoring and coaching. The third stage is the Goal Alignment

stage where the company‘s and employees‘ goals are aligned. The three deliverables from the program are the

transformation of the workplace by creating a friendly environment, building capability of people through

training and development which will lead to a break through result.

The NCE initiative is built on two major strategies: The Total Productive Maintenance (TPM) strategy and the

Lean Strategy. The TPM has seven major Pillars which are: Autonomous maintenance, Planned maintenance,

Focused improvement, Education and Training, Early Management, Quality and safety. The Lean strategy on

the other hand has three pillars which are the Lean value stream, Lean office, and Lean design. The major lean

tools in use in this company are the Kanban, Keizen, Lean six sigma, 5s, Value stream mapping, DMAIC and

SMED.

The inception of the NCE programme was 2008 and it was fully implemented in 2009 with the following result

achieved: 30% reduction of customers‘ complaint, 9 % cost reduction, 90% efficiency productivity and zero

accidents. The NCE initiative was implemented in three hundred factories which has brought about a complete

change in employee‘s motivation resulting in overall 1.5 billion CHF savings and 5-6% organic sales growth.

However, the company is still posed with the challenges occurring as a result of waste of motion and machine

stoppages (Nestle Management Report 2016)

3.0 Methodological Framework

This is a case study analysis and the Data Envelopment Analysis (DEA) technique was employed to compute

the technical and scale efficiency for each Decision Making Unit (DMU). The DMUs in this research work are

the various years of comparison utilised from Nestle Nigeria Plc. comparing the company‘s pre-and post-lean

experience.

DEA is an advanced linear programming technique that converts multiple incommensurable inputs and outputs

of each DMU into a scaler measure of operational efficiency relative to its computing DMU‘s (Gullati & Kumar

2008). The authors further stated that DEA model assesses technical efficiency from two major perspectives,

which are: input-oriented technical efficiency which focuses on the possibility of reducing inputs to achieve a

given level of output and output-oriented technical efficiency which emphasizes on the possibility of expansion

in outputs for a given set of input quantities.

An input-oriented technical efficiency measure addresses the question: by how much can input quantities be

proportionally reduced without changing the output quantities produced?

Isocost

C1

A

x2`

x2

Q B1

c

P

P` q

X2/Y

B Isoquant


Analysis (DEA)


Figure 3.1 As an illustration, a production process employs two inputs X1 and X2 and produces one output Y.

QQ1, the isoquants, represents the efficient production frontier. Firm P in fig 2.4 utilised X1 and X2 units

respectively of input X to produce quantity q (on the frontier) For P to be efficient it must reduce input

consumption to XI1 and X2

1 and produce the same quantity q of the output Y. Where the inputs are reduced

proportionally holding the output constant, the technical efficiency (Te) of firm P is given as OP1/OP. This

indicates that the input consumption could be reduced by a proportion equal to OP1/OP. This will demand

reducing X1 down to X11 and X2 toX2

1.

In addition to technical efficiency, input costs can also be considered in effort to determine overall performance

of the firm under investigation. Line BB1 is the isocost line depicting the various combinations of the two inputs

that have the same total cost. In fig 2.4 the isocost line BB1 is tangential to the isoquant QQ

1 at point A, the firm

at point A would have the best technical and allocative efficiency. Allocative efficiency reflects the ability of a

firm to use inputs in optimal proportion given their respective input prices. It refers to whether inputs, for a

given level of output and set of input prices are chosen to minimise the cost of production, assuming that the

organisation being examined is already fully technically efficient (Steering Committee for the Review of

Commonwealth/State Services Provision.1997).

On the other hand, the output oriented technical efficiency answers the question by how much can output

quantities be proportionally expanded without altering the input quantities used? This is an output oriented

measure of efficiency. This efficiency measurement examined the extent to which output produced can be

increased without an increase in input consumption. In figure 3.2 it is assumed that from a single input X two

outputs Y1 and Y

2 can be produced. AA

1 is the isoquant indicating that constant quantity of input used to

produce varying proportion of Y1 and Y

2. The isoquant depicts the best production possibilities and all firms‘

lies to the left and bottom of AA1. In fig 3.2 A is one of such firm and point R is the projection of firm A on to

the best production frontier, that is, AA1. Distance AR determines the amount of technical efficiency. Therefore,

output-oriented technical measure is given as OA/OR. Given the iso-revenue SS1 the allocative efficiency

becomes OR/OQ. Then the overall efficiency would be the product of the two efficiencies:

OA/OR X OR /OQ = OA/OQ

Y

2/X


Analysis (DEA)


Q

R

O

Figure 2.5: Output-Orientation

4.0 Data and Variables

To achieve the objectives of the study, we utilize two sets of variables (the input and output variables) which

was collated using an ex-post facto research design majorly obtained from the annual reports of the case study

between the period of 1994 and 2016. To determine the efficiency scores there is a need to select the relevant

input and output modelling the manufacturing sector behaviour. It is an established fact that the basic input in a

manufacturing concern are the 4M‘s (Man, Money, Material and Machine). It is in the light of this that the

researchers selected number of employees to capture Man, cost of sales to capture money and volume of

inventory to capture materials and the outputs are Turnover, Profit and Operating cash flow as shown below

A1 Y

1/X


Analysis (DEA)


Fig 4.1: Conceptual Model for the Study

Operational efficiency (DEA Analysis) Explanatory Analysis of efficiency (Tibit

Model)

Outcome

( Level Output)

4.1 Model and Variables

The Data envelopment analysis efficiency frontier software was used in analyzing the collated data. The DEA

searches for the input and output weights that maximize the performance of the firm(s) being analyzed. CCR

fractional program (Charnes et al. 1978)

Subject to

h0 = Efficiency score of DMU0

= Input variable i of DMUj

yrj = Output variable r of DMUj

n = Number of DMUs

= Weight for input variable i

Inventory

leanness

Employee

Leanness

Cost of

Sales

Leanness

Turnover

Production Process

1 2 3

OPERATIONAL

EFFICIENCY

Process indicator

……. efficiency

Profit Operating

Cash Flow

Inputs

Output


Analysis (DEA)


= Weight for output variable r

m = Number of input variables

t = Number of output variables

The model stated however, did not make provision for Slacks which is catered for in the Slack Based Measure

(SBM) of fractional program proposed by (Tone 2001) as stated below:

SBM fractional program (Tone 2001)

( ⁄ )

( ⁄ )

Subject to

where 𝜆, s-, s

+ ≥ 0

Notation:

Inputs of DMU0

y0 Outputs of DMU0

𝜆 Weights for DMUs

s-

and s+

: Slacks associated with inputs/outputs

m and s: Numbers of input/output variables

Inputs:

ijx =the amount of production resources (input) i used in production unit j .

Therefore, in this wise,

x j1= represents the volume of inventory available in production unit ― j ‖ per year.

x j2=represents the number of employees available in production unit ― j ‖ per year

x j3=represents the cost of sales incurred in production unit ― j ‖ per year

Outputs:

yrj

=the amount of output r generated in the production units j .

Therefore,


Analysis (DEA)


yj1 = the turnover in production unit j in a year.

yj2= the profit after tax of the production unit j in a year.

yj3 = the operating cash flow in the production unit j in a year.

j = number of production unit considered in the study.

i = number of inputs used by the production units

r = number of output generated by the production units

j = weights attached to the inputs used and outputs of each production unit.

S i

= slack variables attached to the input constraints.

S i

=slack variables attached to the output constraints.

Generally, since a Lean manufacturing system is aimed at reducing cost through the minimization of waste, the

Input minimizing model, which the lean system advocates, was adopted for the study:

Min

Subject to:

3

1 51

j jj

x x

- Inventory Constraints

3

2 101

j jj

x x

- Employee Constraints

3

3 151

j jj

x x

- Cost of Sales Constraints.

Output Constraints

3

1 51

j jj

y y

- Turnover constraints

3

2 101

j jj

y y

- Profit constraints

3

3 151

j jj

yx

- Cash Flow constraints

3

1

1j

j

- Scale Constraints (VRS)


Analysis (DEA)


0 j, 1,2,...,21

jj - Non-negativity Constraints

However, to achieve movement to the efficient frontier in a (there is) a two stage DEA the need to optimize the

slack variables. This required running the model under the same assumption as in the basic DEA model.

Max 1 2 3 1 2 3S S S S S S

Subject to:

3

1 1 51

j jj

x S x

- Inventory Constraints

3

2 2 101

j jj

x S x

- Employee Constraints

3

3 3 151

j jj

x S x

- Cost of Sale Constraints

Output

3

11 101

j jj

y yS

- Turnover constraint

3

22 151

j jj

y yS

- Profit constraint

3

33 201

j jj

y yS

- Cash Flow constraint

3

1

1

0 ,( 1,2...9)

jj

jjj

Scales VRS

3

1

1j

j


Analysis (DEA)


5.0 Empirical Results

5.1 Descriptive Statistics

The summary statistics of the variables of interest is presented in table 5.1. The table in essence provides the

descriptive statistics of the variables employed as input and output parameters in the study‘s model. In addition,

the table is intended to provide a general description of the input resources and outputs of the production unit

adopted as sample (Nestle Nigeria, Plc.).

Table 5.1 Descriptive Statistics of Input Resources and Output

Source: Computed from data obtained from the Annual Reports of Nestle Nigeria Plc. between 1994 and

2014

Table 5.1 shows that on the average Nestle employed about 1518 employees for the period under consideration.

The total inventory held by the company is #4926623(in thousands of naira) while the average cost of sales

incurred is #26360604 (in thousands of naira). However, the result shows that Nestle performance in terms of

turnover, profit after tax and operating cash flow are #44736270, # 7113722 and #9093361 respectively.

The minimum and maximum level of input indicates that expansion or otherwise of the production activities of

the firm. All the variables adopted in the study (Number of employees(NOE), Total Inventory (TOTINV), Cost

of Sales(COSA), Turnover(TO), Profit After Tax(PAT) and Operating Cash Flow(OCF) exhibited a positive

skewness

The distribution is Platykurtic in nature because most of the coefficient of kurtosis is less than 3 except for OCF

that showed a leptokurtic distribution which is heavily tailed.

The company‘s distribution exhibits a normally distributed series based on the Jarque berra probability which

shows no statistical significance at 5% level of significance except for the OCF.

Nestle NOE TOTINV COSA TO PAT OCF

Mean 1518.810 4926623. 26360604 44736270 7113722. 9093361.

Median 1332.000 4585073. 18137513 28461078 3835493. 5576221.

Maximum 2288.000 10956010 82099051 1.43E+08 22258279 36209580

Minimum 1050.000 441832.0 1510030. 2358483. 220763.0 -85141.00

Std. Dev. 463.4208 3568121. 25588519 45007337 7493973. 10312687

Skewness 0.581196 0.435314 0.950118 0.994926 1.078706 1.408054

Kurtosis 1.657788 1.749792 2.644619 2.690283 2.744569 3.871562

Jarque-Bera 2.758602 2.030888 3.270042 3.548505 4.129715 7.603824

Probability 0.251754 0.362242 0.194948 0.169610 0.126836 0.022328

Observations 21 21 21 21 21 21


Analysis (DEA)


5.2 Model Results

Technical Efficiency Scores of the Decision Making Unit

In DEA literatures (Farrell and Fieldhouse, 1962; Charmer and Charmes, 1978; Tone, 2001; and Ray, 2004)

constant returns to scale (CRS) model assumes a production process in which the optimal mix of inputs and

outputs is independent of the scale of operations. However, in this study we anticipate and considered it more

realistic that the firm‘s size and operations are more likely to be influenced by institutional or environmental

constraints and not only by the market forces. Thus, we considered the assumptions of constant returns to scale

to be more tenuous. Consequently, the less restrictive variable returns to scale assumption is specified and

estimated below. The estimated efficiency scores on the strength of the variable returns to scale assumption are

presented in Table 5.

Table 5.2.1: Results of VRS and CRS Model: Pure Technical Efficiency – Nestle Nig. Plc

Year VRS CRS

1994 1.00000 1.00000

1995 1.00000 0.84571

1996 1.00000 1.00000

1997 0.97796 0.94716

1998 1.00000 0.94716

1999 1.00000 0.94755

2000 0.94670 0.87626

2001 1.00000 0.94309

2002 0.98764 0.89482

2003 0.96109 0.89265

2004 0.95079 0.88913

2005 0.94414 0.91489

2006 0.93037 0.91072

2007 0.90541 0.88995

2008 0.93566 0.93313

2009 0.96170 0.96096

2010 1.00000 1.00000

2011 0.97242 0.97236

2012 1.00000 1.00000

2013 1.00000 1.00000

2014 1.00000 1.00000

2015 1.00000 1.00000

2016 0.98501 0.97562

Source: Researcher’s estimate from VRS model, 2017

From the 23 years‘ survey of the VRS model conducted, Nestle Nig. Plc was deemed to be operating

inefficiently for 12 years representing 52% relative to the other years. The average scores of the inefficient years

(n=12) is 95%. This overall operational efficiency value of Nestle Nig. Plc shows that the company is only 5%

away from the optimal usage of their input resources.

It can also be deduced from the technical efficiency rate on Table 5.2.1 that the implementation of the lean

system has improved the operational efficiency of the firm. If the company fully implemented the lean system in

the year 2008 as explained in the company‘s profile a deconstruction of five years before the lean system was

implemented showed that the operational efficiency of the firm was below optimality as seen from the VRS

result. However, after the implementation of the lean system in 2008. Nestle Nig. Plc attained optimality in five


Analysis (DEA)


years (2010, 2012, 2013, 2014 and 2015) which implies that that implementation of the lean system must have

enhanced their operational efficiency.

On the other hand, the CRS model measures total efficiency with strong disposability of outputs; that is, all

inputs are desirably considered. Under this assumption Nestle Nig. Plc was found to be operating efficiently for

seven (7) years (1994, 1996, 2010, 2012, 2013, 2014 and 2015) out of the 23years period considered for the

study of which five of the years occurred after the implementation of the lean system. However, as explained

earlier the VRS is more applicable for this study and will be the focus of the analysis because it takes a more

realistic view of the Decision- Making Unit that employs factors of production as its input which are subject to

change due to a given increase in size (Scale).

However, to facilitate ready inter year comparison of the efficiency scores for each of the DMUs, the VRS

model efficiency estimates is depicted in a bar graph in figure 5.2. The graph indicates that while some of the

years witnessed positive changes in efficiency and were consistently efficient some remain in the realm of

inefficiency in the year sampled. The downward adjustment of the efficiency level demands some managerial

actions in order to ensure optimal and efficient usage of resource input by the DMUs.

Figure 5.2: Comparative Graph of VRS and CRS

5.3 Scale Efficiency Characteristics of the Companies (DMU’s)

The need to provide further insight into the impact of the firm size on efficiency motivated the scale efficiency

tests. Scale efficiency tests indicate that a firm may be operating at activity levels that contribute to higher than

minimum average costs or most productive scale size. The implication is that while some firms could be

operating at too large a scale to maximize the productivity of their inputs, other firms may appear to be too small

and, therefore, exhibiting higher average costs. Table 5.3.1 contains the summary result of individual firm scale

efficiency score.

0.75

0.8

0.85

0.9

0.95

1

1.05

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Comparative Result of the VRS and CRS model

VRS CRS


Analysis (DEA)


Table 5.3.1: Scale of Efficiency’ Score for the Years of Nestle Nigeria Plc.

S/N YEAR SCALE EFFICIENCY SCORE TYPE OF SCALE

1 1994 1 CRS

2 1995 0.85 IRS

3 1996 1 CRS

4 1997 0.95 IRS

5 1998 0.95 IRS

6 1999 0.95 IRS

7 2000 0.88 IRS

8 2001 0.94 IRS

9 2002 0.89 IRS

10 2003 0.89 IRS

11 2004 0.89 IRS

12 2005 0.91 IRS

13 2006 0.91 IRS

14 2007 0.89 IRS

15 2008 0.93 IRS

16 2009 0.96 IRS

17 2010 1 CRS

18 2011 0.97 IRS

19 2012 1 CRS

20 2013 1 CRS

21 2014 1 CRS

22 2015 1 CRS

23 2016 0.98 IRS

Source: Researcher Estimates from DEA VRS model, 2017

IRS—Increasing Returns to Scale, CRS-------- Constant Returns to Scale

The years with a higher scale efficiency scores have less input wastes attributable to their size. The comparison

of the scale efficiency scores of these DMUs shows that out of the 23 years sampled, Nestle Nigeria showed

seven (7) years (1994, 1996, 2010, 2012, 2013, 2014 and 2015) of no scale inefficiency of which five occurred

after the implementation of the lean system. This implies that, sixteen (16) years which is approximately 69.6%

of the sampled years for the firm are scale inefficient. The key performance index reveals that a manufacturing

concern with 90% production process under control is operating at optimal level provided that the normal loss

does not exceed 10% (KPMG,2014 & Global food index, 2015). Taking a closer look at Nestle, the evidences

show that all the years with scale inefficiency had about 80% efficiency on the average which is quite far away

from the optimal threshold level. However, after the adoption of lean manufacturing in 2008, the firm shows

five (5) years of 100% efficiency and three (3) of the years that showed a scale inefficiency still revealed an

operational efficiency level of 97%, which is very close to optimality based on the DEA scale. However, based

on the KPI index optimality is attained.


Analysis (DEA)


In analyzing the company‘s efficiency scores, the nature of scale inefficiency for the company is clearly

indicated. The result of this analysis as shown in columns 4 of table 5.3.1 tells the pattern of scale efficiency for

the DMUs. Nestle Nigeria Plc. indicates that about 69.6% of the years examined showed increasing returns to

scale (IRS) while 30.4% showed constant returns to scale (CRS); it is noteworthy that companies operating

under constant returns to scale have no scale inefficiency. It can therefore be said that Nestle Nigeria did not

operate under the most productive scale size for 69.6% of the years considered. However, after the adoption of

the lean manufacturing system in 2008, it is obvious that Nestle Nigeria Plc. made a tremendous progress in

their size and capacity utilization reflected in their movement from increasing returns to scale (IRS) to a

constant returns (CRS) to scale, that showed no scale inefficiency.

5.4 Production Input Resources Reduction and Output Increase for the Inefficient

Companies

The second stage data analysis model (slacks model) allows for the analysis and determination of the input and

output slacks for the DMU‘s. These slacks s+, s

- indicate the magnitude by which specific input resources in

each of the inefficient company ought to be reduced or its output increased, that is the turnover, operating cash

flow and the profit after tax can be increased for the companies to attain efficiency in its operations. The

magnitude of production resources input reduction or output expansion as well as the preferred target inputs to

make the less efficient firms obtain optimality is shown in table 5.3.2

Table 5.3.2: Result of 2nd Stage DEA Analysis

Years

Input Slack Output slack

CS (N000) TI(N000) NOE(N000) TO (N000) PAT(N000) OCF(N000)

1994 0 0 0 0 0 0

1995 0 0 0 0 0 0

1996 0 0 0 0 0 0

1997 0 415721 0 151606 302316 0

1998 0 0 0 0 0 0

1999 0 0 0 0 0 0

2000 0 0 0 0 0 319772

2001 0 0 0 0 0 0

2002 0 676190 0 0 0 754555

2003 0 1375901 0 0 0 0

2004 0 240090 0 0 0 0

2005 0 934817 0 0 0 0

2006 0 1486606 0 0 362216 0

2007 0 339118 0 0 1373907 236168

2008 0 720052 0 0 0 5118107

2009 0 3146336 0 0 683625 809187


Analysis (DEA)


2010 0 0 0 0 0 0

2011 0 1319577 0 0 0 2324674

2012 0 0 0 0 0 0

2013 0 0 0 0 0 0

2014 0 0 0 0 0 0

2015 0 0 0 0 0 0

2016 0 923124 0 0 0 354327

Source: Researchers estimates from Slack model, 2018


Analysis (DEA)


Table 5.3.3: Result of 2nd Stage DEA Analysis

Years

Input Target Output Target

CS (N000) TI(N000) NOE(N000) TO (N000) PAT(N000) OCF(N000)

1994 1510030 441832 1332 2358483 220763 747865

1995 2962804 1346950 1050 4458175 612828 100000

1996 3614616 1264571 1141 6128414 1284113 1473932

1997 3163354 1131187 1163 5255932.2 1012477 1158869

1998 4059786 1063153 1131 6187462 801829 1850113

1999 4643236 1494369 1080 7724503 1250550 1962636

2000 6112568 1816433 1076 10027714 1605183 2081485

2001 8541723 2312720 1090 14146932 2526450 2829028

2002 12204991 2241610 1071 19578894 3174080 3875827

2003 14952595 3030758 1119 24631949 3804114 4967270

2004 17244901 3398745 1143 28461078 3835493 6466448

2005 19936242 3440269 1292 34335891 5303128 6296591

2006 22065919 3820983 1388 38422782 6022545 7172906

2007 25174976 4392382 1472 44027525 6815806 8032173

2008 29286713 5282345 1664 51742302 8331599 10694328

2009 38426416 7141510 1960 68317303 10467203 12729276

2010 46495387 8494039 2113 82726229 12602109 15348315

2011 55591743 8309520 2108 97961260 16808764 22972677

2012 66538762 8784909 2179 116707394 21137275 30243832

2013 76298147 9853893 2288 133084076 22258279 36209580

2014 82099051 10956010 2245 143328982 22235640 23495038

2015 83925957 10813960 2356 151271526 23736777 39877436

2016 106583385 20637750 2325 181910977 7924968 61484847

Source: Researchers estimates from Slack model, 2018

Apparently cost of sales and number of employees were optimally utilized by the firm such that no reduction in

the amount is required to achieve efficient operations for all the years considered. However, there is a need for

the volume of inventory to scaled down by the volume given in the slack Table (Table 5.3.2) for the affected

years. Nestle ought to have scaled down their volume of inventory for ten years, For example, in 1997, 2002-

2009 and 2011 the firm ought to have scaled down their stock level by the values of slacks as seen in Table

5.3.2 to maintain an optimal inventory level which will result in an increase output by the expansion values seen

in column 5, 6 and 7 of Table 5.3.2 that would have brought the firm to an optimal operational efficiency level.

The target input and output table shows the optimal combination unit of the input resources required to attain the

optimal output target that will result in the operational efficiency of the firm.


Analysis (DEA)


Evidently, the implementation of the lean manufacturing system by the company has improved their operational

efficiency as the slack recorded in the periods after the implementation of the lean system was very minimal.

Furthermore, the computation of the magnitude of inefficiencies at the various years provides a useful

managerial insight into the weakest year of performance. And, with this information policy makers and

administrators can proactively take decisions on which input waste must have been responsible for the sub-

optimal result which will invariably improve the operational efficiency of the system.

5.5 Benchmarks or Peers for the DMU’s

The DEA model allows for comparison amongst the Decision-Making Units (DMU‘s) and permit selection of

benchmark facilities and ‗role models‘. A DMU is a benchmark for other if at the optimal value of Ф* the

weight λ*≠0 for the benchmarking decision making unit (Zhu, 2009). The non-zero optimal λj* represent the

benchmark for a specific decision making unit under evaluation. The benchmarks, consequently, is the role

model against which the facilities under evaluation can compare its operations and emulate in other to become

an efficient unit.

Maghary and Lahdelma (1995) suggested that it is worth identifying the number of times that an efficient DMU

acts as peers for the inefficient ones.

This approach enables us to classify the DMUs as either self-evaluator, that is, those that are not peers or

benchmark for other ones; and active comparators (Afzali,2007). Table 5.5.1 contains the benchmarks analysis

of the DMUs and the number of times each efficient DMU serves as benchmark for others. DEA frontier

identifies the companies which have been referenced with each company thereby facilitating comparison.

Table 5.5.1: Peer count and Benchmark Years/Company

S/N YEARS PEER AND BENCHMARK YEAR/ COMPANY NO OF TIMES

REF.

1 Nestle Nig 1994 NESTLE Nig 1994 10



4 Nestle Nig 1997 NESTLE 94, NESTLE 95 and NESTLE 96 0



7 Nestle Nig 2000 NESTLE Nig 1995,NESTLE Nig 1999, NESTLE Nig

2001, NESTLE Nig 2010 0


9 Nestle Nig 2002 NESTLE Nig 2001, NESTLE Nig 2010, Nestle Nig 2015 0

10 Nestle Nig 2003 NESTLE Nig 2001, NESTLE Nig 2012 0

11 Nestle Nig 2004 Nestle Nig 99, Nestle Nig 2001, Nestle Nig 2013 Nestle

Nig 2010, Nestle Nig 2012 0




Analysis (DEA)




14 Nestle Nig 2007 Nestle Nig 99, Nestle Nig 2010, Nestle Nig 2014 0

15 Nestle Nig 2008 Nestle Nig 96,Nestle 99 Nestle Nig 2010, Nestle Nig 2012

Nestle Nig 2015 0

16 Nestle Nig 2009 Nestle Nig 96, Nestle Nig 99 0

17 Nestle Nig 2010 Nestle Nig 2010 27

18 Nestle Nig 2011 Nestle Nig 96, Nestle Nig 2010, Nestle Nig 2012 0

19 Nestle Nig 2012 Nestle Nig2012 13


21 Nestle Nig 2014 Nestle Nig2014 10



Source: Researchers Estimates from Benchmark Analysis, 2016

Table 5.5.1indicates that one (1) of the efficient years, (Nestle 98), is a self-evaluator which indicates that it

needs to be excluded as it does not impact on the efficiency scores of other years in the series. Also from table

5.5.1, ten (10) of the years are reference years or role models for others. Nestle 2010 was referenced 27 times,

which is a period that occurs after the adoption of the lean manufacturing system. This result confirms the

tremendous success recorded by Nestle from the adoption of the Lean system. The benchmark analysis provides

a good basis for comparison of production and operating practices amongst similar firms or different years for

the same firm which can be helpful in improving the production process and operational efficiency of the

weaker years/weaker ones along the same value chain.

The graph in figure 5.5.1 depicts the years against their peer counts; years that are evaluators or role models for

others are indeed efficient, thus, removing them from the model will impact on the efficiency rating of the peer

group or other facilities.

0

5

10

15

20

25

30

BENCHMARK YEARS OF NESTLE NIGERIA PLC.


Analysis (DEA)


Figure 5.5.1 Benchmark Analysis of Nestle Nig. Plc.

6.0 HYPOTHESIS TESTING

Studies has been inconclusive on how the implementation of the lean system affects the operational efficiency

of the firm as established in the literature review. Therefore, we hypothesize that the

Lean Manufacturing System(LMS) has no significant effect on the operational efficiency of the sampled firm.

The LMS is measured using Money Leanness (Cost of sales), Material Leanness (Total Inventory) and

Manpower Leanness (Number of Employees). While the Operational Efficiency of the firm is captured using the

Data Envelopment Analysis Efficiency Score.

DEA ES 1 20 3COSA TOTINV NOE t

Where:

DEA ES= Data Envelopment Analysis Efficiency Score

COSA = Cost of Sales

TOTINV = Total Inventory

NOE = Number of Employees

β0= Constant term associated with the regression model

β1 = coefficient of cost of sales

β2 = coefficient of total inventory

β3 = coefficient of number of employees

The hypothesis was tested using OLS method of estimation OLS method of estimation using multiple regression

analysis. Table 6.1 shows the results of multiple regression analysis on this equation.

Cost of Sales (COSA)

Total Inventory (TOTINV)

Number of Employees

(NOE)

DEAES

β1 = -5.28E-09, 1.43E-08, 5.45E-10 (Before)

β1 = 5.99E-10, 4.50E-09, 1.53E-09 (After)

β2 =-3.86E-08, -1.10E-07, -4.58E-08 (Before)

β2 =1.11E-08, -9.30E-09, -3.62E-08 (After)

-3.86E-08

-0.000177

β3 =-0.000177, 0.000684, -0.000121 (Before)

β3 =-2.46E-05, -0.000248, -0.000157 (After)


Analysis (DEA)


Figure 6.1. Relationship between Efficiency Score and Lean Manufacturing

Table 6.1: Regression estimate of Nestle Nigeria Plc before and after lean manufacturing

Variables Before After

Coeff Std Error T-Stat Prob Coeff Std Error T-Stat Prob

C 1.263305 0.080037 15.784 0.0006 0.907245 0.084439 10.744 0.0017

COSA -5.28E-09 4.75E-09 -1.111 0.3476 5.99E-10 6.31E-10 0.949 0.4124

TOTINV -3.86E-08 1.75E-08 -2.206 0.1145 1.11E-08 4.72E-09 2.360 0.0994

NOE -0.000177 6.00E-05 -2.952 0.0599 -2.46E-05 7.16E-05 -0.343 0.7536

R2 0.876975 0.936100

Adjusted R2

0.753949 0.872200

F-Statistic 7.128396 14.64949

Prob (F-

Statistic) 0.070490

0.026890*

Dependent Variable: DEA ES *Significance level 0.05

Source: Researcher’s study, 2016

DEA ES 1 20 3COSA TOTINV NOE t

DEAES = 1.263305- 5.28COSA -3.86TOTINV–0.000177NOE Before

DEAES = 0.907245+ 5.99COSA + 1.11TOTINV–2.46NOE After

Interpretation of Result

The table 6.1 shows the multiple regression result of the effect of lean implementation measured by cost of sale

(COSA), total inventory (TOTINV) and number of employees (NOE) on Data Envelopment Analysis Efficiency

Score (DEAES) of Nestle Nigeria Plc before and after the implementation of the lean system. The result

indicates that for the period before lean, COSA, TOTINV and NOE have negative effect on DEAES. The period

after the implementation of the lean system shows that COSA and TOTINV have positive effect on DEAES,

while NOE has a negative effect on DEAES which implies that an increase in the number of employees can

cause a decline in efficiency as shown by the signs of the coefficients. The results are all in line with the a-priori


Analysis (DEA)


expectation except for the number of employee (NOE) coefficient that was still negative after the

implementation of the lean system. This implies that the employees may still be going through a learning

process or finding it hard adjusting to new ways of doing things that made the coefficient negative.

Also, the size of the coefficients shows that before the implementation of lean, ₦1 change in COSA and

TOTINV caused a 5.28% decrease and 3.86% decrease in DEAES respectively, while one employee added to

the workforce of Nestle also caused a 0.0001% decrease in DEAES. However, the size of the coefficients after

the implementation of lean shows that a ₦1 change in COSA and TOTINV caused a 5.99% increase and 1.11%

increase in DEAES respectively, while one employee added to the workforce of Nestle also caused a 2.46%

decrease in DEAES. The lean system advocates maintaining a lean workforce, that is why the employment of an

additional employee will reduce efficiency by 2.46%

Furthermore, the Adjusted R-squared reveals that about 75% variations in DEAES before the implementation of

lean can be attributed to the influence of all our explanatory variables while the remaining 25% variations in the

respective dependent variable were caused by other factors not included in this model. Also, the adjusted R-

squared for the period after the implementation of the lean system shows that about 87% variations in DEAES

can be attributed to the influence of all our explanatory variables while the remaining 13% variations in the

DEAES are caused by other factors not included in this model. This implies that the lean implementation

variables are more effective on the DEA score of Nestle Nigeria Plc.

The probability of the F-statistic of the models stood at 7% and 3% for the period before and after the

implementation of lean respectively. Implying that Cost of sales, total Inventory and Number of employees have

an insignificant effect on DEAES of Nestle Nig. Plc before the implementation of the lean system while after its

implementation, COSA, TOTINV, and NOE have a significant effect on DEAES.

Therefore, it can be deduced that the implementation of the lean system has contributed significantly in boosting

the operational efficiency of Nestle Nigeria Plc.

7.0 Conclusion and Limitation of the Study

This paper reports the results of an empirical investigation of how the implementation of the LMS affects the

operational efficiency of Nestle Nigeria Plc. using Data Envelopment Analysis. The result shows that a positive

and a significant relationship between the variables of interest. The P value before the implementation of the

lean system by the company was 0.11 which is statistically insignificant, while the P-value after the

implementation of LMS showed a significant effect with of (0.026). It was also noticed that year 2010 was a

DMU that was very strategic for the company as it was referenced twenty- seven (27) times which serves as a

benchmark for other years.. The operational efficiency of the company after the adoption of the Lean system

witnessed an improvement particularly as the average scores of the inefficient years for Nestle Nigeria increased

to 95% as against the initial 80% average which implies that the company was only 5% away from the optimal

usage of their input resources, while the overall average of the operational efficiency score stood at 98% after

the implementation of the lean system as against the above 90% experienced seven years before the

implementation of LMS . It is therefore recommended that the Nestle Nigeria Plc and others along the same


Analysis (DEA)


value chain should seek to become a lean enterprise by applying more lean tools in order to improve the

operational efficiency and optimise the performance of the existing system. The study of just Nestle may not

provide a good basis for generalisation so the study can be extended to other companies along the same value

chain which will aid comparison of result thereby providing a good basis for judgement

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Lean Manufacturing and Operational Efficiency of Nestle ......lean manufacturing system is a manufacturing system that aims at achieving more with less in such a way that value is

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