iorms.comiorms.com/march2018events/uploads/2018proceedings/iorms wor… · web viewlean...
TRANSCRIPT
LEAN MANUFACTURING AND OPERATIONAL EFFICIENCY OF NESTLE NIGERIA PLC. USING DATA ENVELOPMENT ANALYSIS (DEA)
AMOS, Nneoma BenitaBusiness Administration & Marketing, Babcock University, Ilishan Remo, Ogun state, Nigeria.
ADEBOLA, Solomon AjayiVice chancellor’s office, Adeleke University, Ede, Osun State, Nigeria.
ASIKHIA, Ubaisifo OlalekanBusiness Administration & Marketing, Babcock University, Ilishan Remo, Ogun state, Nigeria.
ABIODUN, JoachimBusiness 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 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. It is also observed that the company attained a 100% optimal efficiency for five years (2010, 2012, 2013, 2014 and 2015) as against only one year (2001) in the period before the adoption of the lean system. 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.
1 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 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 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 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).
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
Production Line
Manpower Utilization
Supporting Department Operators/Workers
Machine Efficiency
maintenance, to prevent stoppages and downtimes that come with breakdown of machines.
Subramamiam et al. (2009)
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 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
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
Engage People
Continually improve the value stream
Understand value
Evaluate adding or no value
adding activities
Eliminate Non adding activities
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?
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
toX21.
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
IsoquantB
P”
X2/Y
Figure 3.1Isoquant: Input-Orientation
B`B1`
Q`q
P`
P
c
B1
Q
x2
x2`
A
x1x1`O X1/Y
C1
Isocost
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 QQ1 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 Y2 can be produced. AA1 is the
isoquant indicating that constant quantity of input used to produce varying proportion of Y1 and
Y2. 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
Q
R
Q1
S1
A
Y2/X
A
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
Fig 4.1: Conceptual Model for the Study
Operating Cash FlowProfit
Process indicator ……. efficiency
OPERATIONAL EFFICIENCY
Production Process
1 2 3
Turnover
Cost of Sales
Leanness
Employee Leanness
Inventory leanness
Y1/XA1
Inputs
Output
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)
Max h0=∑r=1
t U r yr 0
∑i=1m νi x i0
Subject to
∑r=1t U r yrj
∑i=1m ν i χ ij
≤1 , j=1,2,………, n
h0 = Efficiency score of DMU0
χ ij = Input variable i of DMUj
yrj = Output variable r of DMUj
n = Number of DMUs
νi= Weight for input variable i
U r = 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)
Min ρ=1−( 1m
)∑i=1m si
−¿ / χ i0
1+( 1s
)∑r=1s si
+¿ / yr 0¿¿
Subject to
x0=Xλ+s−¿¿
y0=Yλ−s+¿ ¿
where 𝜆, s-, s+ ≥ 0
Notation:
x0 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:
=the amount of production resources (input) used in production unit .
Therefore, in this wise,
x 1 j= represents the volume of inventory available in production unit “ ” per year.
x 2 j=represents the number of employees available in production unit “ ” per year
x 3 j =represents the cost of sales incurred in production unit “ ” per year
Outputs:
y rj =the amount of output r generated in the production units .
Therefore,
y 1 j = the turnover in production unit in a year.
y 2 j = the profit after tax of the production unit in a year.
y 3 j = the operating cash flow in the production unit in a year.
j = number of production unit considered in the study.
= number of inputs used by the production units
= number of output generated by the production units
λ 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 θ
Subject to:
- Inventory Constraints
- Employee Constraints
- Cost of Sales Constraints.
Output Constraints
- Turnover constraints
- Profit constraints
- Cash Flow constraints
- Scale Constraints (VRS)
λ j≥0 - 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
Subject to:
- Inventory Constraints
- Employee Constraints
- Cost of Sale Constraints
Output
- Turnover constraint
- Profit constraint
- Cash Flow constraint
Scales VRS
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
Nestle NOE TOTINV
COS
A TO PAT OCF
Mean 1518.810 4926623.
2636060
4 44736270 7113722. 9093361.
Median 1332.000 4585073.
1813751
3 28461078 3835493. 5576221.
Maximum 2288.000 10956010
8209905
1 1.43E+08
2225827
9 36209580
Minimum 1050.000 441832.0 1510030. 2358483. 220763.0 -85141.00
Std. Dev. 463.4208 3568121.
2558851
9 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
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.
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 5.2
Table 5.2.1: Results of VRS and CRS Model: Pure Technical Efficiency – Nestle Nig. Plc
Year VRS CRS1994 1.00000 1.000001995 1.00000 0.845711996 1.00000 1.000001997 0.97796 0.947161998 1.00000 0.947161999 1.00000 0.947552000 0.94670 0.876262001 1.00000 0.943092002 0.98764 0.894822003 0.96109 0.892652004 0.95079 0.889132005 0.94414 0.914892006 0.93037 0.910722007 0.90541 0.889952008 0.93566 0.933132009 0.96170 0.960962010 1.00000 1.000002011 0.97242 0.972362012 1.00000 1.000002013 1.00000 1.000002014 1.00000 1.000002015 1.00000 1.000002016 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 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.
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 20160.75
0.8
0.85
0.9
0.95
1
1.05
Comparative Result of the VRS and CRS model
VRS CRS
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.
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.
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
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
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.
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
2 Nestle Nig 1995 NESTLE Nig 1995 3
3 Nestle Nig 1996 NESTLE Nig 1996 22
4 Nestle Nig 1997 NESTLE 94, NESTLE 95 and NESTLE 96 0
5 Nestle Nig 1998 NESTLE Nig 1998 1
6 Nestle Nig 1999 NESTLE Nig 1999 16
7 Nestle Nig 2000
NESTLE Nig 1995,NESTLE Nig 1999, NESTLE Nig 2001, NESTLE Nig 2010 0
8 Nestle Nig 2001 NESTLE Nig 2001 6
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
12 Nestle Nig 2005
Nestle Nig 99, Nestle Nig 2001, Nestle Nig 2012 Nestle Nig 2014, Nestle Nig 2015 0
13 Nestle Nig 2006
Nestle Nig 99, Nestle Nig 2010, Nestle Nig 2012 Nestle Nig 2014, Nestle Nig 2015 0
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
20 Nestle Nig 2013 Nestle Nig 2013 7
21 Nestle Nig 2014 Nestle Nig2014 10
22 Nestle Nig 2015 Nestle Nig 2015 6
23 Nestle Nig 2016 Nestle Nig 2015 0
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.
Nestle N
ig 1994
Nestle N
ig 1995
Nestle N
ig 1996
Nestle N
ig 1997
Nestle N
ig 1998
Nestle N
ig 1999
Nestle N
ig 2000
Nestle N
ig 2001
Nestle N
ig 2002
Nestle N
ig 2003
Nestle N
ig 2004
Nestle N
ig 2005
Nestle N
ig 2006
Nestle N
ig 2007
Nestle N
ig 2008
Nestle N
ig 2009
Nestle N
ig 2010
Nestle N
ig 2011
Nestle N
ig 2012
Nestle N
ig 2013
Nestle N
ig 2014
Nestle N
ig 2015
Nestle N
ig 20160
5
10
15
20
25
30
BENCHMARK YEARS OF NESTLE NIGERIA PLC.
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
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)
β3 =-0.000177, 0.000684, -0.000121 (Before)β3 =-2.46E-05, -0.000248, -0.000157 (After)
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 AfterCoeff 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 0.0017COSA -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 0.7536R2 0.876975 0.936100Adjusted R2
0.753949 0.872200
F-Statistic 7.128396 14.64949Prob (F-Statistic) 0.070490
0.026890*
Dependent Variable: DEA ES *Significance level 0.05
Source: Researcher’s study, 2016
DEA ES
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 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 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
REFERENCES
Abioye, T. E., & Bello, E. I. (2012). A Review of Awareness and Implementation of Lean Manufacturing within Nigerian Small-Scale. Journal of Mechanics Engineering and Automation, 2(1), 374-380.
Afzali, H.H (2007) Efficiency of Hospitals owned by the Iranian Social Security Organisation: Measurement, and Remedial actions. Hospital PhD. Thesis University of Adelaide
Amin, M. A. & Karim, M. A. (2013). A time-based quantitative approach for selecting lean strategies for manufacturing organisations. International Journal of Production Research, 51(4), 1146–1167. doi: 10.1080/00207543.2012.693639
Anand, G., & Kodali, R. (2010). Analysis Of Lean Manufacturing Frameworks. Journal of Advanced Manufacturing Systems, 9(1), 1-30. doi: 10.1142/S0219686710001776
Atkinson, P. (2004). Creating and implementing Lean strategies Management. African Journal of Business Management, 48(2), 18-21.
Badurdeen, H. (2007). Lean Manufacturing Basics. Ebooks available at: http://www.leanmanufacturingconcepts.com
Banjoko, S. A., Iwuji, I. I., & Bagshaw, K. (2012). The Performance of the Nigerian Manufacturing Sector: A 52- Year Analysis of Growth and Retrogression (1960 -2012). Journal of Asian Business Strategy, 2(8), 177-191
Camuffo, A. & Volpato, G. (1995). The labour relations heritage and lean manufacturing at Fiat. The International Journal of Human Resource Management, 6(4), 795-824.
Charnes, A., Cooper, W. W., & Rhodes, E., (1978), “Measuring the efficiency of decision making units,” European Journal of Operational Research, 2(6), 429-444.
Coelli, T. J., Prasada-Rao, D. S., O’Donnell, C. J., & Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis (Second Edition), New York: Springer Science +
Business Media.Cusumano, M. (1994). “The limits of lean”. Sloan Management Review , 35(4), 27-32.
Deflorina, P. & Scherrer-Rathje, M. (2012). Challenges in the transformation to lean production
from different manufacturing-process. International Journal of Production Research , 50
(14), 3956–3973.
Enoch, O. K. (2013, November 7). Lean Accounting and Lean Business Philosophy in Nigeria:
An Exploratory Research. International Journal of Economics, Finance and
Management, 2 (7): (ISSN 2307-2466), 508-516. Retrieved December 23, 2014, from
http://www.ejournalofbusiness.org
Farrell, M. J. & Fieldhouse, M. (1962). Estimating efficient production functions under increasing returns to scale. Journal of the Royal Statistical Society, 125, 252-267
Fatubarin, A. (2014). Thoughts on Food-Producing Plant Biodiversity of Nigeria. European Journal of Botany, Plant Science and Phytology, 1(1), 1-12
FIIRO (2012). Nigerian Food and Beverage Industry rising above the Storm. Leadership Online Version.
Grant, R. M. (2010) Contemporary Strategy Analysis, Seventh Edition. United Kingdom, John Wiley & Sons, Limited.
Gulati, R. & Kumar, S. (2008). An Examination of Technical, Pure Technical and Scale efficiencies in Indian Public Sector Banks using Data Envelopment Analysis. Economic Journal of Business and Economics 1(2): 33-69.
Heymans, B. (2016). Lean Manufacturing and the Food Industry. Continuous system improvement, 1-6. Retrieved from www.flowmakers.com
Inman, R. A. & Green, K.W. (2018). Lean and green combine to impact environmental and Operational Performance, International Journal of production Research, DOI: 10.1080/002075143.2018.1447705.
Kachru, U. (2007). Production and Operations Management: Text and Cases (1st ed.), New Delhi: Excel Books.
Karim, M. A., Alam, M. R., & Amin, M. A. (2010). Lean Manufacturing Strategy in a make-to- order Manufacturing Environment. ASOR Bulletin, 29(3), 34–47.
Koelsch, J.R (2008). Machine Efficiency = Energy Efficiency. Manufacturing Engineering Magazine. Accessed from http://www.sme.org/MEMagazine/Article.aspx?id=20210&taxid=1415. Retrieved on the 7th of August, 2016
KPMG, (2014). Manufacturing in Africa Report, 2014. KPMG Africa.
Lehtinen .U. and Torkko . M. (2005) The Lean concept in the food industry: A case study of a contract manufacturer. Journal of Food Distribution Research. 36 : 57–67.
Modi, S. B. & Mishra, S. (2011). What drives financial performance–resource efficiency or resource slack? Evidence from U.S. Based Manufacturing Firms from 1991 to 2006. Journal of Operations Management. 29: 254–273.
Moori, R. G., Pescarmona, A., Kimura, H. (2013). Lean Manufacturing and Business Performance in Brazilian Firms. Journal of Operations and Supply Chain Management, 6(1), 91-105.
Mostafa, S., Dumrak, J. & Soltan, H. (2013). A framework for lean manufacturing implementation. Production and Manufacturing Research: An Open Access Journal , 1: 44–64.
Okpala, K. E. (2013). Lean Six Sigma Methodologies and Organizational profitability: A Review of Manufacturing SMEs in Nigeria. American Journal of Industrial and Business Management, 3(1), 573-582.
Oliver, N. & Delbridge, R. J. (1996). Lean Production Practices: International Comparisons in the Auto Component industry. British Journal of Management, 7(1), 29-44.
Ondiek, G. O., Kisombe, S. M. (2013). A Survey on Adoption of Lean Manufacturing Tools and Techniques in Sugar Processing Industries in Kenya. Industrial Engineering Letters, 3(10): (ISSN 2225-0581), 92-105.
Openda, C. K. (2013). Lean Manufacturing Practices and Performance of organisations listed at the Nairobi Securities Exchange : An MBA thesis submitted to the Department of Management Science, University of Nairobi
Ray, S. (2007) “Are Some Indian Banks too Large? An Examination of Size Efficiency in Indian Banking.” Journal of Productivity Analysis 27(1): 41-56.
Scodanibbio, C. (2009). World-Class TPM - How to calculate Overall Equipment Effectiveness (OEE). Accessed from http://www.scodanibbio.com/site/access/e_courses/downloads/preview/oee_pw.pdf . Retrieved on the 10th of August,2016
Stevenson, W. (2013). Operations Management (11th ed.), USA, McGraw-Hill.
Subramaniam, S.K., Husin, S. H., Yusop, Y. & Hamidon, A. H. (2009). Machine efficiency and man power utilization on production lines. Proceedings of the 8th WSEAS Int. Conf. on Electronics, Hardware, Wireless and Optical Communications
Tiwari, A., Turner, C., & Sackett, P. (2007). A Framework for Implementing Cost and Quality Practices within Manufacturing. Journal of Manufacturing Technology Management. 18(6): 731-760.
Tone, K., (2001). A Slacks-Based Measure of efficiency in Data Envelopment Analysis. European Journal of Operational Research, 130, 498-509.
Wamalwa, M. S., Onkware, .K. & Musiega, D. (2014). Effects of Lean Manufacturing technology strategy implementation on Factory Time Efficiency, a case study of Mumias Sugar Company Limited in Kakamega County, Kenya. International Journal of Business
and Management Invention, 3(5): (ISSN (Online): 2319 – 8028, ISSN (Print): 2319 – 801X), 01-10.
Wince-Smith, D. L., Echevarria, J., Allen, S. R. (2013). Global Manufacturing Competitiveness Index. Deloitte, Council on Competitiveness.
Womack, J. P. & Jones, D. T. (1996). Lean Thinking: Banish Waste and Create Wealth for your Corporation. New York: Simon and Schuster.
Womack, J. P & Jones, D. T. (2005). Lean Solutions: How companies and customers can creater benefits and. New York: Free Press.
Womack, J. P., Jones, D. T. & Ross, D. (1990). The machine that changed the world: The Triumph of Lean. New York: Rawson Associates.