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371
International Journal of Supply and Operations Management
IJSOM
November 2014, Volume 1, Issue 3, pp. 371-391
ISSN-Print: 2383-1359
ISSN-Online: 2383-2525
www.ijsom.com
Analyzing the operations strategies of manufacturing firms using a hybrid
Grey DEA approach – A case of Fars Cement Companies in Iran
Mohamad Amin Kaviani a, Mehdi Abbasi *a
a Department of Industrial Engineering, College of Engineering, Shiraz branch, Islamic Azad
University, Shiraz, Iran
Abstract
In competitive markets, the operations strategies of companies are normally formulated based on
their competitive advantages. An effective operations strategy should maintain and improve
competitive advantages based on the capabilities of the corporate operations resources.
Considering the market requirements and the operational performance of the rivals is the key for
success and survival of a company in the competition. Therefore, recognizing where a company
stands in comparison with its rivals and adopting the appropriate operations strategy plays vital
roles in the success of companies. This paper proposes a method for comparing and ranking
operations strategies of companies based on the concept of efficient frontier using data
envelopment analysis (DEA) in grey environment. In the aforementioned method, DEA is used to
evaluate the efficiency of operations strategies of manufacturing firms. Also, grey theory is used
to support the uncertainty of the experts’ opinions regarding the inputs and outputs of the DEA
model. Then the respective units are ranked, and analyses are performed. The proposed approach
is applied for the entire nine cement factories of Fars Province in Iran, and the units are ranked,
respective analyses are presented regarding the efficient and inefficient units.
Keywords: Operations strategy; Competitive advantages; Performance objectives; Efficient
frontier; Prioritizing; Grey DEA.
* Corresponding author email address: abbasi_m@iaushiraz.ac.ir
Kaviani and Abbasi
372
1. Introduction
In the current competitive ambience of industries, managers’ strategic decisions are substantial for
the organizations in order to lead them toward their long-term goals. Formulating the operations
strategies by taking day-to-day experiences into consideration, leads to acquire sustainable
competitive advantages. Therefore, managers should provide and improve the competitive
advantages by making proper decision in the operations function. This entails a precise operations
strategy analysis of the firm in comparison with the rivals and the best practices. Operations
strategy is one of the most important factors in the business planning of every organization
(Beckman and Rosenfield, 2008). In a systematic market-based perspective, strategic decisions are
made according to the business circumstances and market requirements. Operations strategy is the
generic pattern of decisions which shape all types of operations capabilities in a long-term period
and their contribution to the overall strategy (Slack and Lewis, 2011). To maximize the efficiency
of the operations strategies, the managers should seek the continuous improvement of the
performance operations and simultaneously move in line with the business strategy of the
organization. Furthermore, operations strategy is inseparably linked to the concept of “competitive
advantage” (Chambers et al., 2004). Possessing sustainable competitive advantage to survive in
every competitive market is totally necessary for efficient enterprises. What is interpreted by the
sustainability of competitive advantage is the fact that competitive advantage should comprise
different processes from the rivals (the durability is industry-dependent) and be hard to copy,
replace or transfer. Competitive advantages, known as “performance objectives,” are divided into
the five categories of quality, cost, dependability, speed, and flexibility (Slack and Lewis, 2011).
Aforementioned performance objectives are used to rank and analyze the operations strategies of
the firms.
One of the important concepts in operations strategy literature is the concept of efficient frontier.
A company with a position on the efficient frontier, is an operationally efficient unit, moreover, it
ranks among the best practices of the industry. If the company fails to position on the efficient
frontier, it is not operationally efficient and should strive to reach the best practices and position
on the efficient frontier. It is important to point out a number of organizations that are not content
with the remaining on the efficient frontier, tend to push out the efficient frontier by redefining
market requirements through work on novel delights. (Chambers, 2004; Slack and Lewis, 2011).
On the other hand, DEA is a method for analyzing and measuring the relative efficiency of units
by having different inputs and outputs (Charnes et al., 1978). Wide applications of DEA in
various problems indicate the popularity of this method in measuring efficiency and productivity
(Jahanshahloo et al., 2013). Traditional DEA models are CCR and BCC or a combination of
these two models. The CCR (Charnes, Cooper and Rhodes) model was first presented by Charnes
et al. (1978) for measuring the efficiency of decision making units (DMUs) under constant return
to scale assumption. Then the BCC (Banker, Charnes, and Cooper) model was proposed by
Banker et al. (1984) by considering variable return to scale assumption. In the BCC model,
variable return to scale can be increasing, decreasing or constant. In addition, one of the important
subjects in DEA is the concept of “efficient frontier” (Korhonen, 2002). By using optimization
techniques, such as linear programming (LP), in which it specifies whether the respective
decision-making unit is on or within the efficient frontier, we intend to separate efficient and
inefficient units from each other (Seiford and Thrall, 1990).
Int J Supply Oper Manage (IJSOM)
373
The given studies on the circumstances governing the problem are caused to use BCC model in
this research. Also, due to existence of ambiguous information in experts’ opinions, grey DEA and
interval grey numbers are used to support the uncertainty. Grey numbers cover the weakness of
the BCC method regarding the uncertainty of input and output data and support the vagueness in
the experts’ opinions.
One of the goals of this research is to declare the concept of efficient frontier in operations
strategies particularly in DEA method. Operations strategy empowers companies to identify their
status in comparison with their rivals and make a major contradistinction in terms of utility. This
research is applied for Fars Province cement companies in Iran.
It should be noted that there is the assumption of the same products and market targets while the
efficiency of the operations strategies are investigated in the whole cement factories. The
innovation of this research is to develop a homologous and bilateral relationship between the two
concepts of “efficient frontier in operations strategy” and “DEA method and grey DEA.”
According to the author’s investigation, there has been no research in such aspect and by this, we
mean adapting DEA model in the operations strategy, consequently this is a completely novel,
noteworthy subject.
The rest of the paper is organized as follows: the second and third sections of the paper
respectively look into the related studies and methodical foundations of the research, The fourth
section roots around the research methodology and it’s execution in Fars Province cement
companies. The fifth section renders a conclusion and proposals for further researches.
2. Literature Review As Gardner (2004) discusses, an organization can be considered as a system. Operations strategy
fundamentals are based on strategic thinking and strategic thinking is totally related to system
perspective (Liedtka, 1998). System thinking focuses on the system as the whole and the
efficiency of the whole system is not optimized unless the efficiency of each subsystem is at
optimal point (Gardner, 2004). Thus, the operational efficiency, as a component of the whole
efficiency of an organization, effects on the overall performance of a firm and measuring the
operational efficiency helps the firm to maximize its overall efficiency. Therefore, optimal
operational efficiency of an organization can lead getting the high operational performance and
finally gaining the efficient operations strategy.
Numerous researches have been performed in the field of utilization of DEA method for
evaluating the operational efficiency of manufacturing and service enterprises. Braglia et al.
(2003) compared the relative efficiency of the steel industrial factories as well as the calculation
and rank of efficient units by means of the Anderson-Peterson technique. They also presented
managerial solutions for achieving the optimal status of operational strategy of the factories under
study. Talluri et al. (2003) used competitive advantages as inputs to the DEA model for comparing
the efficiency of 51 suppliers categorizing them into three groups using statistical analyses.
Employing a special model in DEA called super slack-based model, Düzakin E. and Düzakin H.
(2007) investigated the operational efficiency of 500 major efficient companies in Turkey. The
model which was used in their research was capable of ranking efficient units, therefore they
presented the rankings of the companies under this study. For comparing the operational
efficiency of European airports, Barros and Peypoch (2009) presented a two-level DEA model.
Kaviani and Abbasi
374
By selecting CCR model at the first level, they ranked airports, and at the second level, they
proposed methods for efficiency improvements in the airports that had low operational efficiency
using regression analysis and the “benchmark” concept in DEA.
Another research on evaluating operational efficiency was the one by Meenakumari et al. (2009)
who utilized two of CCR and BCC models for measuring the operational efficiency of 29
electronic public companies. With deliberation of inefficient units, they recommended the use of
potentials to improve the efficiency of inefficient units as well as efficient units to achieve the
desirable level of efficiency. Liu et al. (2010) used DEA to measure the operational efficiency of
the thermal power plants of Taiwan. Conducting return to scale of analysis in increasing,
decreasing, and constant modes, they stated a series of suggestions for the under-studied units to
become more efficient. Nath et al. (2010) examined the effects of operational and marketing
capabilities of organizations on their performance. In this research, DEA method was used and
financial and operational efficiency of the organizations were evaluated through the
resource-based view.
In particular Sun (2011) was investigated and ranked six among manufacturers in Taiwan’s
Science and Industry Park having used the DEA method and Malmquist productivity index. With
the Combination of DEA and performance matrix, Memon and Tahir (2012) who evaluated the
operational efficiency of 49 companies in Pakistan, consequently demonstrated that DEA results
can be also applied to the performance matrix. Moreover, with the combination of both DEA and
life cycle assessment, Iribarren et al. (2013) examined 29 wind farms based on their operational
performance. Kaviani et al. (2014) used the integrated approach of importance-performance
matrix and fuzzy AHP to prioritize the operations strategies of a number of cement companies in
Fars Province. They set as comparison criteria the five competitive advantages of quality, cost,
flexibility, delivery speed, and dependability. Selecting four competitive advantages as inputs to
the DEA model and two indices as model outputs, Bulak and Turkyilmaz (2014) evaluated the
efficiency of 744 production units from 10 sections of industry.
On the other hand, the applications of Grey DEA in efficiency measurement have been increased
significantly in recent years. For instance, by using the DEA model and grey relational analysis,
Wang et al. (2007) measured the efficiency of hospitals in China and the observed indicators were
reduced from 9 to 5. Wang et al. (2009) used a super efficiency grey DEA model to evaluate the
energy efficiency in China. The information from the period of 1995 and 2005 was used in the
aforesaid research.
The other examples are Shuai and Wu (2011) who used the DEA method and grey entropy to
evaluate the impact of internet marketing on the performance of Taiwan’s hotels. They
demonstrated the fact that operational efficiency and internet marketing have a direct relationship
with marketing efficiency effects. And Chen Y. and Chen B. (2011) who also exploited a
combination of grey DEA and Malmquist productivity index for evaluating the operational
performance of wafer fabrication industries in Taiwan. They examined efficiencies in the two
modes of efficiency in constant and variable return to scale, and consequently compared the
results. Lastly Le et al. (2014) used grey DEA for measuring the efficiency of the Vietnamese
garment industry.
The authors’ investigations corroborate that no study has been executed in analyzing and ranking
the operations strategy of manufacturing firms using the grey DEA approach. The current research
sought to fill this gap.
Int J Supply Oper Manage (IJSOM)
375
3. Theoretical Principles of the research
3.1. Operations Strategy
The responsibility of operations as a functional unit in organizations is to transform operations
resources into products and services (Brown et al. 2013). A review of the literature on operations
strategy shows that from the outset, the activity of the functional field of operations has not been
of interest as a factor that creates competitive advantage. The competitiveness of the work
environment drew greater attention to the decisions pertaining to operations. Skinner’s 1969 paper
titled “Manufacturing-missing link in corporate strategy” was a preamble to the introduction of
manufacturing and operations into the strategic decisions of companies.
Over the years, the concept of operations strategy has been defined in different ways: operations
strategy is an integrated pattern of decision making in operations that is linked to business strategy
(Hayes and Wheelwright, 1984); operations strategy is a general pattern for decisions that shape
the long-term capabilities of any kind of operations and the way they contribute to the overall
strategy through ongoing reconciliation of market requirements with operations resources (Slack
and Lewis, 2011). On the one hand, operations strategy meets the requirements determined by
business strategy and, on the other hand, it aids the organization by incorporating customer
requirements into operations capabilities which leads to introducing the company to new markets
and developing new opportunities for it (Beckman and Rosenfield, 2008). Paying attention to
these definitions demonstrates what they have in common is that operations strategy could be
developed through creating competitive advantage. In fact, the main role of operations strategy is
to convert the competitive advantages of organization to operations capabilities (Boyer and Lewis,
2002). According to the definition by Slack and Lewis (2011), operations strategy reconciles
operations resources to market requirements. This indicates two important viewpoints consist of
the market-based view (external) and the resource-based view (internal) in formulating operations
strategy of organizations.
3.1.1. Concept of efficient frontier in operations strategy
According to authors’ knowledge, the concept of operations strategy efficient frontier was first
introduced by Slack et al. (2006). Figure 1 shows the concept of efficient frontier in operations
strategy, and the relative performance of a number of organizations in the same industry may be
seen in it. Organizations A, B, C, and D are located on the efficient frontier curve. In the short
term time horizon, it is impossible for the organization to simultaneously achieve excellent
performance in all aspects of competitive advantages. Whereas, in the long term, the improvement
of the entire competitive advantages (performance objectives) is the goal of operations strategy.
The explanations are provided in this section are based on the two competitive advantages, variety
and cost efficiency.
The organizations that are located on the efficient frontier of their industry are on different
positions on the frontier compared to each other due to different marketing strategies and
managerial viewpoints. Organization X is inefficient in terms of operations due to failing to create
a trade-off between the two competitive advantages. Organizations B and X are on the same line.
However, because of better trade-off between the two performance objectives, Organization B is
placed on the efficient frontier like the best rivals, while Organization X is not on the efficient
Kaviani and Abbasi
376
frontier. The companies on the efficient frontier (the best practices in the industry) can improve
their operational efficiency by overcoming implicit trade-off on the efficient frontier curve.
Organizations not located on the efficient frontier should benchmark best practices of the industry
by redefining their operations strategy and following the organizations situated on the efficient
frontier (Slack and Lewis, 2008, 2011; Slack et al., 2006).
Figure 1- Concept of efficient frontier in operations strategy (Slack et al. 2006)
3.1.2. Competitive advantages and performance objectives
Competitive advantages are factors that enable an organization to compete and survive in a
competitive market (Slack and Lewis, 2008). Given the strategic role of operations, competitive
advantages are associated with organization performance (Slack and Lewis, 2011). Introducing
competitive advantages as “performance objectives,” competitive priorities link the operations
strategy of organizations with their performance. On the other hand, competitive advantages are
considered the achievable goals of the manufacturing section of the organizations. In the literature
of operations management, competitive advantage emphasizes on the strategic importance of
operations resulting in the achievement of sustainable competitive advantage. Supporting business
strategies in manufacturing firms are among other goals of competitive advantages (Bulak and
Turkyilmaz, 2014).
Slack and Lewis (2011) categorized performance objectives in the five groups of quality, cost,
speed, dependability, and flexibility. These five performance objectives illustrate competitive
advantages. The definitions of each of the five mentioned competitive advantages are as follows:
- Quality: Performing tasks properly, procuring goods and services without error and in
accordance with the previously-determined goals
- Speed: Performing tasks rapidly, minimizing the time between the customers’ request for
goods or services and the delivery
- Dependability: Carrying out the work in a timely manner, abiding by the delivery
commitments promised to the customers
Int J Supply Oper Manage (IJSOM)
377
- Flexibility: Changing what you do or the way the work is done, the ability to change or
match the activities of operations in order to overcome unexpected circumstances or
gain customers’ unique behavior or introducing new products or services
- Cost: Carrying out the work in an inexpensive manner, producing goods and rendering
services with a cost that enables them to properly perform pricing for the market in a
way that the organization revenue is also allowed for.
3.1.3. Hayes & Wheelwright Four stage model for demonstrating the strategic role of
operations
Hayes and Wheelwright (1985) introduced a four-stage model for operations strategies of
organizations where the operations capabilities of organizations was displayed from an internal
view and the strategic evaluation of rivals was put on display from an external view. They
demonstrated that operations strategy should create a vision in which the role of operations
resources in business is shown.
The first stage of their model is the internal indifference stage pertaining to organizations that
wish to merely solve their problems and are internally neutral. At this stage, the organization has a
reactive approach, and operations strategy is not known as a competitive advantage.
The second stage is external indifference comprising organizations that wish to keep abreast with
the rivals performing as good as their competitors. These organizations are externally neutral and
make use of the “benchmarking” strategy. Since organizations attempt to adopt best practices of
the industry at this stage, they cannot outperform them and will equal them at best. This stage is a
start to the creation of competitive advantage. However, in this stage, operation is not related to
business strategy.
The third stage is internally supportive pertaining to organizations that wish to be the best in their
own industry. In the efficient frontier model of operations strategy, these organizations are placed
on the efficient frontier and use the “supportive” strategy. At this stage, operations strategy is in
line with business strategy and supports it.
The fourth and the best stage is externally supportive pertaining to organizations that create needs
in the industry and pioneer in innovation and the creation of requirements and motivation in the
market. In the efficient frontier model of operations strategy, these organizations can push out the
efficient frontier. At this stage, organizations may perform superior compared to the best practices
of the industry.
In the present research, it is assumed, that none of the companies under study are at the first stage
of Hayes and Wheelwright’s model.
Kaviani and Abbasi
378
Hayes & Wheelwright, 1985 ) ( Figure 2- Hayes & Wheelwright Four stage model
3.2. Incorporating Grey theory in DEA model
3.2.1. DEA - BCC models
DEA is a powerful method for calculating the efficiency of decision-making units. The return to
scale structure is among the features of the DEA model. Return to scale may be constant or
variable (Aji and Hariga, 2013). The CCR model is among constant return to scale models. In
many organizations, a small decision-making unit cannot be compared with a larger
decision-making unit by multiplying its inputs and outputs by a constant factor. Hence, in such
organizations, constant return to scale does not hold. To resolve this defect, Banker et al. (1984)
invented a new model by making changes to the CCR model which is known as BCC given the
initials of their surnames. As mentioned earlier, the BCC model is used in light of the conditions
governing the problem.
and grey interval numbers 3.2.2. Grey systems theory
The grey systems theory, which is very similar to Zadeh’s fuzzy theory, was presented by Deng
(1982). Fuzzy mathematics deal with problems where the experts’ uncertainty is expressed by
membership functions (Zareinejad et al., 2014). If the information and data is inadequate, or there
are limitations to the sample volume and the membership function cannot be extracted, the grey
systems theory may be applicable. This theory is employed for solving vague problems and those
having uncertain data. Using a relatively small volume of information and great variability in
criteria, this theory yields satisfactory, desirable outputs. Grey theory, just like fuzzy theory, is an
effective mathematical programming model for solving unclear, vague problems (Deng, 1982).
Greyness in the grey region denotes incomplete information as well as uncertainty.
Each grey system is described by grey numbers, equations, and matrices where grey numbers are
the cells of the system. A grey number may be defined as a number with uncertain information
Int J Supply Oper Manage (IJSOM)
379
(Wang et al., 2009). For instance, the rank of criteria in a decision making is expressed as a
linguistic variable that can be expressed using number intervals. These number intervals include
uncertain data. It may also be stated that a grey number is a number which its precise value is
unknown, but the interval of its value is known. On the whole, a grey number is expressed by an
interval or a set of numbers. Assume that grey numbers exist as follows (Rahimnia et al., 2011;
Alparslan Gok et al., 2014):
dcdc
baba
,,
2
,,
1
In this case, addition, subtraction, multiplication, and division of two grey numbers ⊗1 and ⊗2 as
well as the inverse of each grey number are defined as follows:
Rkkbkak
cd
d
b
c
b
d
a
c
a
d
b
c
b
d
a
c
a
bdbcadacbdbcadac
ab
ab
cbda
ab
dcba
,,.
0,,,,max,,,,min
.
,,,max,,,,min
2
.
1
0,
1
,
1
1
,)
2
(
121
,
,
21
2
1
1
21
2
1
As noted earlier, given incomplete, ambiguous information in the experts’ opinions, interval grey
numbers are used in the present research.
3.2.3. Grey DEA model
DEA is a method for measuring efficiency that is capable of determining the relative efficiency of
the homogeneous set of decision-making units by having different inputs and outputs (Talluri et
al., 2003). For many DEA models, such as BCC, the data are clear and certain. However, in real
problems, uncertainty may exist. Since the BCC model works with certain data and cannot be
executed for problems involving uncertainty, new changes have been recently made to the DEA
method enabling this method to solve problems that involve uncertainty. In general, uncertainty in
the real world may be divided into the three types of stochastic, fuzzy, and grey (Yang, 1998).
Following the introduction of grey systems and logic in 1982 and the DEA model in 1978, Yang
(1998) introduced the hybrid Grey DEA model using grey interval numbers. He introduced the
grey CCR model by presenting upper and lower bound for the model’s interval efficiency.
Afterwards, various researchers have utilized Grey DEA in their works. Wang and Liu (2012)
presented a Grey CCR model under the data consistency condition. Their model calculates certain
limits in order to compute the upper and lower bound of interval efficiency. In their most recent
research based on grey Linear programming, Xia et al. (2014) put forward a method for DEA with
grey numbers. Using interval and conventional grey numbers, Xu and Zhou (2013) presented Grey
DEA for CCR and BCC. In their proposed Grey DEA model, DMUs were categorized in strongly
Kaviani and Abbasi
380
efficient, weakly efficient and inefficient.
4. Research Methodology
The operational efficiency of Fars Province cement companies was investigated from the
perspective of their operations strategy. Given research objectives, the stages of conducting the
research were as follows:
4.1. Step 1- Identifying DMUs and Determining Inputs and Outputs
The DMUs of this research were the entire cement companies of Fars Province comprising nine
factories named as DMU1 to DMU9 to preserve information and for the sake of confidentiality.
The information required for DEA inputs and outputs were gathered by direct interviews and
examining companies’ documentation through visiting the cement factories of Fars Province. To
determine inputs and outputs, the criteria were clarified by investigating relevant literature:
With regard to selecting DEA inputs, Talluri et al. (2003) considered five competitive advantages,
namely quality, cost, time, flexibility, and innovation to be the inputs to the DEA model. Bulak
and Turkyilmaz (2014) considered four competitive advantages, namely quality, delivery, cost and
flexibility to be the inputs to the DEA model. Sarmiento et al. (2007) also stated that competitive
advantages may be utilized as DEA inputs or outputs to estimate operational efficiency of
organizations. The five groups of competitive advantages or, in other words, the performance
objectives of Slack and Lewis (2008), namely quality, cost, flexibility, delivery and dependability,
were used as inputs to the DEA model.
With respect to outputs, it should be noted that efficiency could be measured by using financial
and non-financial indices (Bulak and Turkyilmaz, 2014). The most typical variables for financial
performance are return on investment (ROI) and return on assets (ROA) employed by Talluri et al.
(2003) as DEA model outputs using a 7-point Likert scale for measuring them. Return on equity,
return on sale, and net profit are among other financial indices. Market share was one of the
variables used for the non-financial performance. Bulak and Turkyilmaz (2014) used the market
share index as well as net profit margin as the DEA model outputs using a 5-point Likert scale for
measuring output indices. Growth of sale and employees, customer satisfaction, and brand
prestige were among other non-financial indices (Bulak and Turkyilmaz, 2014).
Two financial variables and one non-financial evaluative variable were used in order to account
for both financial and non-financial perspectives for increasing model validity. ROI and ROA are
the financial variables and market share (which is provided from yearly net sale) is the
non-financial variable as outputs for the Grey DEA model. Table 1 demonstrates input and output
variables.
Int J Supply Oper Manage (IJSOM)
381
Table 1- Input and Output Variables
Variable
Inputs
Variable
Outputs
x1
Quality
y1
ROA
x2
Cost
y2
ROI
x3
Dependability
Y3
Market share
x4
Flexibility
Input and Output Factors
x5
Speed
4.2. Step 2- Data collection for Inputs and Outputs
At this stage, to extract input data for DEA, linguistic variables were used as survey criteria by
conducting interviews with the members of the board of directors and the production managers of
each of the 9 cement factories of Fars Province and asking for their opinions about the 5
performance objectives pertaining to the factories’ operations strategy. It is considerable that
interviewed experts of studied companies preferred to explain their opinions for the situation of
inputs and outputs as poll criteria. These variables were converted into interval grey numbers. It is
noteworthy that it was presumed that people agree about the posed questions. The used scale is
shown in Table 2.
Table 2- Scale for Inputs (Li et al., 2007)
Scale
Vey high
(VH)
High
(H)
Medium
High
(MH)
Moderate
(M)
Medium
Low
(ML)
Low
(L)
Very Low
(VL)
Grey
interval
Number
[0.9,1]
[0.6,0.9]
[0.5,0.6]
[0.4,0.5]
[0.3,0.4]
[0.1,0.3]
[0,0.1]
The data pertaining to the inputs may be seen in Table 3.
Kaviani and Abbasi
382
Table 3- Interval Grey numbers for Inputs
Speed
(X5)
Flexibility
(X4)
Dependability