Global Journal of Management Studies and Researches, 1(3) 2014, Pages: 158-163
Academic Journals
Global Journal of Management Studies and Researches
ISSN 2345-6086
www.academicjournalscenter.org
Performance Evaluation of Green Supply Chain based on LFPP and Balanced Scorecard Approach Ali Mohaghar 1, Hossein Janatifar *2, Maedeh Dehghan 3 1 Associate Professor, Faculty of Management, University of Tehran, Tehran, Iran 2 Instructor of Islamic Azad University, Department of Management, Qom Branch, Qom, Iran 3 M.S of Entrepreneurship Management, Semnan University, Semnan, Iran * Corresponding Author: E-mail:
[email protected] ARTICLE IN FO Keywords: Fuzzy Set BSC LFPP
ABSTRACT As pressures for environmental sustainability increase, industries need to adopt strategies to reduce environmental impact and improve products, services and environmental performance. Green supply chain management (GSCM) has become a proactive approach to enhance environmental performance. The purpose of this paper is applying a new method to ranking the effective factors on Green supply chain performance. Proposed approach is based on LFPP and BSC methods. Balanced Scored Card is used to identify effective factors on Green supply chain performance after that LFPP method is used to determining the weights of the criteria by decision makers According to result, attention and responding to customer requests about environment (C2) is selected as the most important factor that effect on Green supply chain performance. © 2014 Global Journal of Management Studies and Researches. All rights reserved for Academic Journals Center .
Introduction A supply chain is a set of facilities, supplies, customers, products and methods of controlling inventory, purchasing, and distribution (Altiparmak et al., 2006). Since the introduction of the concept in the early 1980s, supply chain management (SCM) has been used to describe the planning and control of materials, information flows, and the logistics activities internally within a company and also externally between companies (Cooper et al., 1997). Presutti and Mawhinney (2007) stated that 70% or more of manufacturing firms’ expenditures are on supply chain-related activities, which highlights the potential impact of an effectively managed supply chain in contributing to overall improvement in financial performance. With global business developing rapidly, the increasing demand for the consumption of commercial products has greatly accelerated the depletion of resources and contributed environmental pollution. Green supply chain management (GSCM) has emerged as a response to the challenge of how to improve long term economic profits and environmental performance (Sheu et al., 2005). According to Zhu and Sarkis (2004), green supply chain management (GSCM) encompasses a set of environmental management practices which are useful for logistics management and are designed to incorporate environmental considerations into the forward and reverse logistics. According to Walker et al. (2008), the green supply chain concept covers all phases of a product’s life cycle, from the extraction of raw materials through the design, production, and distribution phases, to the use of the product by consumers and its disposal at the end of the product’s life cycle. Bacallan (2000) suggested that organizations could enhance their competitiveness through improving their green performance to comply with mounting environmental regulations, address the environmental concerns of their customers and stakeholders, and mitigate the environmental impact of their service activities.
GSCM and Performance Evaluation GSCM has emerged ‘‘as an important new archetype for enterprises to achieve profit and market share objectives by lowering their environmental risks and impacts and while raising their ecological efficiency’’ (Zhu et al., 2005). There is a growing awareness among customers’ oneco-friendly products that increases pressure on manufacturers to incorporate GSCM into their supply chain to satisfy customer requirements (Shen et al., 2013). In recent years, a number of firms realized the potentials of SCM in day-to-day operations management. However, they often lack the insight for the development of effective performance measures and metrics needed to achieve a fully integrated SCM due to lack of a
Performance Evaluation of Green Supply Chain based on LFPP … Global Journal of Manageme nt Studies and Re se arches, 1(3) 2014
balanced approach and lack of clear distinction between metrics at strategic, tactical, and operational levels (Gunasekaran et al., 2001). According to Chan (2003), performance measurement describes the feedback or information on activities with respect to meeting customer expectations and strategic objectives. It reflects the need for improvement in areas with unsatisfactory performance. Interest in performance measurement and management has notably increased in the last 20 years. Companies have recognized that monitoring and understanding firms’ performances have become essential to compete in continuously changing environments (Taticchi et al., 2010). Performance measurement is defined as the process of quantifying the effectiveness and efficiency of action (Chan and Qi, 2003). Green performance can be measured in terms of various indexes that assess the reduction of firms’ environmental impacts in a number of categories, each measured by a separate item variable (Wagner and Schaltegger, 2004). GSCM performance includes four main aspects such as environmental, operational, and positive and negative economic performance (Zhu et al., 2005). Liang et al. (2006) highlighted that for an effective green supply chain management, evaluating the overall performance of the entire chain is crucial.
Balanced Scored Card (BSC) Focusing exclusively on traditional financial accounting measures, such as return on investment and payback period, has implications, and has been criticized as the root cause for many problems in industries (Hafeez et al, 2002). As managers stress on short-term financial performance metrics, they have a tendency to trade off actions, such as new product development, process improvements, human resource development, information technology and customer and market development that can bring in long-term benefits, for current profitability, and this limits the investments with future growth opportunities (Banker et al, 2004). Such actions of managers are a consequence of poorly designed performance measurement systems that only focus on short-term financial performance. In the attempt to solve the problem by supplementing financial measures with additional measures that can help evaluate the long-term performance of a firm, Kaplan and Norton introduced the BSC, a performance measurement framework that provides an integrated look at the business performance of a company by a set of measures, which includes both financial and non-financial metrics ([Kaplan et al, 1992], [Kaplan et al, 1993] and [Kaplan et al, 1996a]). The name of BSC is with the intent to keep score of a set of measures that maintain a balance “between short- and long-term objectives, between financial and non-financial measures, between lagging and leading indicators, and between internal and external performance perspectives” (Kaplan et al, 1996b). Of the BSC’s four performance perspectives, one is a traditional financial performance group of items, and the other three involve non-financial performance measurement indexes: customer, internal business process, and learning and growth. The four perspectives are explained briefly as follows (Kaplan et al, 1996b): • Financial: This perspective typically contains the traditional financial performance measures, which are usually related to profitability. The measurement criteria are usually profit, cash flow, ROI, return on invested capital (ROIC), and economic value added (EVA). • Customer: Customers are the source of business profits; hence, satisfying customer needs is the objective pursued by companies. In this perspective, management determines the expected target customers and market segments for operational units and monitors the performance of operational units in these target segments. Some examples of the core or genetic measures are customer satisfaction, customer retention, new customer acquisition, market position and market share in targeted segments. • Internal business process: The objective of this perspective is to satisfy shareholders and customers by excelling at some business processes that have the greatest impact. In determining the objectives and measures, the first step should be corporate value-chain analysis. An old operating process should be adjusted to realize the financial and customer dimension objectives. A complete internal business-process value chain that can meet current and future needs should then be constructed. A common enterprise internal value chain consists of three main business processes: innovation, operation and after-sale services. • Learning and growth: The primary objective of this perspective is to provide the infrastructure for achieving the objectives of the other three perspectives and for creating long-term growth and improvement through people, systems and organizational procedures. This perspective stresses employee performance measurement, such as employee satisfaction, continuity, training and skills, since employee growth is an intangible asset to enterprises that will contribute to business growth. In the other three dimensions, there is often a gap between the actual and target human, system and procedure capabilities. Through learning and growth, enterprises can decrease this gap. The criteria include turnover rate of workers, expenditures on new technologies, expenses on training, and lead time for introducing innovation to a market.
Evaluation Methods In this section, some essentials of the fuzzy set and Logarithmic fuzzy preference programming are briefly described as follows.
The Fuzzy Logic and Linguistic Variables Fuzzy set theory was first developed in 1965 by Zadeh; he was attempting to solve fuzzy phenomenon problems, including problems with uncertain, incomplete, unspecific, or fuzzy situations. Fuzzy set theory is more advantageous than traditional set theory when describing set concepts in human language. It allows us to address unspecific and fuzzy characteristics by using a membership function that partitions a fuzzy set into subsets of members that ‘‘incompletely belong to” or ‘‘incompletely do not belong to” a given subset. 151
Performance Evaluation of Green Supply Chain based on LFPP … Global Journal of Manageme nt Studies and Re se arches, 1(3) 2014
Fuzzy Numbers We order the Universe of Discourse such that U is a collection of targets, where each target in the Universe of Discourse is [ ]. called an element. Fuzzy number ̃ is mapped onto U such that a random is appointed a real number, ̃ If another element in U is greater than x, we call that element under A. The universe of real numbers R is a triangular fuzzy [ ], and number (TFN) ̃, which means that for ̃ ⁄ ⁄
{
̃
Note that ̃ , where L and U represent fuzzy probability between the lower and upper boundaries, respectively, as in Figure 1. Assume two fuzzy numbers ̃ , and ̃ ; then,
𝜇𝐴 𝑥
1
0
L
M
U
Fig. 1. Triangular fuzzy number ̃
̃
̃
̃
̃
̃
̃
̃
(
̃
(
)
)
Fuzzy Linguistic Variables The fuzzy linguistic variable is a variable that reflects different aspects of human language. Its value represents the range from natural to artificial language. When the values or meanings of a linguistic factor are being reflected, the resulting variable must also reflect appropriate modes of change for that linguistic factor. Moreover, variables describing a human word or sentence can be divided into numerous linguistic criteria, such as equally important, moderately important, strongly important, very strongly important, and extremely important. For the purposes of the present study, the 5-point scale (equally important, moderately important, strongly important, very strongly important and extremely important) is used.
The LFPP-based Nonlinear Priority Method In this method for the fuzzy pairwise comparison matrix, Wang et al (2011) took its logarithm by the following approximate equation: ̃ =(
,
,
), i,j = 1….,n
(6)
That is, the logarithm of a triangular fuzzy judgment a ij can still be seen as an approximate triangular fuzzy number, whose membership function can accordingly be defined as
161
Performance Evaluation of Green Supply Chain based on LFPP … Global Journal of Manageme nt Studies and Re se arches, 1(3) 2014
(
)
( )
( ( )) =
(7) (
)
( )
{
( ( )) is the membership degree of
Where (
}
,
,
). It is very natural that we hope to find a crisp priority vector to maximize the minimum membership ( ( )) i=1,…,n-1 ; j=i+1,…, n} . The resultant model can be constructed (Wang et al, 2011) as
degree λ= min { Maximize Subject to {
̃=
( ) belonging to the approximate triangular fuzzy judgment
λ
( ( ))
}
(8)
Or as Maximize 1- λ ( Subject to {
) (
}
)
(9)
It is seen that the normalization constraint ∑ = 1 is not included in the above two equivalent models. This is because the models will become computationally complicated if the normalization constraint is included. Before normalization, without loss of generality, we can assume for all such that for . Note that the nonnegative assumption for (i = 1,. . . ,n) is not essential. The reason for producing a negative value for λ is that there are no weights that can meet all the fuzzy judgments in ̃ within their support intervals. That is to say, not all the (
inequalities
)
(
or
)
can hold at the same time. To
avoid k from taking a negative value, Wang et al (2011) introduced nonnegative deviation variables such that they meet the following inequalities: (
and
for
) (
)
(10)
It is the most desirable that the values of the deviation variables are the smaller the better. Wang et al (2011) thus proposed the following LFPP-based nonlinear priority model for fuzzy AHP weight derivation: J= (1-λ) 2 + M.∑
Minimize
( (
Subject to
∑ ) )
(11)
{
}
Where = for i = 1,…, n and M is a specified sufficiently large constant such as M = 10 3. The main purpose of introducing a big constant M into the above model is to find the weights within the support intervals of fuzzy judgments without violations or with as little violations as possible.
The Application of Proposed Approach This research has been conducted in company which manufacturing nutritive products. The problem is prioritization of effective factors on GSCM performance. For this reason, first of all, four type criteria based on Balanced Scored Card (BSC) perspectives are determined. Secondly, LFPP method is proposed to realize the evaluation. Evaluation criteria for GSCM performance are presented in Table 1.
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Performance Evaluation of Green Supply Chain based on LFPP … Global Journal of Manageme nt Studies and Re se arches, 1(3) 2014
Table1. Effective factors on GSCM performance Customer Internal Processes
Financial
1) The customer query time
5) Return on investment
2) Attention and responding to customer requests about environment
6) Transportation and Inventory cost
3) Presenting new products according to customers' requirements
7) Investing new money by stakeholder 8) Budget efficiency used for environmental improvement
4) Customer satisfaction
Learning & Growth
9) Efficiency of reverse logistics system
13) Personnel training and empowerment
10) Flexibility of service systems
14) Development of R & D activities
11) Efficiency improvement of operational processes
15) Knowledge sharing between supply chain Members
12) Implementation of environmental standards (for example. ISO 14001)
16) Developing green technology In partnership with supply chain Members
In addition to, the schematic structure established is shown in Fig 2.
Evaluation of Effective Factors on Green Supply Chain Perfornance
Financial
C1
C2
C3
Internal processes
Customer
C4
C5
C6
C7
C8
C9
C10
C11
Learning & Growth
C12
C13
C14
C15
C16
Fig. 2. Schematic structure of research After forming the decision hierarchy for ranking factors, the main criteria to be used in evaluation process are assigned weights by using LFPP method. Geometric means of these values are found to obtain the pairwise compassion matrix on which there is a consensus (Table 1). In LFPP, firstly, we should determine the weights of each main criterion by utilizing pair-wise comparison matrices. We compare each main criterion with respect to other criteria. You can see the pair-wise comparison matrix in Table 2.
Table 2. Fuzzy pair-wise comparison matrix between main criteria Internal Financial Customer Processes Financial (1.00,1.00,1.00) (1.00,1.38,1.80) (2.00,2.30,2.9) Customer (0.56,0.72,1.00) (1.00,1.00,1.00) (1.00,1.50,2.00) Internal Processes (0.35,0.43,0.50) (0.50,0.67,1.00) (1.00,1.00,1.00) Learning & Growth (0.43,0.63,0.83) (1.00,2.00,3.03) (1.67,2.69,5.77)
Learning & Growth (1.20,1.60,2.30) (0.33,0.50,1.00) (0.50,0.39,0.67) (1.00,1.00,1.00)
After forming the model (11) for the comparison matrix and solving this model using Genetic algorithms, the weight vector for main criteria is obtained as follow: = (0.381, 0.211, 0.044, 0.364) T After that we form fuzzy pair-wise comparison matrix for sub criteria and by using the model (11) the weight vector for sub criteria is obtained. According to result, effective factors on Green supply chain performance are ranked as follow: C2 > C9 > C4 > C1 > C12 > C3 > C10 > C11 > C8 > C7 > C14> C5> C15 > C6 > C16> C13 161
Performance Evaluation of Green Supply Chain based on LFPP … Global Journal of Manageme nt Studies and Re se arches, 1(3) 2014
Conclusion Green supply chain management (GSCM) has become a proactive approach to enhance environmental performance. Evaluation of supply chain performance can improve the overall performance of the organization. This paper presents a framework for evaluation of effective factors on GSCM performance based on LFPP and balanced scorecard (BSC) approach. BSC is one of the most comprehensive and simple performance measurement means which emphasizes both aspects of financial and non-financial, long-term and short-term strategies as well as internal and external business measures. In this paper, balanced Scored Card is used to identify effective factors on green supply chain performance after that LFPP method is used to determining the weights of each sub criteria by decision makers According to result, Attention and responding to customer requests about environment (C2) is selected as the most important factor that effect on green supply chain performance. Acknowledgement The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
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[19] [20] [21]
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