Modeling sustainable production indicators with linguistic preferences

Journal of Cleaner Production 40 (2013) 46e56

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Modeling sustainable production indicators with linguistic preferences Ming-Lang Tseng* Lunghwa University of Science and Technology, Graduate School of Business & Management, No. 300, Sec. 1, Wanshou Rd, Guishan Shiang, Taoyuan, Taiwan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 October 2010 Received in revised form 21 November 2010 Accepted 21 November 2010 Available online 30 December 2010

Measuring sustainable production indicators (SPIs) is becoming an important environmental activity due to government directives and increasing awareness among the populous to protect the environment and reduce waste. For printed circuit board manufacturers, measuring SPIs that are dedicated to sustainable activities usually require a multi-criteria structure with driving and dependence power for tracking. Hence, the measures of SPIs are always based on subjective perceptions and interactive relations in nature. A hybrid method for composing a hierarchical structure based on linguistic preferences and that concurrently applies perception judgment is lacking. This study proposes a novel approach in which fuzzy set theory and interpretive structural modeling are adopted to address the analytical objective. The proposed criteria are categorized into a hierarchical structure and arranged into visual quadrants on a graph according to their driving and dependence powers. The insights graph and hierarchical structure could assist mangers in strategic planning related to improving their firms’ environmental activities. Managerial implications and concluding remarks are addressed. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Sustainable production indicators Fuzzy set theory Interpretive structural modeling

1. Introduction The printed circuit board (PCB) manufacturing sector has been around for more than 30 years and enjoys highly experienced staff, a matured industrial supply chain, top quality engineering for PCB layout design, and the benefits of one-stop manufacturing production lines. Nevertheless, the European Union has established a range of environmental policies, such as the restriction of hazardous substances (RoHS) and waste electronics and electrical equipment (WEEE) directives. These two closely linked directives, respectively, ban the use of six hazardous chemicals in the manufacture of electrical and electronic equipment and set collection, recycling and recovery targets for eliminated waste (Tseng 2010; Tseng et al., 2009a,b; Tseng et al., 2008b). Essentially, RoHS applies to the design of products, whereas WEEE is aimed at the life cycles of products. In particular, original equipment manufacturing (OEM) firms must maintain sustainable production and adapt their managerial responses to changing environments to sustain their competitive edge. To provide such a response, a firm must identify the sustainable production indicators (SPIs) on overhauling the production process to achieve the firm’s goal of waste elimination and reduce the impact to the environment. The SPIs enable the firm’s continuous improvement in its environmental impact with great

* Tel.: þ886 910309400. E-mail address: [email protected]. 0959-6526/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2010.11.019

emphasis on green product development in a competitive and sustainable market. The major cause for the continued deterioration of the global environment is the unsustainable pattern of consumption and production, especially in industrialized nations, such as Taiwan. However, there is a growing consensus that to achieve sustainability, it is necessary to move towards developing SPIs for promoting and measuring achievements (Sustain Ability, 2000). Many firms are beginning to understand the importance of sustainable development, although they may not be certain of how the concept applies to their business activities. Consequently, firms must integrate their resources and SPIs to ensure corporate survival in the face of the always changing policies. The hierarchical structure of SPIs has never before been determined nor have they been evaluated in linguistic preferences. Nevertheless, the evaluation of SPIs should be approached in a hierarchical manner. The challenge of this study is that the evaluation of SPIs is always based on hierarchical structure and uses linguistic preference due to the rapid changes in environmental information, and the measures are always in terms of subjective preferences (Veleva et al., 2001; Veleva and Ellenbecker, 2001; Tseng et al., 2009b). A desirable evaluation tool would hybridize fuzzy set theory and interpretive structural modeling (ISM) to fulfill the study objective. Fuzzy set theory is helpful in dealing with the vagueness of human thought and expression in making decisions. In particular, fuzzy set theory presents a useful method for converting these linguistic terms into triangular fuzzy numbers (TFNs). This study uses the algorithm developed by Dubois

M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

and Prade (1980), which effectively aggregates the assessment of the respondents to each criterion with TFNs. This study applies ISM to evaluate the subjective judgment and interactive relations among the criteria into a hierarchical structure (Agarwal et al., 2007). Therefore, the hybrid method is capable of achieving the study objectives. In real situations, numerous interdependent criteria are considered when evaluating an OEM firm. The primary criteria used to evaluate the SPIs are interactive criteria that were considered in prior studies (Lowell Center for Sustainable Production, 1998; Tseng et al., 2009b), namely, (1) energy and material impact on the natural environment, (2) economic performance, (3) community development or social justice, (4) health and safety environment, and (5) green products. For instance, a firm may have outstanding energy and material performance as it relates to the natural environment in relation to its performance in waste elimination and reducing hazards for the natural environment, such as control processes for producing and improving green products and the health and safety qualities of the environment. The reduction of energy and the introduction of material wastes into the natural environment are partially related to the firm’s economic performance. However, the traditional statistical approach is no longer suited to evaluate the proposed interactive and dependent relations of the SPI criteria. To understand the hierarchical interactive relations and compose a hierarchical structure, ISM is typically used. This technique is suitable for analyzing the influence of one criterion on the other criteria. It helps to impose order and direction on the complexity of relationships and composes the criteria into a hierarchical structural framework. For the purposes, this study attempts to develop a hierarchical structure for SPIs that is sufficiently general and can be applied in different homogeneous firms. To date, few studies have adopted such a hybrid method to model and evaluate SPIs in practical field situations. This study presents multiple criteria that are sufficiently general so that they can be applied under various study settings. Resolving problems in evaluating the performance of firms is fundamentally important to both researchers and practitioners. The proposed criteria of this study involve the presentation of linguistic preferences into TFNs to compose a hierarchical structure and further decompose the interrelations between criteria into a crisp value that can be used for analysis. The study begins with a brief introduction to the definition of SPIs, their principles, and the objectives of this study. The remainder of this study is organized as follows: A discussion of the literature related to SPIs is presented in Section 2. Section 3 presents the proposed hybrid method, which applies fuzzy set theory to linguistic preferences and applies a modeling technique based on ISM. Section 4 presents the empirical case study. The measures are provided with measurement guides. Section 5 concludes with a summary of the findings of the method and recommendations for its further development and practical application. 2. Literature review Due to mandated environmental orders from the European Union, such as the WEEE and ROHS directives, and the increasing green competitive pressures, firms must adapt to maintain their competitiveness. Increased green competition pressures are also forcing OEM firms to continuously adapt and develop to enhance their competitiveness in production. SPIs have become the primary basis for improving a firm’s productivity and competitiveness. A firm must develop and evaluate SPIs rapidly and facilitate the adoption of SPI measures within its organization to strengthen its green competitive advantage. However, Ranganathan (1998) clearly pointed out that without any agreement on the fundamentals

47

of what to measure and how to make the measurements, the management will all be awash in a sea of confusing, contradictory, incomplete, and incomparable information. Hence, SPIs are essential for sustainable production. Morrissey and Browne (2004) proposed that a sustainable management model should not be only environmentally effective and economically affordable but also socially acceptable. Therefore, when composing a set of SPIs, the overhauling production process should achieve the firm’s goals of waste elimination and reducing the impact on the environment. 2.1. Sustainable production indicators Sustainable production can be defined as (1) the creation of goods and services using processes and systems that are nonpolluting, (2) the conservation of energy and natural resources, (3) the practice of economically viable operations, (4) the maintenance of a safe and healthy environment for employees, communities and consumers, and (5) socially and creatively rewarding all working people (Lowell Center for Sustainable Production, LCSP, 1998). This definition is consistent with the current understanding of sustainable development, because it emphasizes the environmental, social and economic aspects of a firm’s activities. Recently, a growing number of firms are beginning to use environmental, health and safety, and social indicators (Tseng etal., 2008a; Tseng and Lin, 2009; Tseng et al., 2009a). Thereafter, based on differing SPIs perspectives, some studies have presented indicators or constraints for sustainable production measures. Existing business-related sustainability indicators tend to emphasize the environmental aspects of production. However, Veleva et al. (2001) argued that SPIs should also include economic and social measures. They also proposed a framework that consists of five levels for categorizing existing indicators relative to the basic principles of sustainability. Their study provided a method of evaluating a set of indicators that focus on environmental, health and safety aspects of production, and work is underway to expand their method to include social and economic aspects to inform decision-makers and measure progress towards more sustainable production. Furthermore, Veleva and Ellenbecker (2001) presented a set of indicators of sustainable production for promoting business sustainability. They first introduce the concept of sustainable production, as defined by the LCSP, which includes six dimensions and desirable qualities. Based on that framework, they suggested five stages of core and supplemental indicators for raising the awareness of firms and measuring their progress toward sustainable production systems. The six dimensions are, namely, (1) energy and material use, (2) the natural environment, (3) economic performance, (4) community development and social justice, (5) workers, and (6) products. In 2007, Su et al. studied many modern decision-making support systems that consider social factors in addition to expenses and benefits, environmental effects, technical issues, and management aspects. Moreover, Lin et al. (2010) demonstrated that firms must integrate organizational capabilities and business innovation to ensure corporate survival, and firms must do their best to strengthen their competitiveness in the face of ever-changing green technology and the short life cycles of electronic products. In conclusion, the SPIs of a firm are based on multiple aspects and criteria to be approached. According to the literature, the business activities, processes and characteristics associated with SPI applications are adopted as SPIs dimensions. A study by Fresner (1998) on Austrian preventive environmental protection approaches in Europe showed that the process was water could be saved by recycling the cooling water as process waste water. The study reported the following: (1) Water usage could be avoided by optimizing the use of water through better process control; (2) Operational sequences could be changed to avoid waste; (3) Operators could be trained to calculate the exact

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M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

demand of chemicals to avoid bath rests; and (4) Wasted dyeing baths could be reused. De Bruijn and Hofman (2000) also analyzed the contribution of pollution prevention to the transformation of industries by evaluating the results of various pollution prevention projects. The results showed that pollution prevention was proven to be a valuable concept with a prime focus on material flows and an emphasis on the minimization of environmental effects. It has led to improvements in efficiency and reductions in waste and emissions. The product orientation is undervalued in pollution prevention. Pollution prevention can be an important path for companies towards a more sustainable strategy (Tseng et al., 2009b). Shriberg (2002) described “... The Michigan housing sustainability study developed thirty-eight recommendations to move the organization toward increased sustainability and assessed in terms of their importance, cost and implementation time to produce priority”. Grutter and Egler (2004) have described cleaner production as a preventative integrated continuous strategy for modifying products, processes or services. They also considered cleaner production as the best technological and good housekeeping strategy towards sustainable development. Kjaerheim (2004) successfully demonstrated a cleaner production method that gives both environmental and economic benefits, promotes facility efficiency, reduces the need for expensive end-of-pipe treatment and disposal technologies, improves material and energy efficiency, improves the quality of the system, and reduces longterm liabilities associated with the release of chemicals into the environment. Firms who wish to become more sustainable in their daily production practices should aim to address these aspects and criteria. Decision-makers should also consider the interactions among the criteria of sustainable production (Tseng et al., 2008b).

2.2. Proposed method In this study, a hierarchical structure is built for the prequalification of criteria related to production activities. This is the first study that aims to compose the criteria into a hierarchical structure. Liao and Tseng (2009) used a set of criteria applied to ISM to construct an analytical hierarchical model and applied a fuzzy analytic hierarchy process (AHP) to deal with the improvement of worker productivity in the presence of qualitative approach. Tseng et al. (2008a) used a fuzzy analytical network process (ANP) approach to incorporate and align managerial competitive priorities with various criteria, such as organizations, systems and technologies, assessment and feedback, training and people, into a dependence framework. García-Cascales and Lamata (2009) performed an environmental impact assessment in an intrinsically complex multi-dimensional process involving multiple criteria. This assessment used the AHP, as a potential decision-making method in the management maintenance processes. Agarwal et al. (2007) proposed using ISM to increase the agility of the supply chain to assist in providing the right product, at the right time to the consumer, which is the main objective of any supply chain. Furthermore, interrelationships of the criteria that influence supply chain agility were derived and categorized to driving and dependence power in that study. Moreover, Kannan et al. (2009) applied ISM and order preference by similarity to ideal solution (TOPSIS) to analyze third-party reverse logistics providers (3PRLPs) with a multi-criteria group decisionmaking model in a fuzzy environment to guide the selection process for the best 3PRLP. Lee et al. (2010) applied a hybrid method of ISM and fuzzy ANP to the acquisition of new core technology equipment, which is especially important for manufacturing advanced products, and the technological know-how of the equipment must be transferred completely from the equipment supplier to engineers and operators of the firm so they can effectively utilize the equipment.

Therefore, there is a shortage of ISM studies that are evaluated based on linguistic preference approach. In light of these shortages of prior literatures, this study applies fuzzy set theory and ISM to evaluate the subjective judgment and interactive relations among criteria into a hierarchical structure. Thus, the SPIs are multi-dimensional, complex, and interactive production activities. They are difficult to quantify, and the outcome is highly uncertain when data and information are lacking. The reviewed preliminary literature illustrated that criteria and decision-making are critically important for the success of the measures. Hence, this study attempts to use fuzzy set theory and ISM to compose a hierarchical structure by explicitly describing the decision structure and utilizing the subjective judgments of evaluators based on this decision structure. 2.3. Firm’s SPIs This section describes the composition of a set of valid criteria to satisfy the content validity. The study of Veleva et al. (2001) justified the measurement of SPIs with four criteria related to any stage of the product life cycle. Moreover, Veleva and Ellenbecker (2001) expressed the measures of SPIs in six criteria, namely, energy and material use, the impact on the natural environment, economic performance, community development and social justice, workers and products, into multiple stages of life cycle assessment that lead to successful environmental practices. They conclude in their study that the SPIs of a firm are based on multiple criteria that are both quantitative and qualitative, and that successful SPIs depend on the criteria that they proposed. The study of Tseng et al. (2008b) on organizational design is also a form of proactive key factor environmental practices to improve economic performance. Tseng (2009) pointed out that firms have begun to use environmental, health and safety and social indicators to improve their environmental practices. The proposed model integrates the relevant literature mentioned above. The business activities, components, and characteristics that are found to be associated with SPIs are put forward as criteria. Past studies have offered valuable structures based on some indicators. For example, if one organization invests to reduce its use of fresh water, reduce its material use, reduce its energy use, increase its use of energy from renewable sources, reduce the amount of waste that it generates before recycling (in air, water and land), reduce its greenhouse gas emissions, reduce the amount of hazardous waste that it generates, and reduce the amount of waste that it generates through contracted service/material provides (Su et al., 2007; Tseng et al., 2009a; Tseng and Chiu, 2013; Lin et al., 2010; Tseng, 2010). Then, it presents the daily operation process criteria. An SPI criterion lacks the support of economic performance and products, then the operation process will be restricted. The criteria related to economic performance are to reduce environment health and safety compliance costs, eliminate customer complaints and returns, and reduce the number suppliers participating in raw material or packaging life cycle assessment. Moreover, the percentage of suppliers from the local area, the percentage of products consumed locally, the increase of employment opportunities for local communities are the necessary criteria for community development and social justice (Lowell Center for Sustainable Production, 1998; Veleva and Ellenbecker, 2001) The health and safety environment aspect denotes a firm’s ability to provide a safe environment for workers. This aspect is comprised of several indicators including, obtaining zero lost workdays due to work-related injuries and illnesses, increasing the rate of employee suggested improvements in quality, social and environment health and safety performance, improving employee training on green knowledge, and increasing employee well-being and job satisfaction (Veleva et al., 2001).

M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

The green product aspect signifies the promotion and sale of green products on the basis of understanding customer demand. This aspect is primarily influenced by the fact that green products can be disassembled, reused or recycled, is free from hazardous materials, use 100% biodegradable packaging, and increase the percentage of products with take-back policies. Based on the literature, an evaluation framework is presented with 21 primary interactive/dependence criteria for the evaluation of SPIs. Table 2 presented the study structure description encountered for evaluating the SPI measures (Farrell and Hart, 1998; Veleva et al., 2001; Veleva and Ellenbecker, 2001). The evaluation of SPIs creates a typical multi-criteria problem based on varying aspects and criteria. This study discusses these criteria and their association with the SPIs. In a multi-criteria decision-making (MCDM) problem, multi-criteria indicators of evaluation can be considered. The assessment of SPIs in the context of firm history must be collected by a literature review and expert management staff. The assessment should be particularly concerned with determining what criteria have enabled the firm to sustain in the long run. For the empirical study, the evaluation criteria must be established for the current scenario, which is a chain (interactive relations) of the criteria. The proposed 21 criteria have been considered in previous studies of SPIs that can be found in the literature. 3. Method The criteria cluster has to be dependent, and the relations are described in natural language. The hierarchical structure and interactive relations can be obtained by (i) assigning relations to the criteria and their associated xi criteria (xij, i ¼ 1, 2, xj) and (ii) assessing the interrelation rating of its associated criteria using linguistic preferences. This study proposes a fuzzy logic-ISM approach, followed by the proposed application procedures. In this approach, the relevant qualitative information about the linguistic preferences is first gathered and the results are compiled into a quadrant map. Hence, the first phase is to define the decision objectives and evaluate the SPIs on a qualitative level. 3.1. Fuzzy set theory To determine the qualitative measures (linguistic preferences in this case), fuzzy set theory can be used to represent imprecise judgments mathematically. An effective fuzzy aggregation method is required. Any fuzzy aggregation method must always contain a defuzzification method because the results of human judgments with fuzzy linguistic variables are fuzzy numbers. Defuzzification refers to the selection of a specific crisp element based on the output fuzzy set, which converts fuzzy numbers into crisp values. The qualitative measures are based on the fuzzy arithmetic presented by Dubois and Prade (1980), and the calculated aggregation is determined by k evaluators using

 ~ ¼ ð x ; x ; x Þ X ij L ij M ij R ij 20 1, 0 1, 0 1, 3 k k k X X X p p p ¼ 4@ xij A k; @ xij A k; @ xij A k5; p¼1

p¼1

(1)

p¼1

 ~ ¼ ð x ; x ; x Þ are the TFNs, and the xij at the left, where X ij L ij M ij R ij middle and right positions L xij ; M xij ; R xij represents the overall p average ratings of aspect i and criteria j over k evaluators, and xij , p ¼ 1, 2,..k, are the fuzzy numbers for each evaluator. The fuzzy numbers must be transformed into crisp numbers. Many methods can achieve this transformation (e.g., means of maxima, center of sum, center of gravity, and the a-cut method).

49

The defuzzification method developed by Cheng and Lin (2002) is a particularly sensitive and effective approach that discriminates between two fuzzy numbers during fuzzy ranking by performing numerous simulated experiments in which various linear or nonlinear fuzzy numbers and various degrees of overlap of fuzzy numbers are applied. The method utilizes the fuzzy subtraction of ~ The rectangle is a referential rectangle Z~ from a fuzzy number X. obtained by multiplying the height of the membership function of ~ by the distance between the two crisp maximizing and miniX ~ is considered to be a fuzzy number. The mizing barriers. Hence, Z ~ from the fuzzy fuzzy subtraction of the referential rectangle Z ~ can be performed at level mi by the following: number X

~ Z ~ ¼ ½l ; r   ½a ; a  ¼ ½l  a ; r  a  X 1 2 2 i 1 mi i i i i ¼ 0; 1; 2; .; N

(2)

~ and a1 and where li and ri are the left and right fuzzy numbers of Z, a2 are the left minimum and right maximum fuzzy numbers, respectively. The defuzzification of a fuzzy number is performed by

!  X n n n   X X ~ D X ¼ ðri  a1 Þ ðri  a1 Þ  ðli  a2 Þ i¼0

i¼0

(3)

i¼0

where n is the number of a-levels, and, as n approaches N, the summation approaches the area measurement. In Eq. (3), Pn Pn ~ i ¼ 0 ðri  a1 Þ is positive, i ¼ 0 ðli  a2 Þ is negative, and 0  DðXÞ  1. The minimum values of the left spread and the maximum values of the right spread of the fuzzy numbers are a1 and a2, respectively. This proposed framework allows experts to identify options using linguistic expressions. This study is unique in that qualitative descriptions of linguistic expressions are presented by TFNs and the fuzzy values can be defuzzified into crisp values that can be analyzed by ISM. 3.2. Interpretive structural modeling The theory of ISM is based on discrete mathematics, graph theory, social sciences, group decision-making, and computer assistance. The procedures of ISM were developed through individual or group mental models that were used to calculate binary matrices, also called relation matrices, to present the relations of the criteria Warfield (1974). In addition, the Delphi method is a technique that is used to arrive at a group position regarding an issue under investigation. The Delphi method consists of a series of repeated interrogations, usually by means of questionnaires, of a group of individuals whose opinions or judgments are of interest. After the initial interrogation of each individual, each subsequent interrogation is accompanied by information regarding the preceding round of replies, which is usually presented anonymously. The individual is thus encouraged to reconsider and, if appropriate, to change his previous reply in light of the replies of other members of the group. The group position is determined by averaging. The ISM method implemented as follows: 1. 2. 3. 4.

Provisions are made for the inclusion of the scientific criteria; The complex set of relations are presented; The complex set of relations under uncertainties are presented; The congruence with the originators’ perceptions and analytical processes is evaluated; 5. The ease of learning by a public (or, by inference, multidisciplinary) audience is evaluated.

Graphical models, or more specifically, directed graphs (digraphs) are suitable for evaluating criteria. In a digraph, the criteria of a system are represented by the “points” of the graph, and the existence of

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M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

a particular relationship between criteria is indicated by the presence of a directed line segment. It is this concept of relatedness in the context of a particular relation that distinguishes a system from a mere aggregation of criteria. A relation matrix can be formed by asking the question, “Does the feature ei inflect the feature ej?” This study presents group decisions on the interactive relations among criteria. Fuzzy set theory is used to justify the human perceptions on the specific relations. However, human perceptions always contain a grey area (Table 1). The mean of all respondents is used as the judgment criteria for the relations to be justified. If the crisp value is lower than the mean, then the relation is not strong enough to be recorded. The general form of the relation matrix can be presented as follows:  If the (i, j) described is A, the (i, j) described in the reachability matrix becomes 1 and the (j, i) entry becomes 0.  If the (i, j) described is B, the (i, j) described in the matrix becomes 0 and the (j, i) entry becomes 1.  If the (i, j) described is C, the (i, j) described in the matrix becomes 1 and the (j, i) entry also becomes 1.  If the (i, j) described is D, the (i, j) described in the matrix becomes 0 and the (j, i) entry also becomes 0. Following these rules, initial reachability matrix for the criteria is established.

0 B e1 B B e2 D ¼ B B : B @ : en

e1 0 d12 .: .: dm1

e2 d12 0 .: .: dm2

.: .: .: .: .: .:

.: .: .: .: .: .:

(4) k>1

(5)

Then, the reachability and the priority set bases can be defined by Eqs. (6) and (7), respectively, according to the following equations

AðtiÞ ¼

n  o  tj m0ij ¼ 1

(6)

RðtiÞ ¼

o n   tj m0ij ¼ 1

(7)

where mij denotes the value of the ith row and the jth column.

Table 1 Linguistic scales. Linguistic values Extreme (0.75, 1.0, importance 1.0) Demonstrated (0.5, 0.75, importance 1.0) Strong importance (0.25, 0.5, 0.75) Moderate (0, 0.25, 0.5) importance Equal importance (0, 0, 0.25) Triangular fuzzy membership functions Linguistic terms

Criteria

Sustainable production indicators

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16

C20 C21

where ei is the ith criterion in the system, dij denotes the relation between ith and jth criterion, and D is the relation matrix. After constructing the relation matrix, we can calculate the reachability matrix using Eqs. (4) and (5) as follows

M ¼ Mk ¼ Mkþ1

Goal

C17 C18 C19

1 en d1n C C d2n C C : C C : A 0

M ¼ DþI

Table 2 Proposed criteria of SPIs.

Reduce the use of fresh water Reduce hazardous material use Reduce energy use Increase the use of energy from renewable sources Reduce the amount of waste generated before recycling(Air, water and land) Reduce greenhouse gas emissions Reduce amount of hazardous waste generated Reduce of waste generated by contracted service/ material provides Reduce environment health and safety compliance costs Zero customer complaints or returns Percent suppliers participating in raw material or packaging Life Cycle Assessment Percent of suppliers from the local area Percent of products consumed locally Increase employment opportunities for local community Achieve zero lost workdays as result of work-related injuries and illness Increase the rate of employee suggested improvement in quality, social and environment health and safety performance Increase employee training on green knowledge Increase employee well-being and job satisfaction Design all green products can be disassembled, reused or recycled, free hazardous materials Use 100% biodegradable packaging Increase percent of products with take-back policies

According to Eq. (8), the levels and relations between the criteria can be determined and the structure of the criterion’s relations can be expressed in the graph. R represents the intersection of antecedent set and reachability set.

RðtiÞXAðtiÞ ¼ RðtiÞ

(8)

3.3. Dependenceedriving power analysis (DDPA) This study follows a flow chart to obtain a hierarchical model. The structure must be interpreted with DDPA. This tool is used to draw implications for managing the criteria. It identifies the relative interactions of the criteria associated with SPIs and indicates the degree of dependence and driving power ranking at the same time (Martilla and James, 1977). The results are plotted graphically on a two-dimensional grid in which the driving power of the criteria is displayed on the vertical axis, while the dependence power level is displayed on the horizontal axis, and the graph is divided into four quadrants. The quadrants are labeled as the autonomous criteria, dependent criteria, linkage criteria and independent criteria. The first quadrant (the autonomous criteria) includes criteria that have weak driving power and weak dependence. These criteria are relatively disconnected from the system with which they have only few links, which may be strong. The second quadrant (the dependent criteria) consists of criteria that have weak driving power but strong dependence. The criteria in third quadrant (the linkage criteria) have strong driving power and strong dependence. These criteria are unstable because any action on these criteria has an effect on the others and also a feedback effect on themselves. The fourth quadrant includes independent criteria, which have a strong driving power but weak dependence. Using simple visual analysis, the quadrant evaluation grid reveals the strengths and weaknesses of the criteria under consideration and so draws managerial

Table 3 Respondents in triangular fuzzy numbers.

C1 C2 C3 C4

C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20

C20

C19

C18

C17

C16

C15

C14

C13

C12

C11

C10

C9

C8

C7

C6

C5

C4

C3

(0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0)

(0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0, 0.25) (0, 0, 0.25) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0.75, 1.0, 1.0)

(0, 0.25, 0.5) (0, 0.25, 0.5) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.75, 1.0, 1.0)

(0, 0.25, 0.5) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0.5, 0.75, 1.0) (0, 0, 0.25) (0.5, 0.75, 1.0) (0, 0, 0.25) (0.5, 0.75, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5)

(0, 0.25, 0.5) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0, 0.25, 0.5)

(0, 0, 0.25) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0, 0.25) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0, 0.25, 0.5)

(0, 0, 0.25) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0.75, 1.0, 1.0) (0, 0, 0.25) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0.5, 0.75, 1.0) (0, 0.25, 0.5)

(0.75, 1.0, 1.0) (0, 0.25, 0.5) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0, 0.25) (0, 0, 0.25) (0, 0.25, 0.5)

(0, 0.25, 0.5) (0, 0.25, 0.5) (0.5, 0.75, 1.0) (0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0, 0.25) (0.5, 0.75, 1.0) (0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0, 0.25, 0.5)

(0, 0, 0.25) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0, 0.25) (0.75, 1.0, 1.0) (0, 0, 0.25) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0, 0.25) (0, 0, 0.25) (0.5, 0.75, 1.0)

(0, 0.25, 0.5) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5) (0.5, 0.75, 1.0)

(0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0.5, 0.75, 1.0) (0.5, 0.75, 1.0) (0, 0.25, 0.5) (0, 0.25, 0.5)

(0, 0, 0.25) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0.75, 1.0, 1.0)

(0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0, 0.25) (0.5, 0.75, 1.0) (0, 0, 0.25)

(0.75, 1.0, 1.0) (0.75, 1.0, 1.0) (0, 0, 0.25) (0, 0, 0.25) (0, 0.25, 0.5) (0.75, 1.0, 1.0)

(0, 0, 0.25) (0, 0, 0.25) (0, 0, 0.25) (0, 0.25, 0.5) (0, 0, 0.25)

(0, 0.25, 0.5) (0, 0.25, 0.5) (0.5, 0.75, 1.0) (0, 0, 0.25)

(0, 0.25, 0.5) (0, 0.25, 0.5) (0, 0, 0.25)

(0, 0.25, (0.75, 1.0, 0.5) 1.0) (0, 0, 0.25)

C2

M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

C5

C21

51

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M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

implications for resource allocation. The competitive positions are identified, and further improvement strategies are discussed. 4. Results The survey was pre-tested for content validity in two stages. In the first stage, ten experienced researchers were asked to rate the ambiguity, clarity and appropriateness of the items used to measure each criterion. Based on the obtained feedback, the instrument was modified to enhance the clarity and appropriateness of the measures. In the second stage, the survey was mailed to five academics and professionals who are affiliated with PCB firms in Taiwan. These academics and professionals were asked to review the questionnaire for structure, readability, ambiguity and completeness. The final survey incorporated feedback received from these executives, which enhanced the comprehensibility of the instruments. This process yielded a survey that was judged to exhibit high content validity. This questionnaire evaluates 21 criteria related to expectations and performances. 4.1. Sample and survey Using the purposive sampling method, samples were collected from PCB professionals in Taiwan. A total of 120 replies were obtained during the six month period following face-to-face interviews. The 120 replies have extensive experience in the field. This study gathers the relevant qualitative information, and composes a hierarchical structure and quadrant map for the strategic plan. Hence, the first phase defines the decision objectives, which involve evaluating SPIs with linguistic preferences. 4.2. Proposed method In an empirical study, the evaluation criteria for the current scenario must be established based on the chain (interrelations) of the criteria. The proposed 21 criteria have been considered as SPIs in prior studies. The hierarchical structure and interrelations can be obtained by (i) assigning the relations to the criteria and their associated xi criteria (xij, i ¼ 1, 2, xj) and (ii) assessing the interrelations rating by applying fuzzy set theory to its associated criteria. This study proposes a fuzzy ISM approach, followed by the proposed application procedures.

1. Identify the relationships among criteria using TFNs and defuzzify them into a crisp value by applying Eqs. (1)e(3). Then, by using the mean of the replies to justify the interactive relations, interpret the linguistic judgment of the group decisions according to the TFNs. Table 3 presents the relations among the criteria. 2. The proposed technique provides a systemic approach for improving the SPIs performance. For example, for the “A” presented criteria, i will help to achieve criteria j; for the “B” presented criteria, j will be achieved by criteria i; for the “C” presented criteria, i and j will help achieve each other; and for the “D” presented criteria, j and i are unrelated. Using Eqs. (4) and (5) the reachability matrix can be obtained. 3. Interpret the group decisions to determine how the criteria are related and generate a crisp value. An overall structure is extracted from the complex set of criteria then transformed into a reachability matrix format by transforming the information in each entry of the linguistic preferences into 1s and 0s in the reachability matrix. The results of the study indicate that there are five levels in the hierarchical structure in which the top-level criteria possess the strongest driving power. The toplevel criteria C16 and C17 also show a strong dependence on other criteria, called linkage criteria. The improved identification of the top-level criteria helps to enhance the effectiveness of the SPIs. Therefore, the management of the PCB firms should focus its attention on building a strong agent through better use of aforementioned criteria. The criteria in Level II include C4, C8, C11, C12, C13 and C14. Level III includes criteria C1, C3 and C9. Three criteria are in Level IV, which are C5, C6 and C21. The fifth level has seven criteria, namely, C2, C7, C10, C15, C18, C19 and C20 (Table 4). 4. Begin by identifying the criteria that are relevant to the SPIs and extend the criteria with a group problem-solving. Then a contextually relevant subordinate relation is chosen. Having decided on the element set and the contextual relation, a structural self-interaction matrix is developed based on a pairwise comparison of variables. Apply Eqs. (6), (7) and (8) to obtain the hierarchical levels of the criteria. The matrix is partitioned by assessing the reachability and antecedent sets for each criterion. The reachability set consists of the criterion itself and other criteria, which it helps to achieve, whereas the antecedent set consists of the criterion itself and other criteria, which help to achieve that criterion. The intersection of these sets is derived

Table 4 Reachability matrix.

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21

C1

C2

C3

C4

C5

C6

C7

C8

C9

C10

C11

C12

C13

C14

C15

C16

C17

C18

C19

C20

C21

1 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 1 1 1 1

0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1

0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0

1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0

1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0

0 1 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0

1 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0

0 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1

0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1

0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 0 1 0

1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 1 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0

0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0

0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1

0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 0

0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0

1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1

M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

for all the criteria. The criteria for which the reachability and intersection sets are the same are the top-level criteria in the ISM hierarchy. The top-level criteria do not assist in achieving any other criteria above their own level in the hierarchy by definition. Once the top-level criteria are identified, they are separated from the other criteria. Then, the same process is repeated to find the next level of criteria. These identified levels assist in building the digraph and final model. In the present case, the criteria along with their reachability set, antecedent set, intersection set and associated levels are shown in Table 5. 5. Use a visual analysis DDPA evaluation grid to draw the four quadrants for the proposed criteria. The evaluation grid reveals the functions of the SPI criteria. The first quadrant includes criteria (autonomous criteria) that have weak driving power and weak dependence and are relatively disconnected from the system, with which they have only few links. Those criteria are Reduce energy use (C3), Increase the use of energy from renewable sources (C4), Reduce the waste generated by contracted service/material providers (C8), Increase the percentage of suppliers from the local area (C12), Increase employment opportunities for the local community (C14). The second quadrant consists of criteria (dependent criteria) that have a weak driving power but strong dependence, which include Reduce the amount of waste generated before recycling (air, water and land) (C5) and Reduce greenhouse gas emissions (C6). Moreover, the criteria in the third quadrant (linkage criteria) have strong driving power and strong dependence. Those criteria include Reduce hazardous material use (C2), Reduce the amount of hazardous waste generated (C7), Achieve zero customer complaints or returns (C10), Increase employee well-being and job satisfaction, Design all green products can be disassembled, reused or recycled (C18), Reduce the amount of free hazardous materials used (C19), Use 100% biodegradable packaging (C20) and Increase the percentage of products with take-back policies (C21). These criteria fall into the category of linkage criteria. These criteria are unstable, which means that any action on these criteria has an effect on others and also a feedback effect on themselves. Therefore, providing its SPIs performance must be considered as a top priority. The fourth quadrant includes independent criteria that have strong driving power but weak dependence, which include Reduce the use of fresh water (C1), Reduce environment health and safety compliance costs (C9), Increase the

53

percentage of suppliers participating in raw material or packaging life cycle assessments (C11), Increase the percentage of products consumed locally (C13), Achieve zero lost workdays as result of workrelated injuries and illness (C15), Increase the rate of employee suggested improvements in quality, social and environment health and safety performance (C16) and Increase employee training on green knowledge (C17). Using simple visual analysis, the quadrant evaluation grid reveals the strengths and weaknesses of the criteria under consideration and so draws managerial implications for resource allocation. The competitive positions are identified, and further improvement strategies are discussed. 5. Managerial implications This study has several implications for firms who intend to evaluate manufacturing bases in term of SPIs. The SPIs can be used to evaluate the impact of various production and management activities and thus provide a mechanism for monitoring the sustainable production-based performance of firms. Although previous studies showed a great deal of variety in SPI measurements, these varieties did not generally appear to have clear links to organizational decision contexts. Indeed prior studies that made use of single variables are no longer relevant because the challenges of the business environment have changed, and single variables are not able to explain the impact of SPIs. SPIs are the nature of the multi-criteria concept toward sustainable production. In particular, when evaluating the impact of the introduction of developed SPIs activities need to be analyzed from an overall production perspective, and the effects on the contextual organization must be considered. A hierarchical structure if SPIs can allow managers and researchers to better understand the different needs in environmental production and management activities and specific management interventions that would improve the likelihood of excellent and useful research by examining the 21 criteria of the SPIs. These criteria serve as bridging mechanisms that are helpful in improving environmental performance toward sustainable production in the firms. From the criteria perspective, there are five criteria with the most driving and dependence power, namely, Reduce hazardous material use (C2), Reduce the amount of hazardous waste generated (C7), Increase employee well-being and job satisfaction (C18), Design all green products to be disassembled, reused or recycled,

Table 5 Levels of SPIs. Criteria

Reachability set: R(Ci)

Antecedent set: A(Ci)

Intersection

Level

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21

1,7,8,10,14,21 2, 7, 9,11,12,15,16,17,18,21 3,15,16,17,18,21 4,13,15,16,17 5,9,13,14,15,16,17 6,7,9,10,11,18,20 1,2,6,7,9,10,11,12,13,14,20 1,8,9,18,20 2,7,8,9,13,20 1,10,11,13,15,16,17,18 2,10,11,15,21 2,12,19,21 4,9,13,15,20 1,7,14,19,20 10,11,13,15,19, 2016,19,20 16,19,20 17,18,19,21 1,2,3,4,6,10,13,18,19,21 1,2,3,4,12,16,17,18,19,20,21 1,3,4,6,7,8,9,13,16,19,20,21 1,2,3,11,12,17,21

1,7,8,10,14,18,19,20,21 2,7,9,11,12,18,19,21 3,18,19,20,21 4,13,18,19 5 6,7,8,18,20 1,2,6,7,9,14,20 1,8,9,18,20 2,5,6,7,8,9,13,20 1,6,7,10,11,15,18 2,6,7,10,11,15,21 2,7,12,19,21 4,5,7,9,10,13,15,18,20 1,5,14 2,3,4,5,10,11,13,15 2,3,4,5,10,16,19,20 2,3,4,5,10,17,19,21 2,3,6,8,17,18,19, 12,14,15,16,17,18,19,20 6,7,8,9,13,14,15,16,19,20 1,2,3,11,12,17,18,19,20,21

1,7,8,10,14,21 2,7,9,11,12,18,21 3,18, 21 4,13 5 6,7, 18,20 1,2,6,7,9, 20 1,8,9,18,20 2,7,8,9,13,20 1,15,18 2,10,11,15,21 2,12,19,21 4,9,13,15,20 1, 14 11,13,15 16,19,20 17,19,21 2,3,6, 18,19, 12,16,17,18,19,20 6,7,8,9,13, 16,19,20 1,2,3,11,12,17,21

III V III II IV IV V II III V II II II II V I I V V V IV

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M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

criteria. Through the framework, the managers are able to capture a fairly complete picture of contextual SPIs. In other words, managers may find that the application of the SPI framework for assessing the relative performance of the criteria of the SPIs developed, validated and operationalized in their environmental production and management activities is a useful framework for reviewing and improving sustainable production evaluation and strategic development, which may enhancements of their firm’s productivity and allow them to maintain a competitive advantage. 6. Concluding remarks This study was conducted to provide a full account of such an inextricably complex phenomenon as vagueness and dependence relations among the criteria, but the study goal is to conduct a precise and thorough study of the positioning criteria as a strategic direction of SPIs in the PCB manufacturing sector. At present, SPIs are being explored with the intention of providing some practical implications to managers who are eager to probe the relevant strategies, especially to build hierarchical models and understand the relations among the critical criteria. In addition, expert groups have remarked on the merits and drawbacks of the proposed solution. Unlike a traditional hierarchical model based on a linear and piecemeal approach, the modified ISM with fuzzy set theory together is novel because it is based on complex dependence relations with linguistic preferences. Moreover, the model is better able to handle the problem of dependence of criteria, linguistic preferences and model a hierarchical structure because it can provide more valuable information for strategic direction (Sarkis, 2003; Tseng et al., 2008a). The following section is devoted to that purpose. First of all, the proposed method improves the analysis by introducing a linkage criteria quadrant. Specifically, the criteria may be better positioned to direct their goals and practices to achieve sustainable development. This is particularly crucial to allow management to focus on the criteria to satisfy environmental requirements to improve their performance. To assess the criteria and effectiveness of the proposed solution, the study uses the DDPA to develop a positioning visual strategy. It is well known that SPIs must be devised with a set of criteria and must consider what is most valued among the measures. Many works focus on providing valuable advice, including essential criteria for a successful direction

Fig. 1. Cluster of criteria for improving SPIs.

Reduce the amount of free hazardous materials used (C19) and Use 100% biodegradable packaging (C20), as shown in Fig. 1. Moreover, most of the top five criteria are concerning green products, so the marketing manager must improve their operations such as Design green products can be disassembled, reused or recycled, free hazardous materials (C19), Use 100% biodegradable packaging (C20) and Increase percent of products with take-back policies (C21) to correspond with the requirements on the environmental performance level (Table 6). Similarly, increasing the employee well-being and job satisfaction is also related to inspecting their working environment and the management decision is asked for improving production sites. By analyzing all sets of SPI criteria that were submitted by the different respondents, the environmental performance level is then determined by the management. In a broader sense, the SPI framework can be used as an analytical and monitoring tool to develop and construct an overall environmental development strategy for the PCB firms. For the practice of management, SPIs enable organizational managers to better understand the interactions among proposed

Table 6 Driving and dependence power in reachability matrix.

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 Dependence

C1

C2

C3

C4

C5

C6

C7

C8

C9

C10

C11

C12

C13

C14

C15

C16

C17

C18

C19

C20

C21

Driving Power

1 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 1 1 1 1 9

0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 1 8

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 5

0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 5

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 4

1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 7

1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 5

0 1 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 8

1 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 7

0 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 7

0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 5

0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 0 1 0 9

1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 4

0 1 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 8

0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 8

0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 8

0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 8

0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 0 8

0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 10

1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 10

6 10 6 5 7 7 11 5 6 8 5 4 5 5 6 3 4 11 11 12 7

M.-L. Tseng / Journal of Cleaner Production 40 (2013) 46e56

(Tseng et al., 2008a, 2009b; Tseng and Chiu, 2013). Few works provide methods that can be used to empirically evaluate the hierarchical model of the SPIs involved with several complex criteria systematically for PCB manufacturing firms. Hence, this study proposes an effective solution that can position the criteria hierarchically in an analytical manner. A second improvement is that it gives higher driving power to the top three criteria, which are Reduce the amount of waste generated before recycling (air, water and land), Reduce greenhouse gas emissions and Increase employment opportunities for local communities, to position the criteria in the evaluation. The results indicated that the criteria are located in the dependent and autonomous criteria quadrants in the management. Moreover, criteria with higher dependence powers include Reduce the amount of hazardous waste generated, Design all green products so they can be disassembled, reused or recycled, free hazardous materials and Use 100% biodegradable packaging. Lastly, the contribution is to build a visual map and to evaluate SPIs with driving and dependence power successfully. Few prior studies have been able to systematically evaluate and construct the proposed criteria into a hierarchical framework and visual map in uncertainty. Furthermore, the homogenous firms might apply this study to evaluate and determine the driving and dependence powers to reduce the management risks. In conclusion, this study contributes to the literature by (i) constructing a hierarchical framework for the SPIs; (ii) developing multi-criteria measures for SPIs based on linguistic preferences and; (iii) composing a visual map to determine the driving and dependence power of criteria. Moreover, the main contribution of this study is the hierarchical and interactive model that is presented in Table 5. This model provides a useful guideline as a structured and logical means of synthesizing judgments for evaluating appropriate manufacturing bases for OEM firms. It helps structure a difficult and often perception-burdened decision. The second contribution is the identification and examination of the criteria of the SPIs listed in the proposed model based on a comprehensive review, the features of SPIs have been examined and identified. These give an overview structure for the PCB firms without much knowledge of SPIs. Such firms can better understand the SPI criteria. The hybrid method is particularly useful for decision-making in a multi-criteria interdependence contexts. Moreover, it may also prove advantageous for other firm to customize the framework for use in their own environmental production activities. In this manner, evaluators need to take the SPIs and delete their relevant criteria from it and to add what is missing. Consequently, the SPIs can be used in different criteria and can be further modified and refined if required. Because knowledge has an important strategic role in enhancing environmental performance, the firms are expected to use SPIs effectively and efficiently and transfer into their competitive advantages in sustainability environment. However, this hybrid approach is a novel method that can systematically deal with many uncertain problems with interactive criteria unlike traditional approaches, which are always applied by assuming independence of the criteria. To promote and deepen continuing research in the future, it is worthwhile to investigate more cases in depth to uncover invaluable new issues that should be studied further. In addition, the assessment criteria can be improved as different status suffered. ISM is a tool that enables managers to understand the criteria behavior of SPIs. Moreover, fuzzy set theory allows subjective human responses to be translated into a solid crisp value. Prior studies that utilize ISM are always based on human judgment. Nonetheless, there is no study in which ISM and fuzzy set theory are used together. This fuzzy ISM approach is a novel approach for

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generating a visual map for managers to justify the driving and dependence power of proposed criteria. However, the use of 100% biodegradable packaging as a higher driver and dependence power for the management to focus on SPIs. This implies that the criteria can be used as tools to integrate processes and work as drivers for effective SPI integration. In this study, only 21 criteria are identified and arranged into a hierarchical structure. For future studies, more criteria can be identified and evaluated in the fuzzy ISM approach. Some of the criteria are highlighted here for the analysis with the resulted hierarchical model. The relative importance and relations among the criteria are given by the driveredependence diagram. This approach depends upon the opinion of the respondents and some bias may occur. However, this model has not been numerically validated. Analytical network process (ANP) can be used to examine the consistency index and consistency ratio (Tseng et al., 2009a). Moreover, structural equation modeling, which is commonly known as the linear structural relationship approach, has the ability to test the validity of such a hypothetical model. Hence, future approaches might test and enhance the validity of this framework. References Agarwal, A., Shankar, R., Tiwari, M.K., 2007. Modeling agility of supply chain. Industrial Marketing Management 36, 443e457. Cheng, C.H., Lin, Y., 2002. Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research 142 (1), 174e186. De Bruijn, T.J.N.M., Hofman, P.S., 2000. Pollution prevention and industrial transformation evoking structural changes within companies. Journal of Cleaner Production 8, 215e223. Dubois, D., Prade, H., 1980. In: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, pp. 38e40. Farrell, A., Hart, M., 1998. What does sustainability really mean? Environment 40 (9), 5e31. Fresner, J., 1998. Starting continuous improvement with a cleaner production assessment in an Austrian textile mill. Journal of Cleaner Production 6, 85e91. García-Cascales, S.M., Lamata, M.T., 2009. Selection of a cleaning system for engine maintenance based on the analytic hierarchy process. Computers and Industrial Engineering 56 (4), 1442e1451. Grutter, J.M., Egler, H.P., 2004. From cleaner production to sustainable industrial production modes. Journal of Cleaner Production 12, 249e256. Kannan, G., Pokharel, S., Kumar, P.S., 2009. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling 54 (1), 28e36. Kjaerheim, G., 2004. Cleaner production and sustainability. Journal of Cleaner Production 13, 329e339. Lee, A.H.I., Wang, W.M., Lin, T.Y., 2010. An evaluation framework for technology transfer of new equipment in high technology industry. Technological Forecasting and Social Change 77 (1), 135e150. Liao, C.H., Tseng, M.L., 2009. Using interpretive structural modeling and AHP to evaluate the worker productivity criteria in uncertainty. WSEAS Transaction on Business and Economics 6 (8), 435e445. Lin, Y.H., Cheng, H.P., Tseng, M.L., Tsai, C.C., 2010. Using QFD and ANP to analyze the environmental production requirements in linguistic preferences. Expert Systems with Applications 37 (3), 2186e2196. Lowell Center for Sustainable Production, 1998. Sustainable production: a working definition. Informal meeting of the committee members. Martilla, J.A., James, J.C., 1977. Importance e performance analysis. Journal of Marketing 41, 77e79. Morrissey, A.J., Browne, J., 2004. Waste management models and their application to sustainable waste management. Waste Management 24, 297e308. Ranganathan, J., 1998. Sustainability Rulers: Measuring Corporate Environmental and Social Performance. Sustainable Enterprise Perspectives. World Resource Institute, Washington, DC. http://www.wri.org/meb/pdf/janet/pdf. Sarkis, J., 2003. A strategic decision making framework for green supply chain management. Journal of Cleaner Production 11 (4), 397e409. Shriberg, M., 2002. Toward sustainable management: the university of Michigan housing division’s approach. Journal of Cleaner Production 10, 41e45. Su, J.P., Chiueh, P.T., Hung, M.L., Ma, H.W., 2007. Analyzing policy impact potential for municipal solid waste management decision making: a case study of Taiwan. Resources Conservation and Recycling 51, 418e434. Sustain Ability, 2000. Team Spotlights Top 50 Corporate Sustainability Reports. GreenBiz. http://www.greenbiz.com/news/newsFthird.cfm?NewsID¼13397. Tseng, M.L., 2009. Application of ANP and DEMATEL to evaluate the decisionmaking of municipal solid waste management in Metro Manila. Environmental Monitoring and Assessment 156 (1e4), 181e197.

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