A strategic decision framework for green supply chain management

A strategic decision framework for green supply chain management

Journal of Cleaner Production 11 (2003) 397–409 www.cleanerproduction.net A strategic decision framework for green supply chain management Joseph Sar...

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Journal of Cleaner Production 11 (2003) 397–409 www.cleanerproduction.net

A strategic decision framework for green supply chain management Joseph Sarkis ∗ Clark University, Graduate School of Management, 950 Main Street, Worcester, MA 01610, USA Accepted 10 May 2002

Abstract The number of organizations contemplating the integration of environmental practices into their strategic plans and daily operations is continuously increasing. Numerous initiatives have provided incentives for organizations to become more environmentally benign. Some of these regulation driven programs are mandatory, but increasingly numerous voluntary environmental programs are also introduced by organizations. Organizations view many of these environmental programs, which may include technological and organizational development projects, as possible alternatives for gaining or maintaining a competitive advantage. One environmental program area that continues to gain in importance is one that focuses on the external relationships among organizations. To help evaluate alternatives that will effect this relationship we present a strategic decision framework that will aid managerial decisionmaking. This decision framework is based on literature and practice in the area of environmentally conscious business practices. The focus of this paper will be on the components and elements of green supply chain management and how they serve as a foundation for the decision framework. We shall explore the applicability of a dynamic non-linear multiattribute decision model, defined as the analytical network process, for decision making within the green supply chain. Issues facing the modeling approach are also discussed.  2002 Elsevier Science Ltd. All rights reserved. Keywords: Strategic decision making; Natural environment; Analytic hierarchy/network process; Supply chain management

1. Introduction Environmentally conscious business practices have been receiving increasing scrutiny from both researchers and practitioners. Interdisciplinary research has integrated the efforts of management, engineering, physical and social sciences to investigate the issues relevant to this topic. Similarly, multifunctional groups within organizations and external stakeholders have a role in decisions related to organizations and the natural environment. When organizational environmental decisions are to be made they will necessarily be strategic and usually more complex for this reason. These decisions will have internal and external implications for the management of an organization. Green supply chain decisions are one of the latest issues facing organizations



Corresponding author. Tel.: 508-793-7659; fax: 508-793-8822. E-mail address: [email protected] (J. Sarkis).

with strong internal and external linkages. One approach to model the dynamic nature of business and its relationship to the natural environment into a decision framework is a technique that is capable of considering the multidimensional qualitative and strategic characteristics. This paper identifies and structures the primary strategic and operational elements for a framework that will aid managers in evaluating green supply chain alternatives. These alternatives may include such factors of who to partner with, what type of technology to introduce, or what type of organizational practice to adopt. The decision to adopt one of these alternatives will be necessary for an effective green supply chain, but will be dependent on a number of factors and elements. The structure that is developed in this paper is a “network hierarchy” that can be used to evaluate these alternatives. The technique for analyzing the decision is based on the analytical network process (ANP) or the systems-withfeedback approach first introduced by Saaty [13]. The

0959-6526/02/$ - see front matter.  2002 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0959-6526(02)00062-8

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dynamic characteristics and complexity of this decision environment (which is true for most strategic decisions) makes the ANP technique a suitable tool. Managerial decision-making is supported through the application of this tool. Issues and possible extensions to the ANP approach identify some of its application limitations and its flexibility. The paper’s flow begins with an introduction into the issues relevant to green supply chains and their management. The paper then structures the various elements of the green supply chain into a decision framework. An illustrative example is used to explore the application of the ANP technique to this problem. A number of issues and future directions are summarized in the final sections of this paper.

2. Green supply chain management Industrial Ecology has been gaining popularity among the corporate and research communities [5]. Lowe [8] defines industrial ecology as, “a systematic organizing framework for the many facets of environmental management. It views the industrial world as a natural system—a part of the local ecosystems and the global biosphere. Industrial ecology offers a fundamental understanding of the value of modeling the industrial system on ecosystems to achieve sustainable environmental performance.” An industrial ecology (ecosystem) has been defined to exist on three levels [6]. These levels are characterized by the amount of recycling or reuse of material that is within the system (or the system’s “openness”). The first level is a completely closed system with no material or energy leaving the system. The third level is a completely open system with little material or energy, once consumed, remaining within a system. The second level is characterized by some factor of energy and material is reused within the system. The second level seems to be the most applicable model for actual systems. It is within these industrial ecosystems models that green supply chains will play a critical and practical role. Some partial industrial ecosystems are currently in operation. For example, a privately organized industrial ecosystem model exists in Kalundborg, Denmark. The Kalundborg industrial ecosystem consists of a network of organizations composed of an electric power generating plant, an oil refinery, a biotechnology production plant, a plasterboard factory, a sulfuric acid producer, cement producers, local agriculture and horticulture, and district heating utilities [17]. Domestically, government agencies have supported research and development of a key element of industrial ecosystems called eco-industrial parks where ecologically complementary organizations are physically located in a regional area. Private organizations such as Hewlett–Packard, IBM,

Xerox, and Digital Equipment Corporation have introduced some form of initiative for greening their supply chains including the integration of suppliers, distributors, and reclamation facilities [1,2,4,9]. With the increasing acceptance of ISO 14001 environmental standards, there is a greater role for supply chain management in organizational environmental practice [15]. Realizing the significance of these practical examples of organizations involved in managing green supply chains, a tool that will aid in managerial decision making in this complex environment will be beneficial to management and decision makers. We shall describe some important elements of a multidimensional decision environment that faces management. Included among the various elements are influences and relationships of the product life cycle, operational life cycle, organizational performance measurements, and environmentally conscious business practices. These elements serve as the foundation for a decision framework for prioritizing or selecting systems by the organization that will aid in managing green supply chains. 2.1. Product life cycle influence An organizational strategic factor that will influence the management of a supply chain is the product life cycle positioning of the product(s) of an organization. The typical product life cycle is comprised of four phases; a product introduction phase that is characterized by investment in product research and development, a growth phase characterized by increasing production capacity and logistics channels, a maturity phase, where process and cost efficiencies are typically implemented, and a decline phase where the focus is on product divestment. The product life cycle phase will necessarily impact the greening of the supply chain. For example, in the introductory phases, the product is more greatly influenced by the design, and design for the environment issues will play a larger role at this stage. In the mature and decline stages of the product life cycle the improvement of processes and having an efficient reverse logistics system in place will impact the environmental practices of the organization. For a multiproduct analysis, environmental management decisions become increasingly complex. But, within the product portfolio of the company there should be differential environmental strategies and development product life cycle foci which will be depend upon the products’ life cycle maturity. 2.2. The operational life cycle A more tactical set of organizational elements that will influence how the supply chain is to be managed (either internally or externally) can be described by the operational life cycle (or value chain) of an organization. The

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major elements of the operational life cycle will typically include procurement, production, distribution and reverse logistics. We will also include packaging as an element within the operational life cycle. Packaging may not be viewed as a typical stand alone operation but its profound impact on the supply chain allows for its inclusion. The procurement or purchasing decisions will impact the green supply chain through the purchase of materials that are either recyclable or reusable, or have already been recycled. The selection of vendors will also be an important decision at this stage. Vendors who have ISO 14000 certification may be preferable since there is an expectation that the environmental risks associated with these vendors is lessened (which is an analogous argument to selecting vendors that are ISO 9000 certified when reducing risk of poor quality purchases). Reduction of these risks improves the probability that these vendors will also be available for the long term. Whether or not to outsource certain processes or components may also be a concern for the procurement department. Production processes can influence the greening of the supply chain in numerous ways. Some of these impacts include: a process’ capability to use certain materials, capabilities to integrate reusable or remanufactured components into the system (which would require disassembly capacities), and how well the processes are designed for the prevention of waste. It is within this function that much of the environmentally sound techological and process innovations are most advanced (see [3]; p. 14). This focus of environmental innovation could be due to the fact that the production element of the operational cycle is the most internally focused for the organization, allowing the organization to more directly observe the benefits of any new technology or process that is introduced. Distribution and transportation operations networks are also important operational characteristics that will affect the green supply chain. A number of decisions including distribution outlet locations, mode of transportation to be used, control systems, and just-in-time policies, will not only influence the forward logistics network, but also the reverse logistics network. Distribution is also the operation that is most closely tied to the characteristics and requirements of the customer. Thus, customer involvement in distribution systems design and development will more likely provide an effective and efficient distribution network. For, example linking location decisions to those of vendors and customers will improve JIT systems. The reverse logistics operation is probably the least developed and studied of the operational functions. The definition of reverse logistics from an environmental perspective focuses primarily on the return of recyclable or reusable products and materials into the forward supply

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chain. Reverse logistics has also been studied from the perspective of returned and warranted items that may not even have been used. This study of reverse logistics may be considered a subset of the environmental reverse logistics. Pohlen and Farris [12] in a study of the plastics reverse logistics process have identified a number of stages within a reverse logistics channel. Included are: collection, separation, densification, transitional processing, delivery, and integration. Thus, not only does there have to be a network for the reverse logistics collection process, but a number of systems and processes may need to exist for the stages in the reverse logistics channel. Depending on the organization, industry, and product type, the requirements may vary among the stages. Packaging has a strong relationship with other components of the operational life cycle. Packaging characteristics such as size, shape and materials have an impact on distribution due to their affect on the transport characteristics of the good. Better packaging, along with rearranged loading patterns, can reduce materials usage, increase space utilization in the warehouse and in the trailer, and reduce the amount of handling required. Systems that encourage and adopt returnable packaging will require a strong customer supplier relationship as well as an effective reverse logistics channel. With JIT special kit packaging requirements will also be needed. Efficiencies in packaging directly effect the environment. In some countries, take-back legislation on packaging has made the packaging operation and planning a critical environmental logistics consideration. 2.3. Environmentally influential organizational practices There are a number of possible classifications for environmentally conscious business practices. We focus on five major practices or elements that will impact the waste generated by a supply chain. These practices (ordered on a most to least preferable environmental impact scale) include reduction (reduce), reuse, remanufacture, recycle, and disposal alternatives. Reduction is viewed as an in-process, relatively proactive, measure that can be taken by organizations. Typical programs that may aid in this process include total quality management and JIT programs that seek to minimize waste. Introduction of alternative processes and materials may be used to reduce more hazardous materials. Another example of reduction would be to design the product and process to take into consideration environmental factors (also defined as design for the environment). End-of-pipe practices include the remaining four elements. Reuse, remanufacture and recycle practices are similar, but only vary in degree of reuse of the material. Reuse typically keeps the original physical structure of

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the material with little substitution. Remanufacturing requires some disassembly and replacement of parts or components around a core. Recycling can take on new physical and chemical characteristics of the product. Each of these practices may require varying organizational processes and technology. For example, disassembly technology would probably be more preferable for a remanufacturing practice than for a reuse practice. Reuse may require more cleaning type systems than recycling requires. Clearly, the choice of which practice is best for an organization will depend on the organization and product characteristics. The issue of disposal systems and vendors may have the most long-term significance, where disposal of many materials, if not properly treated, may return to haunt an organization in its future. A summary of the possible relationships between operational life cycle and environmentally conscious organizational practices are shown in a flow diagram in Fig. 1. This diagram of the cycles is typical for a single organization. A chain of these figures can be developed that show the relationships among a number of organizations. Indeed, feedback arrows shown in the figure may represent a number of organizations that are involved in a reverse logistics process. 2.4. Organizational performance requirements To complete this decision framework specific organizational performance requirements are included. The categorization of elements for these requirements for our example includes cost, quality, time and flexibility. These generic strategic performance requirements, which may not be environmentally based, are necessary to help identify how well various alternatives can perform on these factors. They are necessary because the alternative that is selected should not only best support the green supply chain, but also make business sense. The use of these organizational performance measures have been supported by a number of strategic thinkers (see [7] and [18], for example). One characteristic of

Fig. 1.

these performance measures is that they are not static. They tend to change over time and will be greatly influenced by the product life cycle. That is, in the introduction phases, flexibility and time may be more important than cost. Whereas cost efficiencies tend to gain importance in more mature environments. These dynamical characteristics are incorporated into the decision framework.

2.5. Green supply chain alternatives

Up to this point we have identified a few alternatives available to organizations for improving the environmental performance of their supply chains. These alternatives may include technological, process, or organizational characteristics. For example one such alternative might be an organizational goal to improve the total quality environmental management (TQEM) [10] within and between organizations. Similar to total quality management, TQEM is a pervasive program which should include suppliers and customers. ISO 14000 certification may also be a goal for the organization and its suppliers. This alternative is based on maintaining documentation and building an information network. Some organizations who have already gone through ISO 9000 certification may find this alternative easy to implement with little additional cost and effort, and thus may be preferable to other alternatives. Other alternatives may be information systems such as electronic data interchange which may be justified for other reasons, but can be evaluated from a greening perspective. These three examples are only a few, emerging technologies, models, and processes that have yet to be developed can be evaluated using the proposed decision framework as well. A good discussion of various systems, requirements and alternatives that can aid the development of green supply chains can be found in [15,19]. The decision framework is now presented.

Functional model of an organizational supply chain with environmentally influential practices (adapted from Sarkis [14]).

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3. The decision framework The decision framework is represented by an “analytical network hierarchy”, which varies from a standard decision hierarchical decision structure as defined by the standard analytical hierarchy process. The variation occurs primarily because two way and ‘looped’ relationships are allowed among the various levels. These levels may also be defined as clusters. These relationships represent multiple dependencies and interdependencies among the elements within the clusters. Fig. 2 shows a “high level” description of the analytical hierarchy network, which does not detail the components within each cluster. The objective or goal of the organization, which appears on the right hand side of Fig. 2, is to develop improved green supply chains. This objective will be influenced by the various clusters that have been previously described in this paper. An example set of relationships among the clusters is shown. These relationships may vary due to assumptions made by the decision-maker, and the level of complexity that they wish to model. The arrows represent the relationships among the clusters. For example, the performance measures and their relative priority or importance (which will be determined using the ANP technique described below) will be dependent upon the stage of the product life cycle. Another set of relationships exists between the organizational performance measures and the operational life cycle elements. The relative importance of different performance measures may be allowed to vary among the operational life cycle elements. For example, quality may be more important than the time performance measure for the packaging function. Within the network, the relative impact (importance) of each alternative will be evaluated for both the performance measures and the environmental practices. The most complicated set of relationships in this model exist for the operational life cycle cluster. There is a two-way dependency between the operational and product life cycles. The relative importance of each

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operational life cycle element will be dependent on what stage of the product life cycle is being considered. In addition, the importance of each product life cycle with respect to a given operational element will also be determined (e.g. the early stages of the product life cycle will have more of an influence on the procurement operations than the decline stages). This model will also assume that there is internal feedback and dependencies among each of the operational life cycle elements. For example, some organizations will require procurement capabilities if they plan on producing products more so than if they were focused on distribution as their major business. These interdependencies within a cluster will have to be determined for each organization and will be impacted by organizational structure. The various environmental practices may also play distinct roles within the operational life cycle. An organization, due to its product’s characteristics, may want to focus its efforts on reduction in the production operation rather than reuse. The effect would be to give a relatively larger importance valuation on reduction than reuse for the production element. Similarly, for the packaging portion, the reuse capability may be more important than disposal, and so on. A detailed representation of all the clusters, the elements within the clusters, and their relationships are shown in Fig. 3. The terms within the parentheses below each component in Fig. 3 are used to denote each of the elements within the supermatrix, which is discussed below. The letters in parentheses near each arc represents a sub-matrix that will represent the relationships within the supermatrix.

4. Evaluating the analytical network hierarchy The evaluation methodology will be composed of two phases. The first phase will focus on the development of pairwise comparisons for each of the dependency relationships to determine their relative importance weights. These weights will be used as an input to the

Fig. 2. High level graphical representation of clusters and influence relationships for decision framework for managing and improving the green supply chain.

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Fig. 3.

Graphical representation of relationships for the green supply chain evaluation framework.

systems-with-feedback supermatrix to help determine the network influences from among the various relationships diagrammed in Figs. 2 and 3. The supermatrix evaluation, the second phase, will encompass three steps, the formation of the supermatrix, the normalization of the supermatrix (making it “column stochastic”) and convergence to a solution. The converged supermatrix will provide us with the relative priorities for each of the alternatives considered within the decision framework. To maintain brevity exposition of the calculations will only describe examples of the major steps in the two stage process. In addition, some example questions that would be used to elicit pairwise valuations are also presented. 4.1. Pairwise comparison evaluations The fundamental decision maker inputs required for the ANP technique are the pairwise comparisons of the elements within each cluster, from which pairwise comparison matrices are formed. These pairwise comparison matrices and their valuation elicitation are similar to those that are used for the AHP approach. For details on the use of AHP and its various calculations see [13]. A pairwise comparison matrix is required when the relative importance of lower level elements are to be determined for thier ‘controlling’ element. For example, to determine the relative importance of the operational life cycle elements (the lower level elements) to the introduction phase of the product life cycle elements (the controlling element) a number of pairwise comparison questions will be asked of the decision-maker. Once such question

is “how much more important is procurement than production operations in the introductory phases of a product’s life cycle?” In this case, since we have 5 dependent level elements within this cluster, 10 pairwise comparison questions need to be answered for a complete set of comparisons. An illustrative pairwise comparison matrix that shows the relative importances of the operational life cycle elements within the introductory phase of a product life cycle is shown in Table 1. The valuation scales used in the example are those recommended by Saaty [13], where 1 is equal importance, 3 is moderate importance, 5 is strong importance, 7 is very strong or demonstrated importance, and 9 is extreme importance. Even numbered values will fall in between importance levels. Reciprocal values (e.g. 1/3, 1/5, etc.) mean less importance, strongly less importance, etc. Only the upper triangle of the matrix needs to be completed. The lower triangle of the pairwise comparison matrix is composed of reciprocal values. For example, in Table 1, we see that procurement is moderately more important than distribution during the introductory phase of a product life cycle. Thus, the value for a13=3, whereas a31=1/3 (or distribution is moderately less important than procurement during the introductory phase of the product life cycle). Once all the pairwise comparisons are complete, the relative importance weight for each component is determined (these results are shown in the final column of Table 1). Given that A is the pairwise comparison matrix, the weights can be determined by expression (1). Aw ⫽ lmaxw,

(1)

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403

Table 1 Pairwise comparison matrix for operational life cycle elements relative importances during introduction phase of the product life cycle Introduction phase

Procurement

Production

Distribution

Reverse logistics

Packaging

Importance wts.

Procurement Production Distribution Reverse logistics Packaging

1.000 0.167 0.333 0.125 0.500

6.000 1.000 2.000 0.500 5.000

3.000 0.500 1.000 0.250 2.000

8.000 2.000 4.000 1.000 6.000

2.000 0.200 0.500 0.167 1.000

0.448 0.073 0.151 0.043 0.285

where λmax is the largest eigenvalue of A and w is the relative importance weights or priority vector (actually the eigenvectors for the principal eigenvalue λmax). The values were calculated using MATLAB software. Pairwise comparisons leave room for possible intransitive preference relationships and inconsistencies. A consistency index (C.I.) and consistency ratio (C.R.) also need to be calculated. The consistency index for a pairwise comparison matrix is determined by: C.I. ⫽

lmax⫺n n⫺1

(2)

where n is the number of components that are evaluated in the pairwise comparison matrix. The C.R. is calculated by taking the C.I. and dividing by a random inconsistency (R.I.) value. A random inconsistency table exists in most AHP and ANP reference books, see [13]. For a pairwise comparison matrix to be consistent, C.R.⬍0.10. In Table 1 we show the values for λmax, C.I., and C.R. We see that this is a relatively consistent set of weights. The priority vector shows that for this organization and industry, procurement (0.448), followed by packaging (0.285), seem to be the functions that are deemed most important for the early stages of a product’s life cycle. All these values can be easily calculated using AHP software such as Expert Choice. The priority vectors for each pairwise comparison matrix will be needed to complete the various supermatrix submatrices. We will need a total of 39 priority vectors to complete our supermatrix. This requirement means that 39 pairwise comparison matrices must be completed. The pairwise comparison matrix results used below were all tested for achieved the consistency goals. 4.2. Supermatrix formation The supermatrix (M) is formed from a number of submatrices that are used to model Figs. 2 and 3 in matrix notation. The supermatrix and its general submatrices are shown in Fig. 4. There will be 9 sub-matrices (A, B, C, D, E, F, G, H and J) that will be formed using the priority vectors. An additional identity sub-matrix (I) is added for the alternatives cluster for computational requirements. The formation of sub-matrix C will require the deter-

Fig. 4. General submatrix notation for supermatrix.

mination of the relative impact of each operational life cycle phase on each of the four product life cycle stages. Four priority vectors will be required to complete C. We have already shown one set of priority weights for C this vector begins in the second column and sixth row of the initial supermatrix, which appears in bold lettering in Table 2. 4.3. The solution procedure The supermatrix M is a reducible matrix with a multiple root, as defined by Saaty [13]. To solve for the values of the alternatives, Saaty recommends that the values of M be column stochastic. That is, the sums of the columns should be normalized to equal a value of 1. One method of making M column stochastic is by determining the relative importances of clusters and multiplying their relevant matrix elements by their relative importance score. In this case we just assumed that all clusters were of equal importance. Two adjustments will need to be completed for the supermatrix to be translated into a column stochastic matrix. The first adjustment influences the operational life cycle and performance measure clusters and their impact on the product life cycle elements. Since there are two clusters, each representative submatrix, in this case submatrices C and E,

GSC1 PLC1 PLC2 PLC3 PLC4 Operational phases OLC1 OLC2 OLC3 OLC4 OLC5 Performance criteria PERF1 PERF2 PERF3 PERF4 Environmental Practices ENV1 ENV2 ENV3 ENV4 ENV5 Alternatives ALTA ALTB ALTC

GOAL Product life cycle

0.000 0.000 0.000 0.000 0.000 0.189 0.052 0.186 0.394 0.179 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.448 0.073 0.151 0.043 0.285 0.538 0.105 0.054 0.302 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.104 0.248 0.463 0.070 0.116 0.181 0.069 0.087 0.662 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.075 0.521 0.077 0.186 0.140 0.089 0.285 0.575 0.051 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.159 0.052 0.074 0.562 0.153 0.141 0.141 0.263 0.455 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.373 0.108 0.095 0.424 0.000 0.408 0.059 0.147 0.386 0.131 0.275 0.545 0.050 0.046 0.494 0.249 0.164 0.046 0.000 0.000 0.000

0.000 0.060 0.245 0.568 0.127 0.287 0.000 0.113 0.547 0.053 0.101 0.538 0.284 0.077 0.508 0.096 0.244 0.092 0.06 0.000 0.000 0.000

0.000 0.176 0.540 0.235 0.050 0.065 0.242 0.000 0.242 0.451 0.537 0.210 0.210 0.042 0.195 0.048 0.088 0.090 0.580 0.000 0.000 0.000

0.000 0.053 0.123 0.354 0.470 0.532 0.076 0.291 0.000 0.101 0.067 0.067 0.522 0.343 0.043 0.537 0.188 0.186 0.046 0.000 0.000 0.000

0.000 0.079 0.255 0.588 0.079 0.102 0.107 0.204 0.586 0.000 0.130 0.328 0.502 0.041 0.153 0.340 0.034 0.399 0.074 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.075 0.333 0.592

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.103 0.216 0.682

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.637 0.105 0.258

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.600 0.200

0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.117 0.268 0.614

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.600 0.200

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.158 0.082 0.761

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.095 0.250 0.655

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 0.334 0.333

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000

GOAL Product life cycle Operational life cycle Performance measures Environmental practices Alternatives GSC1 PLC1 PLC2 PLC3 PLC4 OLC1 OLC2 OLC3 OLC4 OLC5 PERF1 PERF2 PERF3 PERF4 ENV1 ENV2 ENV3 ENV4 ENV5 ALTA ALTB ALTC

Table 2 Initial supermatrix (M) for selection of alternatives for improvement of green supply chain illustration

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J. Sarkis / Journal of Cleaner Production 11 (2003) 397–409

are multiplied by 0.5. The second adjustment will be for the four clusters that influence the operational life cycle represented by submatrices A, D, F and G. Assuming, once again, that each cluster equally impacts the operational life cycle, we multiply submatrices A, D, F and G, by 0.25. The adjusted column stochastic supermatrix (Ms) is shown in Table 3. The final step in the process is to obtain a priority ranking for each of the alternatives. We will determine this ranking by calculating the influence of each of the alternatives on the objective of improving the green supply chain. Saaty states that a simple hierarchy and the additive solution approach is appropriate if strong dependencies among the criteria do not exist. But, in this case the dependencies are considered to be strong. In addition, Schenkerman [16] has shown that the supermatrix approach is capable of reducing the occurrence of rank reversal, thus providing more accurate portrayals of decision-maker preferences. Saaty recommends a simple solution technique to solve this problem by raising the supermatrix Ms to a large power until convergence occurs. In this illustration we only needed to raise the supermatrix to a power of 16 (M16 s ) before convergence occurred within the fourth decimal place (i.e. 10⫺4). The converged supermatrix is shown in Table 4. The relative influences of the alternatives on the objective of improving the environmental performance of the supply chain are shown in the “Goal” column. The results show that alternative B has a higher priority score than alternative C which is better than the current situation (with scores of 0.373, 0.353 and 0.276).

5. Discussion The ANP approach, in practical application, requires significant decision maker input. Its application needs to be targeted to those areas where strategic decision making is required. For example, using this technique for selecting a minor piece of equipment or a day-to-day decision, may not be necessary. Its use should be limited, especially if its use in the decision making process costs more than the outcome of the decision. Yet, for the case of greening the supply chain, the decision is strategic and will broadly effect the operations of not just one, but many organizations. Thus, the investment in making a decision that would profoundly effect the operation of the supply chain clearly requires intensive and robust managerial analysis. Considering critical factors and their interdependencies is necessary for accomplishing this goal. One important consideration in the effectiveness and efficiency of the decision framework begins at the modeling stages. The model and the various dependencies will determine the amount of effort required to arrive at a solution. This effort includes input from decision-makers

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as well as the mathematical approach to solve the problem. Using additive techniques to determine the solution requires less computational effort and may be slightly more intuitive. Yet, more accuracy and consistency generally occurs with the supermatrix solution procedure. The analysis of the supermatrix may not be as intuitive to the decision maker (who usually care about understanding multiple criteria model capabilities, see [11]) as the additive approach. There are some managerial aides that the supermatrix does provide through its summarized structure. One of these aides is a summary of the various linkages and relationships. This summary allows managers to determine what patterns might exist among the various relationships. For example, a management team is able to look at submatrices C and E to see how the priorities of the organization are shifting over a product’s life cycle. The shifting of priorities can be monitored and evaluated by observing this supermatrix. This observation of the managerial significance of the supermatrix has implications for sensitivity analysis. Yet, sensitivity analysis with the ANP approach is still relatively complex when compared to AHP. The computational and data requirements of ANP still make “what–if” analysis geometrically more cumbersome than AHP. There is a need for investigating both sensitivity and what–if analysis for ANP to determine both its strengths and limitations in this area. This investigation is left for future study. An interorganizational application of this decision framework will have to incorporate the perceptions of a number of stakeholders. Not only will there be diverse preferences and perceptions within an organization, but also those of other organizations. Aggregation techniques for AHP (e.g. geometric averaging, consensus scores) can currently be used for each submatrix column in the supermatrix. Alterations to the supermatrix and an addition to the decision framework incorporating a “firm” control hierarchy, where relative organization influences or impact determinations are made can incorporate some of the diversity of opinion or preferences among the organizations. The decision framework has only modeled internal influences and relationships. A number of external factors could be introduced into the model. For example, external factors such as potential for new environmental regulations or cooperation among competing supply chains could also be integrated. The type of environmental forces such as remediation, command and control, or cooperative regulatory policies may also be modeled. These models may be formed as control hierarchies or as part of network hierarchies for decision modeling purposes. A control hierarchy has the characteristic of being separate from a network interdependency model, where the results can be aggregated using an additive model. The network hierarchy would be integrated with the net-

Alternatives

Environmental practices

Performance criteria

Operational phases

GOAL Product life cycle

GSC1 PLC1 PLC2 PLC3 PLC4 OLC1 OLC2 OLC3 OLC4 OLC5 PERF1 PERF2 PERF3 PERF4 ENV1 ENV2 ENV3 ENV4 ENV5 ALTA ALTB ALTC

0.000 0.000 0.000 0.000 0.000 0.189 0.052 0.186 0.394 0.179 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.224 0.037 0.076 0.022 0.143 0.269 0.053 0.027 0.151 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.052 0.124 0.231 0.035 0.058 0.091 0.035 0.044 0.331 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.038 0.261 0.038 0.093 0.070 0.044 0.142 0.288 0.026 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.093 0.000 0.027 0.000 0.024 0.000 0.106 0.079 0.000 0.026 0.102 0.037 0.015 0.281 0.037 0.076 0.097 0.070 0.033 0.070 0.069 0.131 0.136 0.228 0.012 0.000 0.012 0.000 0.124 0.000 0.062 0.000 0.041 0.000 0.012 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.015 0.061 0.142 0.032 0.072 0.000 0.028 0.137 0.013 0.025 0.135 0.071 0.019 0.127 0.024 0.061 0.023 0.015 0.000 0.000 0.000

0.000 0.044 0.135 0.059 0.012 0.016 0.060 0.000 0.060 0.113 0.134 0.053 0.053 0.011 0.049 0.012 0.022 0.022 0.145 0.000 0.000 0.000

0.000 0.013 0.031 0.088 0.118 0.133 0.019 0.073 0.000 0.025 0.017 0.017 0.131 0.086 0.011 0.134 0.047 0.046 0.011 0.000 0.000 0.000

0.000 0.020 0.064 0.147 0.020 0.026 0.027 0.051 0.147 0.000 0.032 0.082 0.126 0.010 0.038 0.085 0.008 0.100 0.019 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.075 0.333 0.592

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.103 0.216 0.682

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.637 0.105 0.258

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.600 0.200

0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.117 0.268 0.614

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.600 0.200

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.158 0.082 0.761

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.095 0.250 0.655

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 0.334 0.333

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000

GOAL Product life cycle Operational life cycle Performance measures Environmental practices Alternatives GSC1 PLC1 PLC2 PLC3 PLC4 OLC1 OLC2 OLC3 OLC4 OLC5 PERF1 PERF2 PERF3 PERF4 ENV1 ENV2 ENV3 ENV4 ENV5 ALTA ALTB ALTC

Table 3 Column stochastic supermatrix (Ms) for selection of alternatives for improvement of green supply chain illustration

406 J. Sarkis / Journal of Cleaner Production 11 (2003) 397–409

GSC1 PLC1 PLC2 PLC3 PLC4 Operational phases OLC1 OLC2 OLC3 OLC4 OLC5 Performance criteria PERF1 PERF2 PERF3 PERF4 PERF5 Environmental practices ENV1 ENV2 ENV3 ENV4 Alternatives ALTA ALTB ALTC

GOAL Product life cycle

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.276 0.373 0.353

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.210 0.436 0.357

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.235 0.450 0.317

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.344 0.308 0.349

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.285 0.402 0.312

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.277 0.361 0.366

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.249 0.356 0.396

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.248 0.385 0.368

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.290 0.382 0.328

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.279 0.356 0.367

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.077 0.542 0.382

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.103 0.216 0.682

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.657 0.196 0.147

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.600 0.200

0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.121 0.575 0.304

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.600 0.200

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.158 0.082 0.761

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.095 0.250 0.655

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 0.334 0.333

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000

GOAL Product life cycle Operational life cycle Performance measures Environmental practices Alternatives GSC1 PLC1 PLC2 PLC3 PLC4 OLC1 OLC2 OLC3 OLC4 OLC5 PERF1 PERF2 PERF3 PERF4 ENV1 ENV2 ENV3 ENV4 ENV5 ALTA ALTB ALTC

Table 4 Converged supermatrix (M16 s ) for selection of alternatives for improvement of green supply chain illustration. Alternative priorities are italicized

J. Sarkis / Journal of Cleaner Production 11 (2003) 397–409 407

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J. Sarkis / Journal of Cleaner Production 11 (2003) 397–409

work model and the solution can be determined through the supermatrix solution approach. A number of other decision factors and criteria can be included in the model, yet the complexity of the decision environment will tend to increase. Increasing complexity, even though more realistic, usually requires additional effort for preference elicitation from decision makers and more complex computations. The tradeoffs between amount of decision maker time and ‘realism’ of the model need to be considered. The application of the ANP approach should not only be concerned with a ‘final’ solution to the problem, but it also should be applied as a learning tool for decision makers and managers to help understand the various linkages among the various components, clusters, and elements. Rank reversal may still be an issue with the ANP approach. Even though the possibilities of rank reversal are lessened with the supermatrix approach, total elimination of these problems needs to be studied. In addition, with the relative novelty of ANP practice and theory, a number of theoretical and mathematical underpinnings still necessitate development.

6. Summary and conclusion The issue of organizations incorporating the natural environment into strategic and operational decisions is a reality that they will or have already encountered. The influences of the natural environment organizational decisions will not only effect the organization that makes the decision, but its customers and suppliers, as well. Incorporating various elements, functions and activities of supply chain management is one method to incorporate some of the systemic organizational and inter-organizational implications of environmentally influential policies. A number of business and environmental factors need to be integrated into this decision. One such decision framework that considers these factors, whose goal is to improve the green supply chain, is introduced in this paper. The major elements and their relationships have a number of interdependencies. These elements include product life cycle, operational life cycle, performance measures, and environmentally influential organizational policy elements. The goal of the framework is to help evaluate a number of alternatives (projects, partnerships, systems or technologies, etc.) that impact these various factors. The decision framework is modeled and solved as an analytical network process (ANP). The formulation of the model and its solution technique are described. Managerial and decision science implications are also presented. The ANP methodology is a robust multiattribute decision making technique for analyzing the major issues facing green supply chains and environmentally conscious business practices, both of which are strategic

in scope. The models and application of the ANP methodology, in the literature and practice, has been quite limited due to its complex characteristics and need for further developments. The major disadvantage of the ANP approach is the large amount of decision-maker input required, even for rather simple networks. The advantage of allowing managers and decision-makers the flexibility to identify and incorporate major interdependencies among many factors and clusters in a “dynamic” fashion, makes this technique a viable alternative to AHP and other multiattribute approaches. Yet, with appropriate software tools, some of which are currently under development, this tool may have as much popularity and application as the AHP approach.

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