Keywords

1 Introduction

For sustainable supply chain partners, supplier selection using green manufacturing approach is one of the important tasks. The supplier should improve the performance of the supply chain keeping in mind environmental aspects along with social and economic aspects [1]. Green supply chain management (GSCM) is fast changing and growing concept in emerging countries. To address the influence and relationship between supply chain management and the natural environment, the ‘green’ dimension is added to supply chain management (SCM). The current awareness in the environmental aspects has made supplier to think and give more emphasis on the environment-friendly criteria [2]. Design for the environment, life-cycle analysis, and total quality environmental management are considered as popular environmentally conscious practices [3]. The fuzzy analytic hierarchy process (FAHP) approach is adopted in this study to develop a supplier selection model. Introduction to fuzzy sets and fuzzy numbers are given initially. In all, seven factors are considered, namely green manufacturing, green design, environment performance assessment, green logistic design, quality, customer co-operation, and green costs with 47 sub-factors.

2 Literature Review on Green Supplier Selection Criteria and Supplier Selection Models

In recent years, various researchers have identified approach toward green supply chain management (GSCM) practice. While the works on the evaluation and/or selection of suppliers are quite a few, those that concern environmental issues are limited. Baskaran et al. [4] assessed various vendors from Indian textile and clothing industry using different green manufacturing measures. For the purpose of metering the performance of the manufacturing sector, Bhateja et al. [5] studied different activities of the supply chain processes of the various Indian manufacturing industries. Deif [6] offered system model for the novel sustainability pattern with an architecture for the designing, planning, and controlling of various green manufacturing undertakings. Kumar et al. [7] explored different practices related to green supply chain management to be adopted by various industries of electrical and electronics products. The environmental performance and green supply chain management practices were studied, and findings were examined using mean score method. Relationships between green supply chain management (GSCM) practices and GSCM driver in Taiwan’s textile and apparel manufacturers were investigated thoroughly [8]. The components and elements of green supply chain management were discussed to determine how those components served as a basis for the decision framework [9]. An integrated and fresh look into the area of GSCM was given comprehensively with the state-of-the-art literature review in the study. The literature on GSCM was covered from its conceptualization, primarily taking a reverse logistics angle [10]. Deshmukh and Sunnapwar [11] used factor analysis approach to discuss various environmental performance measures used in different manufacturing organizations. For green supplier selection, different performance measures were validated by mean method [12].

Based on the results of an investigation of 151 respondents, Punniyamoorthy et al. [13] established a new model using structural equation modeling and fuzzy analytic hierarchy process technique. Ataei et al. [14] utilized FAHP for determining the weights of the criteria. The ranking of the sawability of carbonate rocks was also carried out. Chang [15] presented a methodology for management of fuzzy AHP. Ozdagoglu et al. [16] used analytic hierarchy process (AHP) and fuzzy AHP method. Comparison was made with a case study including the decision-making about employee selection for shop floor of manufacturing platform applied in food industry. To help the industrial specialists in the performance evaluation in a fuzzy environment, Sun [17] developed a model based on the fuzzy analytic hierarchy process (FAHP) and the technique for order performance by similarity to ideal solution (fuzzy TOPSIS). Fuzzy AHP was used by Kahraman et al. [18] for selecting best supplier firm for a white good manufacturer established in Turkey. Deshmukh and Sunnapwar [19] conducted study on use of AHP for green supplier selection in Indian industries.

3 Fuzzy Analytic Hierarchy Process, Fuzzy Sets, Fuzzy Numbers, Algorithm for Fuzzy AHP

To solve multiple-criteria decision-making problems, the analytic hierarchy process (AHP) has been widely used. Because of uncertainty and fuzziness in the individual’s judgment, a pairwise comparison with an AHP may not be possible to accurately find the decision-makers’ decision. The human preference model is uncertain in many real-world circumstances, and decision-makers might be hesitant to give precise numerical values to the comparison judgments. As human decisions are given with crisp numbers in AHP, there is a necessity to introduce fuzzy logic with pairwise comparison. The fuzzy AHP is based on the fuzzy interval arithmetic with triangular fuzzy numbers and the confidence index with an interval mean approach to determine the weights of evaluative elements. An approach based on fuzzy AHP can benefit to reach an operative decision. By using this method, the uncertainty and fuzziness in the decision process can be dealt with. Fuzzy set theory was introduced by Zadeh in 1965 to solve problems involving the absence of sharply defined criteria [15]. Fuzzy set theory looks a lot like human thinking in its use of estimated information and indecision to generate conclusions. It is designed to mathematically represent uncertainty and fuzziness that is intrinsic to many problems. Fuzzy set theory includes fuzzy logic, fuzzy arithmetic, fuzzy mathematical programming, fuzzy topology, fuzzy graph theory, and fuzzy data analysis; however, the term fuzzy logic is often used to define all of these [18]. Triangular fuzzy numbers are defined by three real numbers, given as (l, m, u). The parameters l, m, and u show the lowest possible value, the middle promising value, and the upper possible value that indicates a fuzzy event. A triangular fuzzy number (TFN), M, is shown in Fig. 1.

Fig. 1
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A triangular fuzzy number, M

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The extent FAHP is used in this study, which was introduced by Chang [15]. X = {x1, x2, x3,…, xn} to be considered as an object set, and G = {g1, g2, g3,…, gn} to be a goal set. Every object is considered, and extent analysis is done for each goal as per the method of Chang’s extent analysis. M extent analysis values for each object can be obtained, with the signs given as follows:

where

$$M_{gi}^{1} ,M_{gi}^{2} ,M_{gi}^{j} ,\quad i{ = 1}, 2\ldots n ,$$

\(M_{gi}^{j} \left( {j = 1,2, \ldots , \, m} \right)\) all are triangular fuzzy numbers (TFNs).

Figure 2 illustrates intersection between M1 and M2 where D is the ordinate of the maximum juncture point d between \(\mu_{{M_{1} }}\) and \(\mu_{{M_{2} }}\) to associate M1 and M2; we need both the values of V (M1 ≥ M2) and V (M2 ≥ M1).

Fig. 2
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Intersection between M1 and M2

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4 An Application in Organization (Case Study)

This study focuses on the procurement of various components from three local suppliers Supplier 1, Supplier 2, and Supplier 3, which have been preselected in the approved suppliers’ list of one of the leading manufacturing organizations from India. The goal was to find the best supplier taking into account the green manufacturing approach. Figure 3 provides structure for selecting best green supplier. Seven main criteria are selected for assessment are green manufacturing (GM), green design (GD), customer co-operation (CC), green costs (GC), quality (QTY), environment performance assessment (EPA), and green logistics design (GLD). A sample of the questionnaire form is shown in Table 1.

Fig. 3
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Structure for the selection of the best green supplier

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Table 1 A sample of the questionnaire form
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A comparison is made in sub-attributes and main attributes by decision-maker after creating hierarchy. After the information is taken, the pairwise evaluations are carried out. It is necessary to represent all pairwise comparison judgements into fuzzy triangular numbers. Table 2 represents relation between fuzzy comparison judgments and the main goal. Calculation is carried out according to fuzzy extent analysis. After the normalization of the values, priority weights with respect to main goal are calculated as—0.3010, 0.2488, 0.0903, 0.0984, 0.0313, 0.1350, 0.0951. It was found that the significant element in the green supplier selection procedure is quality as it is having maximum priority weight value (0.3010).

Table 2 Fuzzy evaluation matrix with respect to goal
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The same calculations were applied to the other pairwise comparison matrices and the priority weights of each main attribute, sub-attribute, and alternative were calculated. Mentioned priority weights have indicated for each criterion in Table 3. The priority weights of the alternatives with respect to sub-attributes are calculated. The combination of priority weights of main attributes and sub-attributes was done to find priority weights of the different vendors.

Table 3 Priority weights for main criteria
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Main attributes and sub-attributes weights were used to select best alternative based on the questionnaire forms used. In order to reduce the complex calculations and to calculate the priority weights, macros in Excel were used. The performance of suppliers with respect to each main criterion is shown in Table 4, and corresponding values are shown diagrammatically in Fig. 4. Finally, the weights were calculated keeping into account different alternatives and the main attributes.

Table 4 Performance of suppliers with respect to each main criterion
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Fig. 4
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Performance of suppliers w.r.t. main criterion

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The priority weights for the alternatives are found to be 0.4834, 0.3377, and 0.1789.

5 Conclusion

Green supplier selection process plays an important role in today’s complex environment. This work provides a scientific approach to the green supplier selection process. It first identifies those green supplier selection criteria which are considered to be important in Indian manufacturing sector. The extent fuzzy AHP is used in this study for selecting best green supplier. Fuzzy AHP approach appears more effective in decreasing the uncertainty in calculation of relative weights assigned to various criteria. For selecting the most appropriate supplier alternative, preferred degree related to each of the criteria was assessed. The uncertainties involved in the data acquired were efficiently represented and processed by using the extent fuzzy approach to make appropriate decision. Supplier 1, having highest priority weight, is determined as the best green supplier as a result of this study.