Green supply chain management with linguistic preferences and incomplete information

Abstract

As firms move toward environmental sustainability, management must extend managements efforts to improve environmental practices across the supply chain. The selection of a suitable green supplier according to green supply chain management criteria (GSCM) is essential for the sustainable development of manufacturing firms. The objective of this study was to select an optimal alternative in the presence of incomplete information and linguistic preferences using multiple GSCM criteria. The goal of GSCM is to reduce a firm's pollution and other environmental impacts. In the proposed method, the weights of GSCM criteria and alternatives are described using linguistic preferences that can be resolved with fuzzy set theory. Subsequently, the rank of each alternative was calculated from incomplete information by applying a grey degree. Moreover, a case study was used to resolve the proposed method, and the results and managerial implications of the analysis are discussed in detail.

Introduction

In recent years, environmental management has evolved to include boundary-spanning activities in the supply chain, and both upstream and downstream activities are included in green supply chain management (GSCM) [1], [2]. A limited understanding of GSCM has hindered the development of a widely accepted framework that characterizes and categorizes a firm's environmental activities. The European Union has established a variety of environmental policies, including RoHS (the restricted use of hazardous substances in electrical and electronic equipment) and WEEE (waste electronics and electrical equipment) Directives. These directives ban manufacturers, sellers, distributors and recyclers of electrical and electronic equipment from launching new equipment that contains hazardous materials on the market [3], [4]. While WEEE directives are aimed at the life cycle of the product, RoHS is targeted at the product design stage [56], [57], [58], [59]. Although environmental regulations and mandatory programs have been implemented, pressure to protect the environment also comes from other external stakeholders.

Currently, a wide variety of studies on GSCM can be found in the literature [5], [6], [7], [8]. Srivastava [8] defined GSCM as a combination of environmental and supply chain management (SCM) activities, including product design, material selection, manufacturing processes, final product delivery and end-of-life product management. Moreover, through GSCM, firms can select from a wide variety of suppliers and leverage resources throughout the firm to eliminate the environmental impacts of supply chain activities [3], [9]. Firms typically expect their suppliers to go beyond environmental compliance and develop efficient, green product designs. In addition, suppliers are expected to assess the life cycle of a product. Nevertheless, the firm's suppliers must satisfy GSCM criteria under the constraint of incomplete information and subjective human preferences (uncertainty); however, this phenomenon has not been thoroughly examined.

GSCM philosophy focuses on how firms utilize the supplier's processes and technologies, as well as the supplier's ability to integrate environmental concerns and enhance the firm's competitive advantage [10]. However, to study and advance the body of knowledge related to GSCM, identification of appropriate measures is necessary. To effectively and empirically advance the theory, greater attention must be focused on employing multi-criteria evaluations, assessing the validity of criteria and modifying unacceptable criteria through extensive literature reviews [2], [6], [7], [8], [11], [12]. Hence, in this study, a number of different criteria that can be used to evaluate GSCM practices were integrated, and a literature review on supply chain and environmental management was performed. Firms can benefit from the development of reliable and valid criteria, and the practitioner can apply these criteria as benchmarks to attain continuous improvement. One objective of the present study was to assist firms in understanding the criteria and implementation. However, the uncertainties and incomplete information are often encountered in the implementation process. In the process of GSCM, the selection of green supplier is always encountered, the multi-criteria decision making (MCDM) tools are always proposed to be applied in the process [2], [7], [8], [12], [13].

In real systems, MCDM is often based on subjective preferences or incomplete information. Grey theory is superior for the theoretical analysis of systems with imprecise and incomplete information within a system of evaluation [9], [14]. Moreover, the evaluation system having incomplete information is so called grey system and the triangular fuzzy numbers in grey system represents a set of numbers with less complete information, the system is always represented as lack of information. Hence, the grey possible degree is to evaluate the incomplete information. The principles of the available theories and modeling schemes for the prediction and diagnosis of an uncertain situation are summarized, and the practical applications of theories and linguistic preferences are reviewed. People often employ natural language to express thoughts and subjective perceptions; however, the meaning of words in natural languages is often vague. Although the meaning of a specific word may be well defined, when that word is used to define a set, the boundaries of the set can become uncertain [3]. Hence, the proposed method uses fuzzy set theory to appropriately express the determination of human judgment in GSCM criteria. The second contribution of this study is the development of a hybrid approach for the establishment of GSCM criteria for the selection of an optimal alternative.

In an effort to determine the uncertainty in the proposed model, GSCM criteria were integrated and an optimal alternative was selected. This paper contributes to GSCM literature by developing valid and reliable criteria based on information obtained from GSCM literature and experts in the field. Moreover, this study developed an approach based on grey theory for the determination of linguistic preferences. In Section 2 of this paper, a literature review of GSCM practices is provided. In addition, the methodology used to develop GSCM criteria was validated and is presented in Section 3. Section 4 presents the results of this study, and the implications of the results are discussed in Section 5. The paper is concluded in Section 6 by summarizing the results, implications, limitations and potential topics of future research.