A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms
Introduction
The forceful drivers for the fast growth of reverse logistics are many, including the increasing shortage of natural resources, environmental law, the realization of backward flow value, e-business development, good reputation requirement, customer satisfaction, and the population of information systems (Škapa & Klapalová, 2012). Rogers and Tibben-Lembke (1999) defined reverse logistics as “The process of planning, implementing, and controlling the efficient, cost-effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal” (Roger & Tibben-Lembke, 1999).
The goal of reverse logistics is to focus on the reverse flow of materials by maximizing their value (Kannan, Pokharel, & Kumar, 2009). Products are returned through the supply chain for a variety of reasons: commercial returns, warranty returns, reusable articles, product recalls, end-of-use returns (EOU) and end-of-life returns (EOL) (Han & Ponce-Cueto, 2016). The rate of returns has increased by 57% for retailers and 43% for manufacturers respectively over the past three to five years, as surveyed by Accenture (Zaarour, Melachrinoudis, Solomon, & Min, 2014). Many businesses suffer significantly from poor management of the returns. Only returned products, as reported by CNBC, cost firms more than $260 billion a year and an average profit loss of 10% (McKevitt, 2016).
The efficient implementation of reverse logistics requires an appropriate communication platform. Social commerce, a new business model of e-commerce, makes use of Web 2.0 technologies and social media to support social-related exchange activities. It offers a platform connecting consumers and companies integrating e-business, customer relationship management, technology support, and information systems. Given the enormous effect of returned items on the company's bottom line and social commerce´s popularity, an increasing number of firms have made efforts to streamline their reverse logistics process in social commerce platforms (Tavana, Zareinejad, Caprio, & Kaviani, 2016).
This study focuses on identifying the criteria for effective management of returns through social commerce platforms and evaluating the reverse logistics efficiency of top global companies that use social commerce platforms. Firstly, a previous study, based on a thorough review of literature, identified four main criteria with sixteen sub-criteria from social commerce activities (Han & Trimi, 2017). Then, this research invited five experts to evaluate the reverse logistics performance of three top global companies on these criteria. The identification and evaluation of key evaluation criteria will help researchers and managers in strategic decision-making for reverse logistics implementation.
In this study, we applied a fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique with FLINTSTONES (a software tool) to assess how efficient companies use social commerce platforms for their reverse logistics process. TOPSIS is based on the notion that the chosen alternative should have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution (Hwang & Yoon, 1981). While effective management of reverse logistics is vital to enhance customer satisfaction and improve organizational performance, it is difficult to evaluate the performance of the system due to lack of measurable standards and limited data. In this study, the fuzzy set theory is introduced to model vagueness and uncertainty, which is combined with TOPSIS to form fuzzy TOPSIS. Fuzzy TOPSIS has become popular among researchers and practitioners because of its numerous advantages as follows:
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It is practical and has the ability to provide solutions with partial or incomplete quantitative information (Awasthi et al., 2010, Awasthi et al., 2011b);
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It allows expressing preferences in the form of natural language parameterized by triangular fuzzy numbers;
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It can compare the best and the worst solutions quantitatively;
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It is easy to implement the algorithm (Chang & Tseng, 2008).
The proposed approach is comprised of three steps as shown in Fig. 1. In Phase 1, we identify the criteria for assessing the reverse logistics process on the social commerce platform. In Phase 2, experts are invited to provide linguistic evaluation ratings of the three global companies against the identified criteria. Fuzzy TOPSIS is adopted to generate aggregate scores for assessment and evaluation of the reverse logistics performance. The sensitivity analysis is also applied for testing the robustness of the method. In Phase 3, software tool FLINTSTONES is used to check and adjust the evaluation result.
The rest of the paper is organized as follows. Section 2 presents theoretical background, followed by a note on research methodology in Section 3. Section 4 provides a numerical illustration for the application of fuzzy TOPSIS. Finally, research results and future research needs are discussed in Section 5.
Section snippets
Literature review
A review of literature indicates that reverse logistics in social commerce platforms has received only limited attention. Also, fuzzy TOPSIS has not been applied to evaluating reverse logistics performance in the social commerce platform (Behzadian, Otaghsara, Yazdani, & Ignatius, 2012). We reviewed the theoretical background from two aspects: the literature on solving reverse logistics-related problems using fuzzy TOPSIS and commonly used criteria for performance assessment.
Fuzzy TOPSIS
Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), one of the classic methods for solving multiple criteria decision making (MCDM) problems, was first developed by Hwang and Yoon (1981). The principle of this method is that the most preferred alternative should have the shortest distance from the positive ideal solution (PIS), i.e., the solution that maximizes the benefit criteria and minimizes the cost criteria; and the farthest distance from the negative ideal solution
Application
Fuzzy TOPSIS provides a systematic approach for identifying, evaluating and monitoring reverse logistics performance with a set of criteria. The advantage of the proposed method is its practical applicability and ability to afford a solution when information is poor (partial or limited quantity). To illustrate the proposed approach, this study provides a numerical application.
Discussion
From the results above, we can conclude that firm A1 has the best performance of reverse logistics practices in the social commerce platform. From Table 6, the aggregated fuzzy weights of the Customer relationship (C21), Usage risk (C23), Reviews (C42) and Quality control (C22) are (7, 9.000, 9), (5, 8.200, 9), (5, 7.800, 9) and (5, 7.800, 9) respectively, which rank as C21 > C23 > C42 = C22 > other criteria; while Cloud computing (C33) and Service-oriented architecture (C34) are (1, 5.800, 9)
Conclusion
The return ratio of products has been significantly higher than before the Internet powered digital age (Liu, Chen, Li, & Liu, 2015). At the same time, the rapidly growing social media and Web 2.0 technology have transformed social commerce as an easy and fast tool to effectively manage the reverse logistics process. Social commerce is becoming increasingly popular with technological advances and consumers´ concerns for sustainability (Khor, Udin, Ramayah, & Hazen, 2016). This unique
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