Multi-criteria decision-making (MCDM) methods are smart tools to deal with numerous criteria in decision-making. These methods have been widely applied in the area of sustainable supply chain management (SSCM) because of their computational capabilities. This paper conducts a systematic literature review on MCDM methods applied in different areas of SSCM. From the literature search, a total of 106 published journal articles have been selected and analyzed. Both individual and integrated MCDM methods applied in SSCM are reviewed and summarized. In addition, contributions, methodological focuses, and findings of the reviewed articles are discussed. It is observed that MCDM methods are widely used for analyzing barriers, challenges, drivers, enablers, criteria, performances, and practices of SSCM. In recent years, studies have focused on integrating more than one MCDM method to highlight methodological contributions in SSCM; however, in the literature, limited research papers integrate multiple MCDM methods in the area of SSCM. Most of the published articles integrate only two MCDM methods, and integration with other methods, such as optimization and simulation techniques, is missing in the literature. This review paper contributes to the literature by analyzing existing research, identifying research gaps, and proposing new future research opportunities in the area of sustainable supply chain management applying MCDM methods.

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier’s reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

Purpose: Managing supply chain risk is a crucial element in ensuring the long-term sustainability of any organization or industry. As such, identification of risks and deploying their mitigation strategies should be the focal point to sustain in the long run. The risks that are faced by food processing supply chains are gaining prominence, given more consumers requiring higher quality products while ensuring traceability. In essence, this research focuses on the supply chain risks and mitigation strategies in the spice industry of an emerging economy, Sri Lanka.Design/methodology/approach: This paper integrates two popular multi-criteria decision-making (MCDM) techniques, such as the analytical hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the supply chain risks and to derive their mitigation strategies for the spice industry.Findings: Findings show that “inability to meet quality requirements” has been established as the most significant risk in the Sri Lankan spice industry. On the other hand, “vertical integration” (backward integration) has been discovered as the key mitigation strategy to ameliorate the effects of supply chain risks in this sector.Research limitations/implications: This study is exploratory, and more empirical data and statistical analyses are needed to further validate the outcomes of the study.Originality/value: Despite being one of the largest trade exporters in Sri Lanka, the spice industry gets scant attention to the identification and mitigation of the risks. The authors explored the supply chain risks in the spice industry and then prioritized the suitable mitigation strategies using an integrated AHP-TOPSIS method.