It is important to implement sustainability throughout the supply chain and manufacturing process in order to achieve sustainability in a manufacturing system. As part of that effort, selecting an appropriate supplier while taking sustainability into account is a critical requirement in today's manufacturing industry, particularly in emerging economies like Bangladesh. This requirement is even more critical for any industry that utilizes a variety of chemicals in its manufacturing process. This research attempted to develop a multi criteria decision making (MCDM) framework for sustainable supplier selection in the textile dyeing industry, one of Bangladesh's largest chemical-consuming industries. An extensive literature review and expert feedback have identified 15 critical economic, environmental, and social evaluation criteria for sustainable supplier selection. The weights of the identified evaluation criteria were determined using the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Following that, the Weighted Aggregated Sum Product Assessment (WASPAS) method was used to determine the final ranking of suppliers in the textile dyeing sector using the SWARA method's criteria weights. According to SWARA's findings, the most important criterion is "chemical quality," followed by "price of chemical," "meeting delivery time," "employee health and safety," and "environmental management system." The WASPAS method indicates that supplier A, a global chemical supplier based in Singapore, is the most sustainable of all the competing suppliers. Later in this study, a sensitivity analysis was conducted for WASPAS method for various values of (λ) and later comparing its results with various popular MCDM methods. Sensitivity analysis ensures that the results obtained from WASPAS is in fact valid. This study is expected to assist decision-makers in the textile dyeing industries of emerging economies to efficiently select suppliers who are not only economically viable but also environmentally sustainable in the long run.
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