A Bayesian-based decision modeling approach for prioritizing barriers to sustainability in packaging supply chain

Due to globalization, competitiveness, and technological advancement, packaging enterprises are facing numerous challenges/obstacles in achieving sustainable and resilient infrastructure in their supply chain networks. Therefore, this study aims to propose a framework for barriers to sustainability in packaging supply chain systems (PSCS) that can disrupt the goal of enterprises to adopt sustainable solutions. A total of 29 potential barriers to sustainability in PSCS were identified through an in-depth literature review; these barriers were categorized into six main categories with the aid of experts. We also validated the categorized barriers by using Fleiss Kappa statistics to assess the level of agreement among multiple experts. Then, a multi-criteria decision-making (MCDM) methodology called the Bayesian best–worst method (BBWM) was utilized to analyze and prioritize the identified barriers based on the final weight obtained. This research also facilitates the interrelationship among potential barriers by developing a visual credal ordering network that considers the probabilistic approach, which also deals with any uncertainty and vagueness in the expert’s inputs. The outcomes of our study emphasized that financial barriers like ‘high capital investment,’ ‘vagueness in return liquidity,’ and technological barriers like ‘lack of IoT-enabled infrastructure’ were the most influential barriers that must be dealt with utmost care by packaging firms in developing a sustainable ecosystem. These results can be utilized by the managers, policymakers, and executioners of the packaging industries for prioritization and subsequent consideration of potential barriers. In addition to that, we also proposed enhancement strategies to mitigate the impact of the most significant potential barriers faced by packaging industries in developing countries.