Evaluation of Barriers Toward Data-Driven Supply Chain Sustainability Via Single-Valued Pythagorean Piprecia

Abstract

Sustainable supply chain management (SSCM) is a holistic approach that encompasses economic, social, and environmental dimensions, enabling firms to enhance their long-term competitiveness by meeting legal requirements and strengthening brand equity. The effective implementation of this approach necessitates a strong emphasis on data-driven decision-making. Accordingly, we aimed to identify the key barriers hindering the implementation of data-driven sustainable supply chain practices and to explore potential strategies to overcome these challenges. In the initial phase of the study, a comprehensive literature review was conducted to identify the major barriers to implementing data-driven sustainable supply chains. Subsequently, the relative importance of these barriers was assessed with input from top and mid-level managers working in manufacturing sector enterprises. The identified barriers were then prioritized using the Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) method based on Pythagorean fuzzy numbers. Finally, solution proposals were developed to address the most critical barriers. The study revealed that organizational barriers constitute the most prominent category, representing 29.86% of the total identified obstacles. Closely following are technical barriers, which account for 26.41% and reflect the difficulties associated with implementing and integrating digital technologies. Internal and external environmental barriers are similarly substantial, comprising 25.87% of the total. In comparison, economic barriers make up the smallest share, with a relative weight of 17.86%. The number of researchers analyzing the importance weights of barriers in the context of SSCM 4.0 remains limited. The utilization of a more contemporary and robust method compared to previously applied techniques for determining these weights enhances the originality of this study.