Assessing Big Data Capabilities in Manufacturing Supply Chains: A Pythagorean Fuzzy MAGDM Framework

In the contemporary business landscape, the demand for robust big data capabilities within enterprises has surged, facilitating in-depth analysis and extraction of crucial insights from supply chains to bolster data-driven dynamic decision-making. This study endeavors to develop a meticulous evaluation system tailored for assessing big data capabilities within manufacturing supply chains. Initially, the internal logic of big data capability evaluation criteria is refined by leveraging the Technology-Organization-Environment framework. Subsequently, an advanced Multi-Attribute Group Decision Making model is proposed, seamlessly integrating the Defining Interrelationships between Ranking Criteria (DIBR) method with the Evaluation based on Distance from Average Solution (EDAS) method under Pythagorean fuzzy set theory. To harness expert insights, COWA-Dombi aggregation operators are proposed. The determination of indicator weights is achieved through Pythagorean fuzzy DIBR, followed by employing Pythagorean fuzzy EDAS to compare Big Data Capabilities across various enterprises. This methodological framework is empirically validated through a case study involving six prominent manufacturing companies, offering actionable insights for organizations to leverage strengths and mitigate risks associated with big data implementation in real-world settings. The results underscore the efficacy of the proposed method in effectively assessing big data capabilities within manufacturing supply chains, further validated through comparative and sensitivity analyses.