Investigating and Modeling the Critical Barriers Hindering the Adoption of Data-Driven Decision Making in Advanced Manufacturing Systems

Industry 4.0 has enabled manufacturing organizations to integrate big data analytics for designing an intelligent and flexible decision-making process known as data driven decision making (DDDM). The study employs DEMATEL-ISM computational methods to evaluate and establish a hierarchical structure of critical barriers hindering the adoption of DDDM in AMS. The research offers a comprehensive analysis of the critical barriers to the adoption of DDDM in AMS by combining industry surveys, expert consultations, and the entropy-based threshold value determination (MMDE algorithm). This study identified nine crucial barriers and divided them into three groups: technical and data, organizational and managerial, and socioeconomic. The findings disclose that lack of awareness and planning, lack of commitment from top management, and concerns related to cyber security and privacy are the most significant obstacles preventing the adoption of DDDM in AMS. Through MICMAC analysis, it has been determined that three barriers serve as driving factors, four as dependent factors, and two as linkage factors. This research provides organizations with valuable insights and functions as a benchmark for overcoming obstacles and managing resources effectively during DDDM adoption.