Data-driven decision making in advanced manufacturing Systems: modeling and analysis of critical success factors

Abstract

ABSTRACT Data-driven decision making (DDDM) in advanced manufacturing systems (AMS) is the use of data to make smart decisions that improve manufacturing operations. Companies can make themselves more competitive, cut costs, and improve their production by using data analytics. The investigation of critical success factors aids companies in identifying vital areas that demand attention for the implementation of DDDM in AMS. This comprehension enables companies to devise effective strategies for the successful adoption of DDDM within AMS. In this research, twelve critical success factors that affect the use of DDDM in AMS were discovered and statistically analysed using an integrated methodology of ISM, MICMAC, and DEMATEL to create a hierarchical model. This research paper suggests that companies should focus on developing a skilled workforce and creating a data-driven culture to successfully adopt DDDM in AMS. Additionally, the findings highlight the importance of top management support and government initiatives in promoting the adoption of DDDM in manufacturing.