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ABSTRACTData-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.KEYWORDS: Advanced manufacturing Systemscritical success factors (CSFs)DDDMadoptionbig data (BD)ISM-DEMATEL Article highlight Produces a roadmap for the implementation of DDDM in AMS.Exploring the key drivers that enable the effective implementation of DDDM in AMS through the identification of critical success factors (CSFs).Analysing the CSFs and modelling them on the basis of their prominence using an integrated ISM-MICMAC-DEMATEL methodology.Abbreviations DDDM=Data-driven decision makingAMS=Advanced Manufacturing SystemsCSFs=Critical Success FactorsBDA=Big data analyticsDT=Digital transformationLR=Literature reviewIoT=Internet of ThingsCPS=Cyber-physical systemsSME=Small & medium-sized4IR=Fourth industrial revolution or Industry 4.0SM=Smart ManufacturingISM=Interpretive structural modellingAcknowledgmentsIndustry professionals from India's manufacturing sector were a huge help to the authors in identifying and comparing factors and validating findings, and the authors are grateful for their assistance.Disclosure statementIt should be noted that the research discussed in this publication was not influenced by any financial or personal conflicts of interest of the authors.Data availability statementAll data generated or analysed during this research are included in this article.
Ojha et al. (Fri,) studied this question.