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

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.

KEYWORDS:

• Advanced manufacturing Systems
• critical success factors (CSFs)
• DDDM
• adoption
• big 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 making
AMS	=	Advanced Manufacturing Systems
CSFs	=	Critical Success Factors
BDA	=	Big data analytics
DT	=	Digital transformation
LR	=	Literature review
IoT	=	Internet of Things
CPS	=	Cyber-physical systems
SME	=	Small & medium-sized
4IR	=	Fourth industrial revolution or Industry 4.0
SM	=	Smart Manufacturing
ISM	=	Interpretive structural modelling

Acknowledgments

Industry 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 statement

It 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 statement

All data generated or analysed during this research are included in this article.