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Data-driven approaches for decision-making in advanced manufacturing systems: a systematic literature review

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  • Vimlesh Kumar Ojha
  • Sanjeev Goyal
  • Mahesh Chand

Abstract

Rapid automation in advanced manufacturing systems enable them to capture, store and analyse data and adopt data-driven decision-making techniques. This study investigates the applications of data-driven techniques like big data analytics, AI, and ML in advanced manufacturing systems for decision-making. The paper identifies the various factors that affect the adoption of data-driven manufacturing techniques and reviews the framework strategies for their adoption. Applications of data-driven techniques in manufacturing, such as predictive maintenance, fault analysis, forecasting, and quality improvement, are discussed in detail. The authors also highlight the challenges associated with implementing data-driven decision-making (DDDM) in the manufacturing industry, such as data quality, privacy concerns and skilled workforce requirements. The study concludes that DDDM in AMS increases productivity, reduces operational costs, improves manufacturing operations and increases competitiveness. However, further research is needed to address the identified challenges and develop effective DDDM implementation strategies in AMS.

Suggested Citation

  • Vimlesh Kumar Ojha & Sanjeev Goyal & Mahesh Chand, 2025. "Data-driven approaches for decision-making in advanced manufacturing systems: a systematic literature review," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 53(4), pages 474-498.
    • Handle: RePEc:ids:ijores:v:53:y:2025:i:4:p:474-498
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