Analytics-Based Supply Chain Optimization
Keywords:
Supply Chain Optimization, Industrial Data Analytics, COVID-19 Supply Chain Disruptions, Data Science Applications, Operations Research Methods, Predictive Maintenance Analytics, End-To-End Supply Chain Visibility, Inventory Optimization Techniques, Manufacturing Data Sources, Logistics Analytics, Data Quality Dimensions, Data Integration Challenges, Interoperability Limitations, Factory-Level Operational Systems, Global Supply Chain Visibility, Theory–Practice Gap, Analytics Adoption In Industry, Managerial Decision Support, Evidence-Based Supply Chain Management, Digital Transformation In Operations.Abstract
Demand for supply chain optimization through industrial data analytics has surged, fueled by lessons learned during the COVID-19 crisis. A systematic review of methods applied in practice in 2021 summarizes how data science and operations research techniques are being adopted to tackle key issues. The review follows the ground-up flow of data in a typical supply chain. Publications describe predictive maintenance; end-to-end supply chain visibility; and inventory optimization. The data sources and processes for these analytics are also considered, echoing the importance of data quality and integration. Organizations are investing in technologies and competencies, yet progress remains slow. Acknowledgment of data quality dimensions is essential to achieve reliable models, yet limited. Moreover, a lack of interoperability hampers integration across factory-level operational systems and, consequently, impact on global visibility. These observations highlight the gap between the theory and practice of supply chain optimization.
The study makes a threefold contribution to operations research and data science. It analyzes how pioneering companies are leveraging data science for supply chain optimization, synthesizing methodological applications within the identified analytics. On the data side, it discusses the sources that feed manufacturing and logistics operations, emphasizing critical dimensions of data quality and the requirements for integration across distinct analytical domains. Finally, it translates this body of knowledge into practical insights for managers.
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