Integrating Predictive Analytics into End-to-End Supply Chain Management: A Holistic Framework for Data-Driven Decision Making
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P106Keywords:
Predictive Analytics, Supply Chain Management (SCM), Forecasting, Inventory Optimization, Machine Learning, Data-Driven Decision Making, Disruption ManagementAbstract
Early in the last decade, the global supply chain has gone from a simple linear process to a multi-faceted and interconnected network influenced by dynamic market demands, geopolitical shifts, and more complex customer expectations. However, traditional supply chain management models have fallen short in today's logistics, procurement, inventory, and distribution complexities. Advanced data science techniques form the basis of Predictive Analytics (PA), and this has become an enormously transformative solution for organizations to use historical and real-time data and statistical models to see what’s coming next, reduce risks, and guide strategic operational decisions. Time series forecasting and anomaly detection, combined with machine learning algorithms, lead predictive analytics as a tool that enables supply chain systems to be more predictive, flexible, and responsive regarding uncertainties and decision-making at every node of the chain.
The thesis of this paper is that predictive analytics should be integrated into and through every stage of the end-to-end supply chain, including supplier selection and demand forecasting, transportation, inventory control, and last-mile delivery. Based on an extensive literature review and a real-life case study of a US retail chain, predictive modeling techniques are applied to critical supply chain functions. The results from the implementation also show a meaningful 22% drop in stockouts and a 17% increase in demand forecasting accuracy, which provide meaningful evidence that the proposed approach is effective. The framework incorporates predictive capability within supply chain workflows, which permits businesses to move from reactive to proactive strategies such that supply chain stakeholders can predict disruptions, optimize resource allocation, and boost supply chain resilience in an ever-worsening, volatile, and competitive global marketplace
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