Predictive Supply Chain Integration: A Framework for Data-Driven Planning, Resilience, and Decision Support

Abstract

Predictive supply chain integration is an objective within the Logistics Transformation Program (LTP) Phase 2. Its strategic relevance lies in enabling enhanced decision support for planning, operations, and resilience through predictive analytics. Building upon the concept of predictive analytics, the predictive supply chain integration concept describes the phases, components, and requirements needed to deliver demand forecasting, inventory optimization, logistics network optimization, and risk monitoring capabilities. Specific use cases are identified, current capabilities assessed, and a framework developed to guide subsequent stages of implementation. Predictive analytics represents the next evolution of the more widely adopted prescriptive analytics, for which supply chain management has been a pioneering use case. Predictive capability encompasses data and model-driven decision making in the face of uncertainty, with objectives extending beyond cost and efficiency to also include service level attainment and supply chain risk as non-negotiable constraints. Three elements underpin predictive capability: a clear outline of how predictions will create value; a specification of who has decision rights over predictions, how these are devolved through the organization, and how responsibility and accountability for those decisions is measured; and the establishment of trigger points that capture the difference between predicted and actual ('prediction envy') and respond accordingly. The requirements for prediction-enabling data are also distinct from those needed to support the prescriptive analytics of operational decision making—prophecy is aided not only by volume but also by accuracy, completeness, and appropriate lead time, interval and scenario granularity. Consequently, while the underpinning data architecture falls within the domain of LTP Phase 2A is primarily concerned with developing predictive modelling capabilities.