Supplier delivery performance can deteriorate or recover over time, so plans built on static reliability assumptions often lead to material shortages, costly expediting, and persistent backlog. This paper studies an integrated multi supplier procurement and production inventory planning problem where supplier reliability is modeled as a time varying state updated from observed delivery outcomes, including on time delivery, lateness, and partial fulfillment. We propose a digital twin enabled closed loop framework in which the digital twin consolidates system state information, tracks outstanding pipeline orders, updates supplier reliability states from delivery observations, and provides material availability signals to procurement and production decisions on a rolling horizon. The planning model represents supplier activation and selection, order allocation under lead times, MOQ constraints, and supplier capacity limits, with optional expediting or supplier support actions. Production is constrained by shared capacity that may be reduced by machine breakdown events. Demand is stochastic and unmet demand is fully backlogged. Simulation experiments compare reliability aware policies against static reliability and myopic benchmarks under disruption scenarios. Results are evaluated using total cost and service indicators, demonstrating the benefit of delivery performance feedback for more robust coordination.

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Keywords

Digital twin, supplier reliability, multi sourcing, production inventory planning, simulation optimization

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