This project develops an integrated production–supply chain simulation framework for evaluating operational and financial trade-offs in complex manufacturing systems. In many production environments, scheduling and supply-chain planning are analyzed through disconnected models, causing task priorities to be set without timely visibility into material availability, shortage propagation, expediting needs, or inventory-policy impacts. This separation can lead to infeasible schedules, production line blockages, delayed completions, and suboptimal financial performance. To address this challenge, we couple production scheduling, inventory replenishment, material availability, and shortage-expediting decisions through a custom service-bus run-time infrastructure (RTI) that enables real-time request-response and publish-subscribe communication between simulation federates.

The framework is evaluated on an anonymized 50-product manufacturing case study using combinations of due-date-based scheduling heuristics and inventory policies. Results show that integrated scheduling and inventory coordination substantially improve performance relative to the Baseline–Part-at-a-Time (PaaT) configuration. Demand Driven Material Requirements Planning (DDMRP) heuristic configurations eliminate lateness and part-shortage events, reduce traveled work by approximately 99%, and increase throughput by 184%, but require higher inventory investment. In contrast, System Availability (As) heuristic configurations achieve strong cash-flow gains and substantial lateness reductions while lowering average inventory value on hand, offering a more capital-efficient alternative. Statistical analysis confirms significant scenario effects across all reported metrics. These findings demonstrate that the proposed framework provides a practical decision-support capability for balancing production efficiency, service reliability, inventory investment, and financial performance in complex manufacturing systems.