Multi-Commodity Materials Planning Under Capacity Constraints Using an Integrated Demand-Supply Prioritization Model

Shared capacity can prevent a materials planner from releasing all requirements generated by material requirements planning. This paper develops an integrated demand-supply planning model that allocates constrained capacity across commodities using projected shortages, safety-stock gaps and criticality-weighted penalties. A linear program limits releases to net requirements and available capacity, while a priority score supports operational explanation. The optimal allocation depends on marginal penalty reduction per capacity unit; shortage recovery does not always precede every safety-stock replenishment when commodity weights differ. A reported numerical illustration compares sequential release, proportional rationing and integrated allocation under common demand and capacity scenarios. Its values illustrate the allocation trade-off and do not establish an independently replicated simulation or a measured firm-level improvement. The contribution is a decision rule that makes accepted shortages and inventory protection explicit while preserving the planning-system boundary. Reproducible validation requires the demand generator, random seeds, solver configuration and replication-level outputs. Field evaluation should also examine parameter sensitivity, unused capacity, service distribution and the operational cost of changing priorities.