On the efficiency of cost-based decision rules for capacity planning

The quality of capacity planning significantly affects firm profit-ability, particularly for firms in service industries. In practice, firms use product cost data to infer the expected cost of under- and over-stocking capacity and to determine installed capacity. Theory shows that this is not optimal practice. In light of the informational and computational complexities associated with the optimal theoretical formulation, the use of product cost may be justified as a heuristic. For a multi-product, multi-resource firm, we use simulations to investigate the efficiency of four cost-based decision rules in determining the expected cost of under- and over-stocking capacity. Results indicate surprisingly high performance levels, relative to a benchmark solution. The performance of the product-based planning rule deteriorates as products increasingly share capacity resources. The opposite is true for resource-focused rules. There appears to be significant value from identifying mechanisms to balance installed capacity across resources.