Capacity planning with limited information

Abstract

Limited information about the demand for some of the resources needed to produce goods and services (e.g., incomplete and imperfect bills of materials) forces firms to use heuristics when planning resource capacity. We examine the performance of five heuristics: two drawn from practice, two that modify observed approaches, and one motivated by theory. We measure performance as the ratio of the expected cost of supply–demand mismatch from using a heuristic to the value in the full‐information solution. Numerical analysis shows that a simple heuristic that is common in practice—plan rigorously for a few “driver” resources with high‐quality information and use ratios (e.g., 0.25 indirect labor hours per machine hour) to project the capacities for the remaining “non‐driver” resources—is robust and efficient. Using more than one driver resource to plan for the same non‐driver resource delivers significant gains. Reducing measurement error with respect to the consumption of driver resources dominates the gain from reducing errors in other aspects. Indeed, with high measurement error, collecting information that reduces other sources of error could decrease overall performance. Finally, a greedy algorithm of choosing the most expensive resources as drivers is optimal.

Cost hierarchies and the pattern of product cost cross-subsidization: Extending a computational model of costing system design

“…Therefore, high COR1 and COR2 values induce similarity between products through highly correlated resource consumption. Low values increase the disparity, e.g., meaning that products become dissimilar [40]. If all information is available about production volumes (MXQ), resource consumption (RES_CONS_PAT), and resource costs (RCC), the benchmark costs of a cost object (PCB) can be calculated by multiplying a relative resource consumption (RES_CONS_PATp) for every resource by every product with the resource costs from RCC.…”