Activity based costing (ABC) has revolutionized product costing, planning, and forecasting in the last decade. It is based on a philosophy of estimation that: "it is better to be approximately right, than precisely wrong." The philosophy of discrete-event simulation modeling follows a similar tack, where statistical inference and the stochastic nature of processes are used to replicate the behavior of a physical system. In this work, ABC and discrete-event simulation are linked to provide an improved costing, planning, and forecasting tool. Numerous point cost estimates are generated by the ABC model, using driver values obtained from a discrete-event simulation of the process. The various cost estimates can be used to produce confidence interval estimates of both the physical system and underlying cost structure. Rather than having a single point estimate of a product's cost, it is now possible to produce the range of costs to be expected as process conditions vary. This improved cost estimate will support more informed operational and strategic decisions.
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