Probabilistic time‐driven activity‐based costing

Abstract

This article proposes a Probabilistic Time-driven Activity-based costing model. Time consumption is represented using probabilistic variables and Monte Carlo simulation technique is used to forecast total cost. The simulation experiments provide data to estimate a confidence interval for the total cost of a cost object. Hence, simulation can easily address variations in time forecasts and provide a more realistic characterization of total cost. The definition of the number of resources (e.g., employees) as decision variables allows analyzing alternatives and answer "what-if questions". The proposal would be very useful for an operation manager in analyzing uncertain scenarios and providing information indicating under what conditions different cost estimates are feasible.