Abstract
ABSTRACT Activity based costing (ABC) systems are implemented with the purpose of obtaining accurate product and process cost information. Due to extensive data requirements, ABC input data are often estimated which leads to inherent imprecision and uncertainty in these systems. The objective of this research is to develop and compare methods for handling this inherent data imprecision and uncertainty within ABC systems. Four methods of uncertainty analysis are explored; interval mathematics, Monte Carlo simulation with triangularly distributed input parameters, Monte Carlo simulation with normally distributed input parameters, and fuzzy set theory. Based on a comparative cost/benefit analysis, Monte Carlo simulation and fuzzy set theory are found to be superior to interval mathematics as methods for handling uncertainty in ABC systems.
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