A comparative study on the activity based costing systems: Traditional, fuzzy and Monte Carlo approaches

Any model׳s usefulness depends largely on the accuracy and reliability of its output. Yet, because all models are imprecise abstractions of reality and because precise input data are rarely if ever available, all output values are subject to uncertainty. This paper discusses how to handle such uncertainty as it relates to the feasibility and benefits of implementing an Activity Based Costing (ABC) system in an uncertain medical care environment. In our investigation, the relationship between sources of uncertainty and systems cost estimates is depicted as an input–output model. We introduce a conceptual framework based on Fuzzy Logic (FL) and Monte Carlo Simulations (MCS) and describe the fundamental elements needed to model an ABC system in an unpredictable, real-world environment. Also, for the purpose of illustrating the discussed concepts and techniques, a case study is presented based on the methodology discussed during the inception phase of a public academic medical center providing patient-centered care. In our case study, we calculated the unit cost of services by three different types of ABC systems: traditional (TABC), fuzzy (FABC), and Monte Carlo (MCABC). Finally, we analyze statistically the results obtained by each system. Based on the results, utilizing FABC and MCABC systems in a large hospital with considerable uncertain information can lead to the significantly different cost estimates from TABC. However, we did not find such a difference between FABC and MCABC.