This paper aims to present a methodology for activity‐based costing, which combines simulation modeling and association rule mining, one of the core data‐mining techniques. The objective of the proposed methodology is to deal with the problem of defining cost drivers.
Activity‐based costing uses the output produced by the simulation of cost drivers as inputs. As opposed to the integration of the ABC technique with simulation modeling, the possibility of estimating an empirical distribution of the simulated cost drivers does not exist in the proposed methodology. This is achieved with the use of data‐mining techniques and is based on the proposition that, if an association is found between a cost driver, whose estimation or calculation is time‐consuming, and another cost driver, which can easily be estimated or calculated, then the latter can lead to the estimation or calculation of the former.
The extracted association rules correspond to existing dependencies between the cost drivers.
The paper presents a combined methodology to deal with the problem of defining cost drivers in activity‐based costing. An example of the proposed methodology in healthcare is also presented.
Kostakis, H., Sarigiannidis, C., Boutsinas, B., Varvakis, K. and Tampakas, V. (2008), "Integrating activity‐based costing with simulation and data mining", International Journal of Accounting & Information Management, Vol. 16 No. 1, pp. 25-35. https://doi.org/10.1108/18347640810887744Download as .RIS
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