Maximum Performance Efficiency Approaches for Estimating Best Practice Costs

Abstract

Data mining is increasingly being used to gain competitive advantage. In this chapter, we propose a principle of maximum performance efficiency (MPE) as a contribution to the data-mining toolkit. This principle seeks to estimate optimal or boundary behavior, in contrast to techniques like regression analysis that predict average behavior. This MPE principle is explained in the context of activity-based costing situation. Specifically, we consider the activity-based costing situation in which multiple activities generate a common cost pool. Individual cost drivers are assigned to the respective activities, but allocation of the cost pool to the individual activities is regarded as impractical or expensive. Our study focuses on published data from a set of property tax collection offices, called Rates Departments, for the London metropolitan area. We define what may be called benchmark or most efficient average costs per unit of driver. The MPE principle is then used to estimate the best practice cost rates. A validation approach for this estimation method is developed in terms of what we call normal-like-or-better performance effectiveness. Extensions to time-series data on a single unit, and marginal cost-oriented basic cost models are also briefly described. In conclusion, we discuss potential data-mining applications and considerations.