Cost Efficiency Benchmarking for Operational Units with Multiple Cost Drivers

We consider the activity-based costing situation, in which for each of several comparable operational units, multiple cost drivers generate a single cost pool. 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 costs per unit of driver. A principle of maximum performance efficiency is proposed, and an approach to estimating the benchmark unit costs is derived from this principle. A validation approach for this estimation method is developed in terms of what we call normal-like-or-better performance effectiveness. Application to longitudinal data on a single unit is briefly discussed. We also consider some implications for the more routine case when costs are disaggregated to subpools associated with individual cost drivers.

[1]  W. Fleming Functions of Several Variables , 1965 .

[2]  Michael H. Kutner Applied Linear Statistical Models , 1974 .

[3]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[4]  H. Anton,et al.  Functions of several variables , 2021, Thermal Physics of the Atmosphere.

[5]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[6]  Alision Joyce Kirby Trade associations as information exchange mechanisms , 1985 .

[7]  Madhav V. Rajan,et al.  Cost Accounting: A Managerial Emphasis , 1972 .

[8]  Emmanuel Thanassoulis,et al.  Relative Efficiency Assessments Using Data Envelopment Analysis: An Application to Data on Rates Departments , 1987 .

[9]  J. Copas,et al.  Applied Linear Statistical Models, 2nd Edition. , 1987 .

[10]  R. Dyson,et al.  Reducing Weight Flexibility in Data Envelopment Analysis , 1988 .

[11]  A. Madansky Prescriptions for working statisticians , 1988 .

[12]  W. Greene A Gamma-distributed stochastic frontier model , 1990 .

[13]  Marvin D. Troutt,et al.  A theorem on the density of the density ordinate and an alternative interpretation of the bqx-muller method , 1991 .

[14]  Nicholas Dopuch,et al.  A perspective on cost drivers , 1993 .

[15]  Marvin D. Troutt Vertical density representation and a further remark on the box-muller method , 1993 .

[16]  R. Banker,et al.  An empirical study of cost drivers in the U.S. Airline Industry , 1993 .

[17]  M. Steel,et al.  Stochastic frontier models: a bayesian perspective , 1994 .

[18]  Srikant M. Datar,et al.  Aggregation, Specification and Measurement Errors in Product Costing , 1994 .

[19]  J. Sherer,et al.  How safe is your job? , 1994, Hospitals & health networks.

[20]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[21]  Oliver Kim,et al.  Partner selection and group formation in cooperative benchmarking , 1995 .

[22]  Marvin D. Troutt,et al.  A maximum decisional efficiency estimation principle , 1995 .

[23]  Samuel Kotz,et al.  On Vertical Density Representation and Ordering of Distributions , 1996 .

[24]  Götz Trenkler,et al.  Records Tests for Trend in Location , 1996 .

[25]  Samuel Kotz,et al.  On Multivariate Vertical Density Representation and its Application to Random Number Generation , 1997 .

[26]  Marvin D. Troutt,et al.  A Further VDR-type Density Representation Based on the Box-muller Method , 1997 .

[27]  Mahendra Gupta,et al.  Estimation of benchmark performance standards: An application to public school expenditures , 1997 .

[28]  Léopold Simar,et al.  Pitfalls of Normal-Gamma Stochastic Frontier Models , 1997 .

[29]  A. U.S.,et al.  FORMULATION AND ESTIMATION OF STOCHASTIC FRONTIER PRODUCTION FUNCTION MODELS , 2001 .

[30]  Carolyn Pillers Dobler,et al.  Mathematical Statistics , 2002 .