Regression analysis for equipment auditing

Purpose - To develop, test and implement a sampling strategy for equipment auditing for aFortune100 company. Design/methodology/approach - Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations. Findings - Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre‐ and post‐audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided. Research limitations/implications - The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre‐ and post‐audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes. Practical implications - The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at aFortune100 company, and are generally applicable to equipment and inventory auditing when high correlation between pre‐ and post‐audit equipment is expected. Originality/value - This paper provides a practical and useful regression‐based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.