The use of multi‐attribute utility theory to determine the overall best‐in‐class performer in a benchmarking study

Purpose – To investigate the application of multi‐attribute utility theory (MAUT) to aid in the decision‐making process when performing benchmarking gap analysis. Design/methodology/approach – MAUT is selected to identify the overall best‐in‐class (BIC) performer for performance metrics involving inventory record accuracy within a public sector warehouse. A traditional benchmarking analysis is conducted on 14 industry warehouse participants to determine industry best practices for the four critical warehouse metrics of picking and inventory accuracy, storage speed, and order cycle time. Inventory and picking tolerances are also investigated in the study. A gap analysis is performed on the critical metrics and the absolute BIC is used to measure performance gaps for each metric. The gap analysis results are then compared to the MAUT utility values, and a sensitivity analysis is performed to compare the two methods. Findings – The results indicate that an approach based on MAUT is advantageous in its ability to consider all critical metrics in a benchmarking study. The MAUT approach allows the assignment of priorities and analyzes the subjectivity for these decisions, and provides a framework to identify one performer as best across all critical metrics. Research limitations/implications – This research study uses the additive utility theory (AUT) which is only one of multiple decision theory techniques. Practical implications – A new approach to determine the best performer in a benchmarking study. Originality/value – Traditional benchmarking studies use gap analysis to identify a BIC performer over a single critical metric. This research integrates a mathematically driven decision analysis technique to determine the overall best performer over multiple critical metrics.