Editing Benchmarking (section)

== Metric benchmarking ==
Another approach to making comparisons involves using more aggregative cost or production information to identify strong and weak performing units. The two most common forms of quantitative analysis used in metric benchmarking are [[data envelopment analysis]] (DEA) and [[regression analysis]]. DEA estimates the cost level an efficient firm should be able to achieve in a particular market. In infrastructure regulation, DEA сan be used to reward companies/operators whose costs are near the efficient frontier with additional profits. Regression analysis estimates what the average firm should be able to achieve. With regression analysis, firms that performed better than average can be rewarded while firms that performed worse than average can be penalized. Such benchmarking studies are used to create yardstick comparisons, allowing outsiders to evaluate the performance of operators in an industry. Advanced statistical techniques, including [[stochastic frontier analysis]], have been used to identify high and weak performers in industries, including applications to schools, hospitals, water utilities, and electric utilities.<ref name="bokir">[http://www.regulationbodyofknowledge.org/price-level-regulation/basic-forms-of-regulation/ Body of Knowledge on Infrastructure Regulation] "Incentive Regulation: Basic forms of Regulation"</ref>

One of the biggest challenges for metric benchmarking is the variety of metric definitions used among companies or divisions. Definitions may change over time within the same organization due to changes in leadership and priorities. The most useful comparisons can be made when metrics definitions are common between compared units and do not change so improvements can be changed.