Evaluating and selecting key performance indicators: an ANP-based model

Purpose – Selecting the most meaningful performance indicators, i.e. key performance indicators (KPIs), represents one of the major challenges that companies have to face for developing an effective performance measurement system (PMS). Selecting KPIs can be interpreted as a multiple criteria decision-making (MCDM) problem, involving a number of factors and related interdependencies. The purpose of this paper is to propose a model, based on the analytic network process (ANP), for driving managers in the selection of KIPs. The model draws upon the consideration that KPIs can be evaluated and selected on the basis of a set of criteria, theoretically founded, and the feedback dependencies between the criteria and performance indicators as well as among indicators. Design/methodology/approach – Based on a review of management literature regarding the information quality required by performance measures, the paper identifies a set of criteria for selecting KIPs. The criteria form the building blocks of the proposed ANP model. The feasibility of the model is proved through its application to a real case. Findings – The paper proposes and illustrates the practical application of an ANP-based model for selecting KPIs. The use of the ANP makes it possible to extracts weights for setting the priorities among indicators, by taking account of mutual dependencies among indicators and criteria. This enhances the quality of the selection process. Originality/value – Often managers choose KPIs without an accurate approach. The paper offers a novel model for driving managers towards the choice of KPIs through a rigorous approach, based on the ANP method. The model draws on a solid theoretical foundation and has been proven in practice.