Affiliation:
1. Centro de Investigación Operativa Universidad Miguel Hernández Avd. de la Universidad, s/n Elche Alicante 03202 Spain
Abstract
AbstractPlanning is an important part of the management of organizations, which deals with the setting of targets that guide the actions needed to improve performance. Decision making is at the essence of planning, as it involves the identification of alternative directions for improvement and the selection of a future course of action. Decision makers (DMs) appreciate information on different ways of improving performance toward best practices, so they can make a choice of the plan that is more closely aligned with management in an ex post evaluation of possibilities. This paper responds to the need of providing DMs with a few (manageable) alternatives when planning improvements. The proposed approach is developed within the framework of data envelopment analysis (DEA). Although DEA has been used for planning, there is a gap in the literature in the sense that we can find only a few papers that have the explicit aim of identifying alternatives. In order to deal with this issue, we explore the whole DEA strong efficient frontier through all of its maximal efficient faces, which allows us to handle targets determined by a broad range of reference sets, and then use location theory tools, namely a p‐median problem, for the selection of p representatives that define alternative directions for improvement. Eventually, DMs are provided with a decision‐support tool for planning improvements by learning from the best practices of others, without requiring prior information on preferences.
Funder
Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana
Ministerio de Ciencia e Innovación