Evaluating after-sales service units by developing inverse network data envelopment analysis model

Abstract

Purpose

The purpose of this paper is to develop an inverse network data envelopment analysis (INDEA) model to solve resource allocation problems.

Design/methodology/approach

The authors estimate inputs’ variations based on outputs so that the efficiencies of decision-making unit under evaluation (DMU_o) and other decision-making units (DMUs) are constant.

Findings

The new INDEA model is developed to allocate resources such that inputs are not increased while efficiency scores of all DMUs remain constant. Furthermore, the authors obtain new combinations of inputs and outputs, together with a growth in efficiency score of DMU_o such that efficiency scores of other DMUs are not changed. A case study is provided.

Originality/value

This paper proposes INDEA model to estimate inputs (outputs) without changing efficiency scores of DMUs.

Citation

Farzipoor Saen, R. and Seyedi Hosseini Nia, S.S. (2020), "Evaluating after-sales service units by developing inverse network data envelopment analysis model", Benchmarking: An International Journal, Vol. 27 No. 2, pp. 695-707. https://doi.org/10.1108/BIJ-01-2019-0017