Measurement of returns to scale in DEA using the CCR model

TL;DR

This paper refines the measurement of returns to scale in DEA using the CCR model by defining RTS at a specific projection point and proposing an efficient algorithm to improve accuracy and computational performance.

Contribution

It introduces a new definition of RTS at the interior of the minimum face and develops an efficient algorithm for its measurement in DEA.

Findings

Enhanced accuracy in RTS evaluation for inefficient DMUs.

Algorithm improves computational efficiency of RTS measurement.

Addresses issues with multiple projection points in DEA.

Abstract

In data envelopment analysis (DEA) literature, the returns to scale (RTS) of an inefficient decision making unit (DMU) is determined at its projected point on the efficient frontier. Under the occurrences of multiple projection points, however, this evaluation procedure is not precise and may lead to erroneous inferences as to the RTS possibilities of DMUs. To circumvent this, the current communication first defines the RTS of an inefficient DMU at its projected point that lies in the relative interior of the minimum face. Based on this definition, it proposes an algorithm by extending the latest developed method of measuring RTS via the CCR model. The main advantage of our proposed algorithm lies in its computational efficiency.