Partial input to output impacts in DEA: Production considerations and resource sharing among business subunits

Abstract

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of peer decision-making units (DMUs), in a multiple input/output setting. Although it is generally assumed that all outputs are impacted by all inputs, there are many situations where this may not be the case. This article extends the conventional DEA methodology to allow for the measurement of technical efficiency in situations where only partial input-to-output impacts exist. The new methodology involves viewing the DMU as a business unit, consisting of a set of mutually exclusive subunits, each of which can be treated in the conventional DEA sense. A further consideration involves the imposition of constraints in the form of assurance regions (AR) on pairs of multipliers. These AR constraints often arise at the level of the subunit, and as a result, there can be multiple and often inconsistent AR constraints on any given variable pair. We present a methodology for resolving such inconsistencies. To demonstrate the overall methodology, we apply it to the problem of evaluating the efficiencies of a set of steel fabrication plants.

An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors

“…Based on the above-mentioned input and output indicators from the data collected from 25 PV-listed companies in the five years from 2011 to 2017, the research used DEAP v2.1 software and an input-oriented BCC model to measure the industrial efficiency of PV devices [57]. The overall technical efficiency, pure technical efficiency, and scale efficiency were computed and further analyzed by using the DEA method.…”

Data Envelopment Analysis Models in Non-Homogeneous Environment

“…The papers Cook et al (2012Cook et al ( , 2013 summarized the sources of nonhomogeneity and dealt with a related problem of missing or imprecise data in outputs in DEA. The study Imanirad et al (2013) extended the traditional DEA methodology to allow efficiency measurement in situations where only partial input-to-output impacts exist. The paper Li et al (2016) considered efficiency where DMUs are non-homogeneous on the input side and examines the case where different DMUs have different natural resource configurations.…”

Internal resource waste and centralization degree in two-stage systems: An efficiency analysis

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