Modelling generalized firms’ restructuring using inverse DEA

Abstract

The key consideration for firms' restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper.

A two‐stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector

“…The returns to scale of the inverse DEA models in this paper is from the base DEA models. Following the inverse DEA models for estimating merger gains in the literature (Amin & Al‐Muharrami, 2018; Amin, Al‐Muharrami, & Toloo, 2019; Amin, Emrouznejad, & Gattoufi, 2017a, 2017b; Gattoufi, Amin, & Emrouznejad, 2014), the above inverse DEA models developed in this paper assumed variable returns to scale. The type of returns to scale can be changed to other appropriate forms of returns to scale depending on the application.…”

“…They also formulated inverse DEA models that minimize the quantity of inputs that is required to produce the output levels of merging units for a given efficiency target. Amin, Emrouznejad, and Gattoufi (2017a) extended the concept of M&A and firms' restructuring and introduced the concept of generalized restructuring using inverse DEA. Amin, Emrouznejad, and Gattoufi (2017b) identified minor and major consolidations using inverse DEA models.…”

Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs

“…Gattoufi et al [18] applied inverse DEA in studying the merger of banks. Amin et al [40] developed the inverse GInvDEA model to illustrate a generalized restructuring. To the best of our knowledge, only three papers [17,21,41] have examined inverse DEA with undesired outputs.…”

“…Their approach is based on the inverse DEA model of Gattoufi et al [15] and the VRS SORM model proposed by Matin et al [43]. Amin et al [40] proposed the generalized inverse DEA, which is a generalized version of the model proposed by Gattoufi et al [15]. Modhej et al [44] combined inverse DEA with artificial neural networks to study the performance of 600 Iranian bank branches.…”

A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries

“…More recently, Amin and Al-Muharrami (2016) addressed new inverse DEA models for mergers with negative data. Moreover, the potential of the inverse DEA has been used in Amin et al (2017b) to anticipate whether a given restructuring between a group of DMUs makes a minor or a major consolidation. The successful result of the inverse DEA in M&A shows the potential power of this methodology in other sectors.…”