Theory of binary-valued data envelopment analysis: an application in assessing the sustainability of suppliers

Abstract

PurposeThe objective of this study is to present a binary-valued data envelopment analysis (DEA) theory. The authors’ proposed approach, for the first time, combines binary-valued and integer-valued theories concurrently in the DEA context. To do so, new production possibility sets (PPSs) with some distinguished features are developed.Design/methodology/approachThe authors address integer inputs and outputs in the proposed approach by introducing a new PPS.FindingsTo take into account the binary data, the authors develop axiomatic DEA principles. The binary production principles guarantee any combination of convexity and feasibility. Furthermore, the authors develop a new DEA model to consider integer and real data. A case study is presented to show the usefulness of the developed models. Using the proposed models, the authors obtained benchmarks to solve the sustainable supplier selection problems.Originality/value(1) For the first time, binary-valued and integer-valued theories are presented in an integrated DEA model. (2) To deal with the pure binary data, a new PPS is proposed. (3) To consider real, integer and binary data, a new PPS is introduced. (4) New technologies are developed to propose feasible solutions. (5) The proposed models can project inefficient decision-making units (DMUs) on efficiency frontier given binary, integer and real data. (6) A case study is given for the performance evaluation of sustainable suppliers.

Impact of big data technological and personal capabilities on sustainable performance on Jordanian manufacturing companies: the mediating role of innovation

“…Big Data (BD) is the name given to this deluge of digital data, and studying BD can help a company gain a competitive advantage (Wamba et al, 2015;Shokouhyar et al, 2020). Big Data Analytics (BDA) refers to the methodology of using this huge data for decision-making via statistical techniques such as machine learning, factor analysis, regression, correlations and a variety of other statistical tests (Wamba et al, 2017;Karimi et al, 2022). Understanding of computer science, data science, statistics and mathematical models is thus applied in a wide range of interdisciplinary fields, including supply chain management (SCM) (Bhatti et al, 2022).…”

“…The decision-making aspect is strongly dependent on the judgment of an expert or a group of experts. If the organization is also able to collect and evaluate big data (that is, it has big data technological capabilities [BDTC]), without competent decision-makers, it can make the wrong decision especially in the context of sustainable SCM (Wamba et al, 2022;Karimi et al, 2022). BDAC must be seen as a concept that has two interrelated componentstechnological and personnel (Anwar et al, 2018).…”

“…Big Data Analytics (BDA) refers to the methodology of using this huge data for decision-making via statistical techniques such as machine learning, factor analysis, regression, correlations and a variety of other statistical tests (Wamba et al. , 2017; Karimi et al. , 2022).…”

“…If the organization is also able to collect and evaluate big data (that is, it has big data technological capabilities [BDTC]), without competent decision-makers, it can make the wrong decision especially in the context of sustainable SCM (Wamba et al. , 2022; Karimi et al. , 2022).…”