The DEA Game Cross-efficiency Model for Supplier Selection Problem under Competition

Ma, Ruimin; Yao, Lifei; Jinand, Maozhu; Ren, Peiyu

doi:10.12785/amis/080242

Cited by 24 publications

(7 citation statements)

References 22 publications

(27 reference statements)

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“…The proposed second DEA model as reported in Ma et al, variable returns to scale assumption (Banker, Charnes, and Cooper [BCC]) also coined from the first names of the proposers, accounted for the component of the scale efficiency, thereby making it easier for processes examined in regions of increasing, constant, and decreasing returns to scale. CCR model can determine CCR efficiency in both primal and dual modes.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.

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Section: Introductionmentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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“…The game cross-model has recently been applied in performance evaluations such as those for suppliers (Ma et al 2014), Olympic ranking (Roboredo et al 2015), public infrastructure investment (Sun et al 2016) and portfolio selection (Essid et al 2018), as it is precise and discriminating in its efficiency estimation.…”

Section: Methodology and Research Designmentioning

confidence: 99%

Diversification and efficiency of life insurers in China and India

Peng

Lian

2020

Geneva Pap Risk Insur Issues Pract

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China and India are the world's most populous and rapidly developing countries and are often discussed together. We examine life insurance firms in these two countries between 2008 and 2016 using a game cross-efficiency model, which comparatively measures insurers' performance using Nash equilibrium weights. We investigate whether there is an optimal business model for three business dimensions: assets, funding and income. From our second-stage regressions we conclude that strategic focus is superior in terms of assets and income to diversification at the insurer level. At the capital market and economic levels, economic development, unemployment, stock market development and other variables are also important. Our study provides useful insights into how a business model can be made more efficient in large emerging markets.

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“…A factor that possibly reduces the usefulness of the cross‐efficiency evaluation is that cross‐efficiency scores may not be unique due to the presence of alternative optima, thus requiring the introduction of a secondary objective function (Zohrehbandian and Sadeghi Gavgani, ). Another way to avoid the possibility of not finding a Pareto solution is represented by the game cross‐efficiency proposed by Ma et al (). The classical DEA approach and its cross‐efficiency extension are extensively described in Section .…”

Section: Supplier Selection Modelsmentioning

confidence: 99%

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

Dotoli

Epicoco

Falagario

et al. 2015

Int Trans Operational Res

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This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross-efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross-efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria.

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The DEA Game Cross-efficiency Model for Supplier Selection Problem under Competition

Cited by 24 publications

References 22 publications

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Diversification and efficiency of life insurers in China and India

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

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