Evaluating the cross-efficiency of information sharing in supply chains

Yu, Ming-Min; Ting, Shih‐Chan; Chen, Mu-Chen

doi:10.1016/j.eswa.2009.09.048

Cited by 128 publications

(65 citation statements)

References 30 publications

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“…The lead time as a piece of upstream information has rarely been discussed in the literature (Chen andYu, 2005, Jia et al, 2007), although lead time appears in many papers considering a system that is not subjected to explicit sharing (Agrawal et al, 2008, Cachon and Fisher, 2000, Li and Zhang, 2008, Moyaux et al, 2007, Zhao and Xie, 2002. Furthermore, some authors studied hybrid models in which information on the demand and inventory level is shared simultaneously (Chengalur-Smith et al, 2012, Li et al, 2006, Yu et al, 2010. Li et al (2006) presented five models of information sharing.…”

Section: Introduction and Literature Reviewmentioning

confidence: 95%

Assessing the value of information sharing and its impact on the performance of the various partners in supply chains

Rached

Bahroun

Campagne

2015

Computers & Industrial Engineering

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Section: Introduction and Literature Reviewmentioning

confidence: 95%

Assessing the value of information sharing and its impact on the performance of the various partners in supply chains

Rached

Bahroun

Campagne

2015

Computers & Industrial Engineering

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“…Yu et al (2010) used a cross efficiency DEA approach to analyze supply chain performance problem. Ramón et al (2010) used to the choice of weight profiles seeks to avoid large differences in the weights attached to the variables.…”

Section: Introductionmentioning

confidence: 99%

A modification of a mixed integer linear programming (MILP) model to avoid the computational complexity

2015

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Having multiple optimal solutions to weights affects to a great extent the consistency of operations related to weights. The cross efficiency method is the most frequently studied topic in data envelopment analysis (DEA) literature. Originally, the cross efficiency method included the efficiency evaluations that were obtained for a decision making unit (DMU) by the classical DEA for the reuse of optimal weights in other DMUs. As the optimal weights in classical DEA solutions usually have multiple solutions, this reduces the usefulness of the cross evaluation. Lam (J Oper Res Soc 61:134-143, 2010) proposed a mixed-integer linear programming (MILP) formulation based on linear discriminant analysis and super efficiency method to choose suitable weight sets to be used in cross efficiency evaluation. In this study, Lam's MILP model has been modified to reduce the steps during the solution process. The model also becomes a linear programming model after the modification to make it easier to use and to reduce the computational complexity. Numerical examples indicate that the proposed weight determination model both reduces the steps and minimizes computational complexity. Furthermore, it has similar performance with Lam's MILP model for the cross efficiency evaluation.

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“…To find the smallest uncertainty of a DMU with cross-efficiency intervals, we need to find the minimum value of entropy in (8). If every evaluated DMU has its own smallest entropy (uncertainty), then we can use this obtained entropy for comparison of DMUs.…”

Section: Entropy Of Cross-efficiency Intervalsmentioning

confidence: 99%

“…It provides an ordering among DMUs, and it eliminates unrealistic weight schemes without requiring the elicitation of weight restrictions from experts [4]. These merits let the method be widely used for ranking performance of DMUs, for example: advanced manufacturing technology selection [5], economic-environmental performance [6], measuring the performance of the nations participating in Olympic Games [7], supply chain management [8], public resource management [9], fixed cost and resource allocation [10], portfolio selection [11], premium allocation for academic faculty [12], and baseball player ranking [13].…”

Section: Introductionmentioning

confidence: 99%

Ranking DMUs by Comparing DEA Cross-Efficiency Intervals Using Entropy Measures

Liu

2016

Entropy

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Cross-efficiency evaluation, an extension of data envelopment analysis (DEA), can eliminate unrealistic weighing schemes and provide a ranking for decision making units (DMUs). In the literature, the determination of input and output weights uniquely receives more attentions. However, the problem of choosing the aggressive (minimal) or benevolent (maximal) formulation for decision-making might still remain. In this paper, we develop a procedure to perform cross-efficiency evaluation without the need to make any specific choice of DEA weights. The proposed procedure takes into account the aggressive and benevolent formulations at the same time, and the choice of DEA weights can then be avoided. Consequently, a number of cross-efficiency intervals is obtained for each DMU. The entropy, which is based on information theory, is an effective tool to measure the uncertainty. We then utilize the entropy to construct a numerical index for DMUs with cross-efficiency intervals. A mathematical program is proposed to find the optimal entropy values of DMUs for comparison. With the derived entropy value, we can rank DMUs accordingly. Two examples are illustrated to show the effectiveness of the idea proposed in this paper.

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Evaluating the cross-efficiency of information sharing in supply chains

Cited by 128 publications

References 30 publications

Assessing the value of information sharing and its impact on the performance of the various partners in supply chains

Assessing the value of information sharing and its impact on the performance of the various partners in supply chains

A modification of a mixed integer linear programming (MILP) model to avoid the computational complexity

Ranking DMUs by Comparing DEA Cross-Efficiency Intervals Using Entropy Measures

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