Rough data envelopment analysis and its application to supply chain performance evaluation

Xu, Jiuping; Li, Bin; Wu, Desheng Dash

doi:10.1016/j.ijpe.2009.06.026

Cited by 114 publications

(71 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A constant returns to scale model is used when there is no further information to make specific scale assumptions (Yoshida and Fujimoto, 2004). Some previous researches have also used DEA for measuring operational performance of vendors and supply chains (Xu et al, 2009;Talluri et al, 2006) …”

Section: 3choice Of Measuresmentioning

confidence: 99%

Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach

Ramanathan

Zhang

2016

International Journal of Production Economics

View full text Add to dashboard Cite

This study examines the impacts of marketing capability, operations capability, environmental capability and diversification strategy on performance of hotel industry in the UK.We conceptualize these impacts by drawing on the resource-based-view of a firm as the theoretical underpinning. We use the financial archival data and information obtained from websites. We use content analysis, regression analysis and Data Envelopment Analysis (DEA). Our results show that operations capability and environmental capability have significant positive effects on performance, marketing capability has a significant negative impact but diversification strategy does not impact on performance. Additionally, there is no evidence of the moderating effects of efficiency on these impacts. Our study suggests that hotel industry in the UK ought to focus on developing operations and environmental capabilities especially by exploiting the synergies between them but reduce excessive reliance on marketing. This paper makes two important contributions to the literature. First, it applies a framework linking the three capabilities (operations, marketing and environment) and diversification to the specific case of the hotel industry. Second, unlike similar previous studies, ours is the first to incorporate environmental capabilities in the analysis.

show abstract

Section: 3choice Of Measuresmentioning

confidence: 99%

Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach

Ramanathan

Zhang

2016

International Journal of Production Economics

View full text Add to dashboard Cite

show abstract

“…The remanufacturing decisionmaking problem for complex mechanical product can be decomposed into two sub-problems: evaluating the remanufacturing feasibility of the components and selecting the appropriate remanufacturing concept for each feasible component. [26]) and performance assessment in terms of efficiencies of DMUs ( [27]; [28]). Dotoli and Falagario [29] proposed an approach integrating DEA, TOPSIS and linear programming to select supplier which had three phases, and DEA was used to evaluate the efficiency of each supplier in the first phase in order to select several suppliers for TOPSIS evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

Component oriented remanufacturing decision-making for complex product using DEA and interval 2-tuple linguistic TOPSIS^*

Geng¹,

Gong²,

Chu³

2016

IJCIS

View full text Add to dashboard Cite

Remanufacturing has been attracting attentions of global manufacturers as an important sustainable development strategy. Component oriented remanufacturing decision-making for complex product involving two phases is proposed in this paper. The first phase is to decide which components to be remanufactured using DEA. To overcome the drawback of traditional DEA without weight constraints, an augmented DEA is applied to evaluate the efficiencies of the pre-selected components considering manufacturing characteristic, comparative cost advantage and general returned status. The second phase is to select an appropriate remanufacturing concept for each efficient component. Interval 2-tuple linguistic model is used in obtaining and collecting experts' evaluations. Besides subjective experts' weights, the objective weights of them are considered in the group decision-making process, which are determined by the precision degree of information experts have given. TOPSIS integrated with interval 2-tuple fuzzy linguistic representation model is proposed to rank alternative remanufacturing concepts. A new distance measuring method between two interval 2-tuple vectors is given out considering both the subjective and objective criteria weights. The objective criterion weight is determined by the discrete degree of the performances of all alternatives on this criterion. A case study is carried out to demonstrate the effectiveness of the developed remanufacturing decision-making approach for complex product.

show abstract

“…de Boer and Telgen 1998), manufacturing (e.g. Xu et al 2009), retailing (e.g. Vaz et al 2010), banking (e.g.…”

Section: Data Envelopment Analysis (Dea) and Its Applicationsmentioning

confidence: 99%

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

Gallear

Ghobadian

et al. 2013

Production Planning & Control

View full text Add to dashboard Cite

This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on the data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.

show abstract

Rough data envelopment analysis and its application to supply chain performance evaluation

Cited by 114 publications

References 24 publications

Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach

Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach

Component oriented remanufacturing decision-making for complex product using DEA and interval 2-tuple linguistic TOPSIS^*

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

Contact Info

Product

Resources

About

Rough data envelopment analysis and its application to supply chain performance evaluation

Cited by 114 publications

References 24 publications

Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach

Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach

Component oriented remanufacturing decision-making for complex product using DEA and interval 2-tuple linguistic TOPSIS*

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

Contact Info

Product

Resources

About

Component oriented remanufacturing decision-making for complex product using DEA and interval 2-tuple linguistic TOPSIS^*