An integrated benchmarking approach to distribution center performance using DEA modeling

Ross, Anthony; Dröge, Cornelia

doi:10.1016/s0272-6963(01)00087-0

Cited by 122 publications

(73 citation statements)

References 23 publications

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“…For example, in Figure 4, the benchmarking candidate sets of L are R 1 = {B}, R 2 = {E, F}, R 3 = {H}, R 4 = {K, I}, and R 5 = {L}. K or I in R 4 can be regarded as the first IBT of L in R 5 , and H in R 3 can be regarded as the second IBT of both K and I. Here, the number of benchmarking steps for an evaluated DMU depends on not only the number of layers but also on whether the benchmarking candidate set exists in the l-th layer.…”

Section: Benchmarking Network Constructionmentioning

confidence: 99%

“…Identification of the best performer, which is the most important task in the benchmarking process, entails evaluation of the relative efficiencies of competitors according to multiple input and output factors. For this purpose, data envelopment analysis (DEA), a methodology for measurement of the relative efficiencies among homogeneous decision-making units (DMUs), has been utilized [5]. Specifically, DEA identifies an efficient frontier comprising Pareto-optimal DMUs along with their respective efficiency scores.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Park

Sung

2016

Sustainability

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Abstract:Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency DEA, K-means clustering and context-dependent DEA methods to minimize resource improvement pattern inconsistency in the selection of the intermediate benchmark targets (IBTs) of an inefficient DMU. The specific advantages and overall effectiveness of the proposed method were demonstrated by application to a case study of 34 actual container terminal ports and the successful determination of the stepwise benchmarking path of an inefficient DMU.

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Section: Benchmarking Network Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Park

Sung

2016

Sustainability

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“…This situation can be improved through the benchmarking approach, which basically involves the process of evaluating and applying best practices to improve the performance of less efficient organizations (Ross & Droge, 2002;Kyrö, 2003). DEA provides a method of identifying the DMUs that represent the "best practice", which are those on the efficient frontier (Post & Spronk, 1999;Hinton et al, 2000;Ross & Droge, 2002).…”

Section: Benchmarkingmentioning

confidence: 99%

Efficiency and productivity analysis of the interstate bus transportation industry in Brazil

2010

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Productivity analysis is an important policy making and managerial control tool for assessing the degree to which inputs are utilized in the process of obtaining desired outputs. Data Envelopment Analysis (DEA) is a non-parametric method based on piecewise linear frontiers estimated with the aid of mathematical programming techniques and used, in this paper, to investigate technical, scale and managerial efficiencies associated with interstate bus companies in Brazil (ISBT). Data has been obtained from the web-site of the Brazilian National Agency of Land Transportation (ANTT). Since production factors in the application are constrained by technical and operational reasons, weight restrictions were introduced into the DEA models. The analysis has shown three groups of non-efficient bus firms, with clear differences in productivity. The relative managerial efficiencies of the firms in the non-efficient groups were also computed and analyzed. Finally, an example of benchmarking a nonefficient firm with DEA is presented.Keywords: regional bus transportation; DEA; efficiency and productivity. ResumoA análise de produtividade é ferramenta importante para a tomada de decisão e para a gestão de organizações, possibilitando avaliar os efeitos dos inputs na obtenção de níveis desejados de outputs. A Análise Envoltória de Dados (DEA) é um método não paramétrico baseado em fronteiras lineares por partes ajustadas através de programação matemática e utilizada para analisar as eficiências técnica, de escala e de gestão de empresas de ônibus que operam nas ligações interestaduais no Brasil (ISBT). Os dados foram obtidos no web-site da Agência Nacional de Transportes Terrestres (ANTT). Como os fatores de produção são restritos por razões técnicas e operacionais, restrições de pesos foram introduzidas nos modelos. A análise mostrou três grupos de empresas não eficientes, com claras diferenças de produtividade. Também são calculadas as eficiências de gestão relativas a esses três grupos. Finalmente é apresentado um exemplo de benchmarking de uma empresa não eficiente através de DEA.Palavras-chave: transporte interestadual em ônibus; DEA; eficiência e produtividade.

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“…Ross and Droge (2002) measured distribution center productivity in a large scale setting, and identified distribution centers with consistent best performance using facet analysis, and detected performance trends using window analysis of 4 years data. Talluri and Baker (2002) presented a multi-phase mathematical programming approach for effective supply chain design.…”

Section: Literature Reviewmentioning

confidence: 99%

Suppliers selection in the presence of both cardinal and ordinal data

Saen

2007

European Journal of Operational Research

150

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Traditionally, supplier selection models are based on cardinal data with less emphasis on ordinal data. However, with the widespread use of manufacturing philosophies such as Just-In-Time (JIT), emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in supplier selection process. To select the best suppliers in the presence of both cardinal and ordinal data, this paper proposes an innovative method, which is based on Imprecise Data Envelopment Analysis (IDEA). A numerical example demonstrates the application of the proposed method.

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An integrated benchmarking approach to distribution center performance using DEA modeling

Cited by 122 publications

References 23 publications

Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Efficiency and productivity analysis of the interstate bus transportation industry in Brazil

Suppliers selection in the presence of both cardinal and ordinal data

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