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Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression

  • S.I. : CLAIO 2016
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Abstract

In the past two decades logistics services providers have increased their sector participation due to growing outsourcing of these services. In current scenario, logistics operators typically offer service packages that include not only transport itself but also other services in supply chain and transport’s service associated information. This work objective is to identify logistics services packages offered from logistics operators that lead to technical efficiency of operations observed in sector. For this analysis, the Data Envelopment Analysis (DEA) in two stages methodology was applied, where the first stage consists in the use of DEA models to obtain relative efficiency scores and the second stage consists in the use of one beta inflated regression to analyze the relationship between the technical efficiency scores obtained and the offered services. This study was made with secondary data base available on a logistics’ sector specialized magazine, for the period 2007–2015. Results show that a relationship between the offer of logistics service packages and logistics service providers’ technical efficiency exists. Different for each cluster, the statistically significant service packages vary as the magnitude of contribution on the efficiency measure. Most packages lead to negative contribution on the efficiency, while a few showed positive contributions.

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References

  • Adler, N., & Golany, B. (2007). PCA–DEA. In J. Zhu & W. D. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis. Boston, MA: Springer.

    Google Scholar 

  • Adler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research,202(1), 273–284.

    Article  Google Scholar 

  • Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega,49, 69–78.

    Article  Google Scholar 

  • Aktas, E., Agaran, B., Ulengin, F., & Onsel, S. (2011). The use of outsourcing logistics activities: The case of turkey. Transportation Research Part C: Emerging Technologies,19(5), 833–852.

    Article  Google Scholar 

  • Aktas, E., & Ulengin, F. (2005). Outsourcing logistics activities in Turkey. Journal of Enterprise Information Management,18(3), 316–329.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science,30(9), 1078–1092.

    Article  Google Scholar 

  • Banker, R., Natarajan, R., & Zhang, D. (2015). Estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: Two-stage versus bootstrap approaches. Working paper, Temple University.

  • Castro, O. D., Jr. (2015). Performance of logistics operators 2007–2015. Revista Tecnologística,252, 38–49.

    Google Scholar 

  • CEL. (2009). Center for studies in logistics. Logistics overview: Outsourcing logistics in Brazil. Rio de Janeiro: CEL/COPPEAD.

    Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research,2(6), 429–444.

    Article  Google Scholar 

  • Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York: Springer.

    Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis, a comprehensive text with models, applications, references and DEA-solver software (2nd ed.). New York: Springer.

    Google Scholar 

  • Dokas, I., Giokas, D., & Tsamis, A. (2014). Liquidity efficiency in the Greek listed firms: A financial ratio based on data envelopment analysis. International Journal of Corporate Finance and Accounting (IJCFA),1(1), 40–59.

    Article  Google Scholar 

  • Dyson, R., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research,132, 245–259.

    Article  Google Scholar 

  • Evangelista, P., & Sweeney, E. (2006). Technology usage in the supply chain: The case of small 3PLs. The International Journal of Logistics Management,17(1), 55–74.

    Article  Google Scholar 

  • Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics,31(7), 799–815.

    Article  Google Scholar 

  • Fries, C. E. (2013). Evaluation of the impact of the use of information and communication technologies on the efficiency of logistic service providers. 193 f. Ph.D. thesis. Industrial and Systems Engineering, Federal University of Santa Catarina, Florianópolis, Brazil.

  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2016). A grey DEMATEL approach to develop third-party logistics provider selection criteria. Industrial Management & Data Systems,116(4), 690–722.

    Article  Google Scholar 

  • Grönroos, C. (2000). Service management and marketing: A customer relationship management approach (p. 394). London, UK: Wiley.

    Google Scholar 

  • Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Serrano, A. L. M. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case. Journal of Cleaner Production,96, 209–219.

    Article  Google Scholar 

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). New Jersey: Prentice Hall.

    Google Scholar 

  • Hoff, A. (2007). Second stage DEA: Comparison of approaches for modeling the DEA score. European Journal of Operational Research,181(1), 425–435.

    Article  Google Scholar 

  • Hosie, P., Sundarakani, B., Tan, A. W. K., & Koźlak, A. (2012). Determinants of fifth party logistics (5PL): Service providers for supply chain management. International Journal of Logistics Systems and Management,13(3), 287–316.

    Article  Google Scholar 

  • Hou, C. E., Lu, W. M., & Hung, S. W. (2017). Does CSR matter? Influence of corporate social responsibility on corporate performance in the creative industry. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2626-9.

    Article  Google Scholar 

  • Ilos. (2014). Institute of logistics and supply chain (p. 2014). Rio de Janeiro: ILOS.

    Google Scholar 

  • Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions (2nd ed.). New York: Wiley.

    Google Scholar 

  • Lai, K. H. (2004). Service capability and performance of logistics service providers. Transportation Research Part E: Logistics and Transportation Review,40(5), 385–399.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega,41(1), 3–15.

    Article  Google Scholar 

  • McDonald, J. (2009). Using least squares and Tobit in second stage DEA efficiency analyses. European Journal of Operational Research,197(2), 792–798.

    Article  Google Scholar 

  • McDonald, J. F., & Moffitt, R. A. (1980). The uses of Tobit analysis. The review of economics and statistics. The Review of Economics and Statistics,62(2), 318–321.

    Article  Google Scholar 

  • Mello, J. E., Stank, T. P., & Esper, T. L. (2008). A model of logistics outsourcing strategy. Transportation Journal,47(4), 5–25.

    Google Scholar 

  • Mitra, S. (2006). A survey of third-party logistics (3PL) service providers in India. IIMB Management Review,18(2), 159–174.

    Google Scholar 

  • Norman, M., & Stoker, B. (1991). Data envelopment analysis: The assessment of performance. New York: Wiley.

    Google Scholar 

  • Ospina, R., & Ferrari, S. L. P. (2010). Inflated beta distributions. Statistical Papers,51, 111–126.

    Article  Google Scholar 

  • Ospina, R., & Ferrari, S. L. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis,56(6), 1609–1623.

    Article  Google Scholar 

  • Resende, P., Sousa, P. R., & Oliveira, P. (2016). Pesquisa de custos logísticos no Brasil. Brasil: Fundação Dom Cabral.

    Google Scholar 

  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics,20, 53–65.

    Article  Google Scholar 

  • Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2014). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications,41(1), 50–58.

    Article  Google Scholar 

  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of econometrics,136(1), 31–64.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). New York: Pearson Education.

    Google Scholar 

  • Wu, J., & Zhou, Z. (2015). A mixed-objective integer DEA model. Annals of Operations Research,228(1), 81–95.

    Article  Google Scholar 

  • Yang, Q., Zhao, X., Yeung, H. Y. J., & Liu, Y. (2016). Improving logistics outsourcing performance through transactional and relational mechanisms under transaction uncertainties: Evidence from China. International Journal of Production Economics,175, 12–23.

    Article  Google Scholar 

  • Zacharia, Z. G., Sanders, N. R., & Nix, N. W. (2011). The emerging role of the third-party logistics provider (LSP) as an Orchestrator. Journal of Business Logistics,32(1), 40–54.

    Article  Google Scholar 

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Correspondence to Diego Castro Fettermann.

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Wohlgemuth, M., Fries, C.E., Sant’Anna, Â.M.O. et al. Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression. Ann Oper Res 286, 703–717 (2020). https://doi.org/10.1007/s10479-018-3105-7

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  • DOI: https://doi.org/10.1007/s10479-018-3105-7

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