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A framework for measuring transport efficiency in distribution centers

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  • Andrejić, Milan
  • Bojović, Nebojša
  • Kilibarda, Milorad

Abstract

Performances of distribution systems are largely affected by the performances of transport systems. This paper is devoted to the analysis of the efficiency of transport subsystems in distribution centers. Transport is a logistics process with the highest energy consumption. In the transport systems two aspects of measuring efficiency are identified. The first aspect is the fleet efficiency which is related to the higher level of decision making. The second aspect of decision making is the vehicle efficiency as operational level of decision making. The main objective of this paper is to propose models for measuring transport efficiency, as well as to identify main factors that affect transport efficiency. The proposed models are based on Principal Component Analysis and Data Envelopment Analysis approaches. According to the results fleet management system, catchment area, vehicle capacity, the age of vehicles and manufacturers are the basic factors that affect transport efficiency.

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  • Andrejić, Milan & Bojović, Nebojša & Kilibarda, Milorad, 2016. "A framework for measuring transport efficiency in distribution centers," Transport Policy, Elsevier, vol. 45(C), pages 99-106.
    • Handle: RePEc:eee:trapol:v:45:y:2016:i:c:p:99-106
    DOI: 10.1016/j.tranpol.2015.09.013
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    1. Quariguasi Frota Neto, J. & Walther, G. & Bloemhof, J. & van Nunen, J.A.E.E. & Spengler, T., 2009. "A methodology for assessing eco-efficiency in logistics networks," European Journal of Operational Research, Elsevier, vol. 193(3), pages 670-682, March.
    2. Drake, L & Howcroft, B, 1994. "Relative efficiency in the branch network of a UK bank: An empirical study," Omega, Elsevier, vol. 22(1), pages 83-90, January.
    3. Adler, Nicole & Golany, Boaz, 2001. "Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe," European Journal of Operational Research, Elsevier, vol. 132(2), pages 260-273, July.
    4. Adler, Nicole & Berechman, Joseph, 2001. "Measuring airport quality from the airlines' viewpoint: an application of data envelopment analysis," Transport Policy, Elsevier, vol. 8(3), pages 171-181, July.
    5. Joseph Sarkis & Srinivas Talluri, 2004. "Ecoefficiency Measurement Using Data Envelopment Analysis: Research And Practitioner Issues," Journal of Environmental Assessment Policy and Management (JEAPM), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 91-123.
    6. Boussofiane, A. & Dyson, R. G. & Thanassoulis, E., 1991. "Applied data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 52(1), pages 1-15, May.
    7. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Kao, Ling-Jing & Lu, Chi-Jie & Chiu, Chih-Chou, 2011. "Efficiency measurement using independent component analysis and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 210(2), pages 310-317, April.
    10. N Adler & B Golany, 2002. "Including principal component weights to improve discrimination in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 985-991, September.
    11. Steven Hackman & Edward Frazelle & Paul Griffin & Susan Griffin & Dimitra Vlasta, 2001. "Benchmarking Warehousing and Distribution Operations: An Input-Output Approach," Journal of Productivity Analysis, Springer, vol. 16(1), pages 79-100, July.
    12. Demirbag, Mehmet & Tatoglu, Ekrem & Glaister, Keith W. & Zaim, Selim, 2010. "Measuring strategic decision making efficiency in different country contexts: A comparison of British and Turkish firms," Omega, Elsevier, vol. 38(1-2), pages 95-104, February.
    13. Timo Kuosmanen & Mika Kortelainen, 2005. "Measuring Eco‐efficiency of Production with Data Envelopment Analysis," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 59-72, October.
    14. Patrick L. Brockett & Boaz Golany, 1996. "Using Rank Statistics for Determining Programmatic Efficiency Differences in Data Envelopment Analysis," Management Science, INFORMS, vol. 42(3), pages 466-472, March.
    15. Sueyoshi, Toshiyuki & Aoki, Shingo, 2001. "A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test," Omega, Elsevier, vol. 29(1), pages 1-18, February.
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    3. Foster,Vivien & Rana,Anshul & Gorgulu,Nisan, 2022. "Understanding Public Spending Trends for Infrastructure in Developing Countries," Policy Research Working Paper Series 9903, The World Bank.
    4. Sarmento, Joaquim Miranda & Renneboog, Luc & Verga-Matos, Pedro, 2017. "Measuring highway efficiency : A DEA approach and the Malquist index," Other publications TiSEM 23264815-321e-45a3-83ee-9, Tilburg University, School of Economics and Management.
    5. El Bhilat, El Mehdi & El Jaouhari, Asmae & Hamidi, L. Saadia, 2024. "Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. Marwa Hasni & Safa Bhar Layeb & Najla Omrane Aissaoui & Aymen Mannai, 2022. "Hybrid model for a cross‐department efficiency evaluation in healthcare systems," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1311-1329, July.
    7. Farah Naz & Anna Fredriksson & Linea Kjellsdotter Ivert, 2022. "The Potential of Improving Construction Transport Time Efficiency—A Freight Forwarder Perspective," Sustainability, MDPI, vol. 14(17), pages 1-19, August.

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