USING A NON-PARAMETRIC TECHNIQUE TO EVALUATE THE EFFICIENCY OF A LOGISTICS COMPANY

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Transport Problems

Silesian University of Technology

Subject: Economics, Transportation, Transportation Science & Technology

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VOLUME 15 , ISSUE 1 (March 2020) > List of articles

USING A NON-PARAMETRIC TECHNIQUE TO EVALUATE THE EFFICIENCY OF A LOGISTICS COMPANY

Petr PRŮŠA * / Stefan JOVČIĆ / Josef SAMSON / Zuzana KOZUBÍKOVÁ / Aleš KOZUBÍK

Keywords : warehouse; logistics; efficiency; Data Envelopment Analysis; decision-making units

Citation Information : Transport Problems. Volume 15, Issue 1, Pages 153-161, DOI: https://doi.org/10.21307/tp-2020-014

License : (CC BY 4.0)

Received Date : 15-October-2018 / Accepted: 12-March-2020 / Published Online: 26-March-2020

ARTICLE

ABSTRACT

Data Envelopment Analysis (DEA) is a relatively new method, a nonparametric technique used nowadays to evaluate the efficiency of the Decision-Making Units. Using this method, the Decision-Making Units can be compared between each other and the most effective ones can be found. Using the DEA method, the performance of a logistic company with twelve warehouses as DMUs is evaluated in this paper. "DEA Excel Solver" user program was used to solve the problem.

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REFERENCES

1. Mangan, J. & Lalwani, C. & Butcher, T. Global logistics and supply chain management. Wiley. 2008. 392 p.

2. Burdzik, R. & Ciesla, M. & Sładkowski, A. Cargo loading and unloading efficiency analysis in multimodal transport. Promet – Traffic – Traffico. 2014. Vol. 26(4). P. 323-331.

3. Fedorko, G. & Molnár, V. & Kuptcova, A. & Průša, P. Data mining workspace as an optimization prediction technique for solving transport problems. Transport Problems. 2016. Vol. 11. No. 3. DOI: 10.20858/tp.2016.11.3.3.

4. Dobrodolac, M. & Lazarević, D. & Švadlenka, L. & Blagojević, M. The impact of entropy on the efficiency of express courier systems. Journal of Applied Engineering Science. 2015. Vol. 13(3). P. 147-154.

5. Ravelić, P. & Dobrodolac, M. & Marković, D. Using a nonparametric technique to measure the cost efficiency of postal delivery branches. Central European Journal of Operations Research. 2016. Vol. 24(3), P_. 637-657. DOI: 10.1007/s10100-014-0369-0.

6. Drenovac, D. & Pjevčević, K. & Vukadinović, K. Primena Fazi analize obavijanja podataka za merenje efikasnosti obrade rasutog terete. Symopis. 2008. P. 375-378. [In Croatian: Application of Phase analysis of data wrapping to measure the efficiency of bulk cargo processing]

7. Cullinane, K. & Wang, T. The efficiency of European container ports: a cross-sectional data envelopment analysis. International Journal of Logistics: Research and Applications. 2006. Vol. 9. No. 1. P. 19-31.

8. Tongzon, J. Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transportation Research Part A. 2001. Vol. 35. P. 107-122.

9. Charnes, A. & Cooper, W. & Rhodes, E. Measuring the efficiency of decision-making units. European Journal of Operational Research. 1978. Vol. 2. P. 429-444.

10. Cooper, W. & Seiford, L. & Tone, K. Introduction to data envelopment analysis and its uses. Springer Science+Business Media Inc. 2006.

11. Ray, S. Data Envelopment Analysis, Cambridge: Cambridge University Press. 2004. DOI: http://dx.doi.org/10.1017/CBO9780511606731.

12. Arkay, E. & Ertek, G. & Buyukozkan, G. Analysing the solutions of DEA through information visualization and data mining techniques: Smart DEA framework. Expert Systems with Applications. 2012. Vol. 39. P. 7763-7775. DOI: http://dx.doi.org/10.1016/j.eswa.2012.01.059.

13. Farantos, I.G. The data envelopment analysis method and the influence of a phenomenon in organizational efficiency: A literature review and the data envelopment contrast analysis new application. 2015. No. 2. 17 p.

14. Zhang Q., Zhang B. Comprehensive evaluation of logistics enterprise performance based on DEA and inverted DEA model. American Journal of Applied Mathematics. 2018. Vol. 6. No. 2. P. 48-54. DOI: 10.11648/j.ajam.20180602.14.

15. Yamada, Y. & Matsui, T. & Sugiyama, M. An inefficiency measurement method for management systems. Journal of the Operations Research Society of Japan. 1994. Vol. 37(2). P. 158-168.

16. Dobrodolac, M. & Švadlenka, L. & Čubranić-Dobrodolac, M. & Čičević, S. & Stanivuković, B. A model for the comparison of business units. Personnel Review. 2018. Vol. 47(1). P. 150-165. DOI: https://doi.org/10.1108/PR-02-2016-0022.

17. Ralević, P. & Dobrodolac, M. & Marković, D. & Mladenović, S. The Measurement of Public Postal Operators’ Profit Efficiency by Using Data Envelopment Analysis (DEA): a Case Study of the European Union Member States and Serbia. Engineering Economics. 2015. Vol. 26(2). P. 159-168. DOI: https://doi.org/10.5755/j01.ee.26.2.3360.

18. Fedorko, G. & Molnár, V. & Honus, S. & Neradilová, H. & Kampf, R. The application of simulation model of a milk run to identify the occurrence of failures. International Journal of Simulation Modelling. 2018. Vol. 17(3). P. 444-457. DOI: https://doi.org/10.2507/IJSIMM17 (3)440.

19. Sabadka, D. & Molnár, V. & Fedorko, G. Shortening of Life Cycle and Complexity Impact on the Automotive Industry. TEM Journal. 2019. Vol. 8(4), P. 1295-1301. Available at: https://doi.org/10.18421/TEM84-27.

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