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Using a non-parametric technique to evaluate the efficiency of a logistics company

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Języki publikacji
EN
Abstrakty
EN
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.
Czasopismo
Rocznik
Strony
155--161
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
  • University of Pardubice, Faculty of Transport Engineering, Studentská 95, 532 10 Pardubice, Czech Republic
  • University of Pardubice, Faculty of Transport Engineering, Studentská 95, 532 10 Pardubice, Czech Republic
autor
  • University of Pardubice, Faculty of Transport Engineering, Studentská 95, 532 10 Pardubice, Czech Republic
  • University of Žilina, Faculty of Management Science and Informatics Univerzitná 8215/1, 010 26 Žilina, Slovakia
  • University of Žilina, Faculty of Management Science and Informatics Univerzitná 8215/1, 010 26 Žilina, Slovakia
Bibliografia
  • 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.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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