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Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors

Year 2020, Volume: 5 Issue: 1, 55 - 81, 27.05.2020
https://doi.org/10.26650/JTL.2020.0007

Abstract

The purpose of this research is to assess the efficiency and productivity of 51 water bottling distributors based on DEA (Data Envelopment Analysis) approaches that were evaluated between 2012 and 2013, as per 5 variables, these being sales, staff, distance, size, and capex. We got different results from the initial assumptions after the analysis. Big cities distributors’ efficiencies are better than the small cities and town distributors. The time period was selected because of the global conflict, war in Syria, Turkish government policies, accepting the war refugees and the other reasons, natural and environmental. According to the research results, some suggestions have been made for the managers of the distributors.

References

  • Bigne, E., & Blesa, A. (2003). Market orientation, trust and satisfaction in dyadic relationships: a manufacturerretailer analysis. International Journal of Retail & Distribution Management, 31(11), 574-590.
  • Chen, F. Y., Wang, T., & Xu, T. Z. (2005). Integrated inventory replenishment and temporal shipment consolidation: A comparison of quantity-based and time-based models. Annals of Operations Research, 135(1), 197-210.
  • Cooper, W. W. (2007). Seiford. LM and Tone, K.(2000) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software.
  • Cooper, W. W. vd.(2000),“Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEASolver Software”.
  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1-39). Springer, Boston, MA.
  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1-39). Springer, Boston, MA.
  • De Souza, M. M. M. &. F., April-June 2010. ‘Performance of Brazilian supermarket companies: a Data Envelopment Analysis approach/Desempenho organizacional setor supermercadista. Faculdade de Economia, Administracao e Contabilidade, 17(2), pp. p151, 17.
  • Diaz-Balteiro, L., Herruzo, A. C., Martinez, M., & González-Pachón, J. (2006). An analysis of productive efficiency and innovation activity using DEA: An application to Spain’s wood-based industry. Forest Policy and Economics, 8(7), 762-773.
  • Emrouznejad, A., 2012. www.deazone.com. [Online] Available at: http://deazone.com/en/cooper-framework/ cooperframework-phase4 [Accessed 06 03 2018].
  • Fare, R., Grosskopf, S., & Lovell, C. (1993). Frontmatter. In Production Frontiers (pp. I-Viii). Cambridge: Cambridge University Press.
  • Farrell, M., 1957. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 3(1), pp. 253-290 .
  • Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: a non-parametric approach. Applied economics, 27(4), 385-395.
  • Ross, A., & Droge, C. (2002). An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management, 20(1), 19-32.
  • Dos Santos, R. F., Marins, F. A. S., Alves, J. M., & Moellmann, A. H. (2010). A real application of the theory of constraints to supply chain management in Brazil. Brazilian Journal of Operations & Production Management, 7(2), 81-100.
  • Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of econometrics, 46(1-2), 7-38.
  • Walden, J., & Kirkley, J. E. (2000). Measuring technical efficiency and capacity in fisheries by data envelopment analysis using the General Algebraic Modeling System (GAMS): A workbook. US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Region, Northeast Fisheries Science Center.
  • William W. Cooper, L. M. S. a. J. Z., 2005. History, Models and Interpretations. DATA ENVELOPMENT ANALYSIS, XIII(8), p. 1266.

Veri Zarflama Analizini Yöntemi Kullanarak Su Distribütörlerinin Etkinliğinin Ölçümlenmesi

Year 2020, Volume: 5 Issue: 1, 55 - 81, 27.05.2020
https://doi.org/10.26650/JTL.2020.0007

Abstract

Bu araştırmanın amacı, bir su fabrikasının 51 tane dağıtıcısının 2012 ve 2013 yılları arasındaki verilerini 5 farklı değişken (satışlar, personel sayısı, firma ile dağıtıcı arasındaki uzaklık, depo büyüklüğü ve diğer kuruluş maliyetleri) yardımıyla Veri Zarflama Analizi yöntemi (VZA) kullanarak dağıtıcıların etkinliğini ve verimliliği değerlendirmektir. Analizden sonra ilk varsayımlardan farklı sonuçlar aldık. Büyük şehirlerin distribütörlerinin verimliliği küçük şehirlerden ve kasaba distribütörlerinden daha iyi olduğu sonucuna varıldı. Küresel çatışma, Suriye’deki savaş, Türk hükümeti politikaları, savaş mültecilerini kabul etme ve diğer doğal ve çevresel nedenlerden dolayı 2012 ve 2013 yılları seçildi. Analiz sonuçlarına göre dağıtıcıların yöneticilerine etkinliklerini ve verimliliklerini artırıcı bazı önerilerde bulunuldu.

References

  • Bigne, E., & Blesa, A. (2003). Market orientation, trust and satisfaction in dyadic relationships: a manufacturerretailer analysis. International Journal of Retail & Distribution Management, 31(11), 574-590.
  • Chen, F. Y., Wang, T., & Xu, T. Z. (2005). Integrated inventory replenishment and temporal shipment consolidation: A comparison of quantity-based and time-based models. Annals of Operations Research, 135(1), 197-210.
  • Cooper, W. W. (2007). Seiford. LM and Tone, K.(2000) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software.
  • Cooper, W. W. vd.(2000),“Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEASolver Software”.
  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1-39). Springer, Boston, MA.
  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1-39). Springer, Boston, MA.
  • De Souza, M. M. M. &. F., April-June 2010. ‘Performance of Brazilian supermarket companies: a Data Envelopment Analysis approach/Desempenho organizacional setor supermercadista. Faculdade de Economia, Administracao e Contabilidade, 17(2), pp. p151, 17.
  • Diaz-Balteiro, L., Herruzo, A. C., Martinez, M., & González-Pachón, J. (2006). An analysis of productive efficiency and innovation activity using DEA: An application to Spain’s wood-based industry. Forest Policy and Economics, 8(7), 762-773.
  • Emrouznejad, A., 2012. www.deazone.com. [Online] Available at: http://deazone.com/en/cooper-framework/ cooperframework-phase4 [Accessed 06 03 2018].
  • Fare, R., Grosskopf, S., & Lovell, C. (1993). Frontmatter. In Production Frontiers (pp. I-Viii). Cambridge: Cambridge University Press.
  • Farrell, M., 1957. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 3(1), pp. 253-290 .
  • Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: a non-parametric approach. Applied economics, 27(4), 385-395.
  • Ross, A., & Droge, C. (2002). An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management, 20(1), 19-32.
  • Dos Santos, R. F., Marins, F. A. S., Alves, J. M., & Moellmann, A. H. (2010). A real application of the theory of constraints to supply chain management in Brazil. Brazilian Journal of Operations & Production Management, 7(2), 81-100.
  • Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of econometrics, 46(1-2), 7-38.
  • Walden, J., & Kirkley, J. E. (2000). Measuring technical efficiency and capacity in fisheries by data envelopment analysis using the General Algebraic Modeling System (GAMS): A workbook. US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Region, Northeast Fisheries Science Center.
  • William W. Cooper, L. M. S. a. J. Z., 2005. History, Models and Interpretations. DATA ENVELOPMENT ANALYSIS, XIII(8), p. 1266.
There are 17 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Uğur Arcagök This is me 0000-0002-4469-9525

Publication Date May 27, 2020
Submission Date February 22, 2020
Acceptance Date March 12, 2020
Published in Issue Year 2020 Volume: 5 Issue: 1

Cite

APA Arcagök, U. (2020). Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors. Journal of Transportation and Logistics, 5(1), 55-81. https://doi.org/10.26650/JTL.2020.0007
AMA Arcagök U. Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors. JTL. May 2020;5(1):55-81. doi:10.26650/JTL.2020.0007
Chicago Arcagök, Uğur. “Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors”. Journal of Transportation and Logistics 5, no. 1 (May 2020): 55-81. https://doi.org/10.26650/JTL.2020.0007.
EndNote Arcagök U (May 1, 2020) Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors. Journal of Transportation and Logistics 5 1 55–81.
IEEE U. Arcagök, “Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors”, JTL, vol. 5, no. 1, pp. 55–81, 2020, doi: 10.26650/JTL.2020.0007.
ISNAD Arcagök, Uğur. “Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors”. Journal of Transportation and Logistics 5/1 (May 2020), 55-81. https://doi.org/10.26650/JTL.2020.0007.
JAMA Arcagök U. Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors. JTL. 2020;5:55–81.
MLA Arcagök, Uğur. “Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors”. Journal of Transportation and Logistics, vol. 5, no. 1, 2020, pp. 55-81, doi:10.26650/JTL.2020.0007.
Vancouver Arcagök U. Using Data Envelopment Analysis for Measuring the Efficiency of Water Bottling Distributors. JTL. 2020;5(1):55-81.



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