Time Period Based COPRAS-G Method: Application on the Logistics Performance Index

M27

Doç. Dr. Burcu Adıgüzel Mercangöz
İstanbul Üniversitesi Ulaştırma ve Lojistik Fakültesi, Lojistik Anabilim Dalı

Dr. Bahadır Fatih Yıldırım
İstanbul Üniversitesi Ulaştırma ve Lojistik Fakültesi, Lojistik Anabilim Dalı

Dr. Öğr. Üyesi Sultan Kuzu Yıldırım
İstanbul Üniversitesi İşletme Fakültesi, Sayısal Yöntemler Anabilim Dalı

Abstract

Background: Logistics is vital for the trades of countries. The inputs such as raw materials and energy that is needed for production and also the outputs of these processes are transported and distributed effectively as a result of an efficient logistics process. In order to measure the logistics performance of countries, The World Bank (WB) is publishing an index entitled Logistics Performance for every two years. Methods: The main value of this study is to provide logistics performance scores of the selected countries for a selected time period. Thus, periodic evaluations can be done for a selected time period. The grey numbers are used for determining a new dataset for a time period and implement to Complex Proportional Assessment of Alternatives (COPRAS) method. 28 European Union (EU) member states plus 5 EU Candidate Countries are ranked by using the COPRAS-Grey (COPRAS-G) method according to their logistics performance scores. In order to see if the ranking calculated by COPRAS-G is representing the past index data, the bilateral comparisons of the rankings are investigated by using the Spearman Rank and Kendall’s Tau Correlation methods. Results: The results showed that the dataset obtained by using grey numbers represent the LPI scores of the countries for the selected time period. Although there are slight differences between the Spearman and Kendall correlation coefficients, the ultimate result is the same. The ranking calculated by COPRAS-G has the strongest relationship with all rankings published by WB. Conclusions: By using the grey numbers combined with the COPRAS-G method, the LPI of Countries can be evaluated for a time period.

Keywords: Correlation Analysis, Grey COPRAS, Grey Numbers, Logistics Performance Index

Jel Sınıflandırma:

  • Adhikary, D.D., Bose, G.K., Dipankar, B., Mitra, S., (2014). Multi criteria FMECA for coal-fired thermal power plants using COPRAS-G. International Journal of Quality & Reliability Management; 31(5), 601-614.
  • Aghdaie, M.H., Zolfani, S.H., Zavadskas, E.K., (2013). Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Economics of Engineering Decisions, 24(1), 5-17.
  • Arslan, R., Bircan, H., Eleroglu, H., (2018). Optimally rating of biogas, compost, vermicompost facilities to be installed in Yozgat Province with ARAS and COPRAS methods. Turkish Journal of Agriculture - Food Science and Technology 6 (12),1844-1852.
  • Bakar, M.A.A, Jaafar, H.S., (2016). Malaysian logistics performance: A manufacturer's perspective. In Peoceedings of the 6th International Research Symposium in Service Management. Procedia . 571-578
  • Bakhouyi, A., Dehbi, R., Talea, M. (2016). Multiple criteria comparative evaluation on the interoperability of LMS by applying COPRAS method, 2016 Future Technologies Conference (FTC), 361-366.
  • Bayrakci, E., Aksoy, E., (2019). Comparative Performance Assessment with Entropy Weighted ARAS and COPRAS Methods of Private Pension Companies, Business and Economics Research Journal, 10(2), 415-433.
  • Bentyn, Z., (2015). Changes of Logistics Performance in Poland as a Result of Integration with the European Union States. We Wrocławiu Research Papers of Wrocław University of Economics Nr 407.
  • Bitarafan, M., Zolfani, S.H., Arefi, S.L., Zavadskas, E.K., (2012). Evaluating the construction methods of cold-formed steel structures in reconstructing the areas damaged in natural crises, using the methods AHP and COPRAS-G, Archives of Civil and Mechanical Engineering Volume 12, Issue 3, September 2012, Pages 360-367.
  • Cakir, E., Ozdemir, M., (2018). Alti Sigma Projelerinin Bulanik Copras Yöntemiyle Degerlendirilmesi: Bir Üretim Isletmesi Ornegi (Six Sigma Projects Evaluation Using Fuzzy Copras Method: A Case Of Manufacturing Company), Verimlilik Dergisi 2018, 1 (1) 7-40
  • Candemir, Y., Celebi, D., (2017). An Inquiry into the Analysis of the Transport & Logistics Sectors’ Role in Economic Development. Transportation Research Procedia, Volume 25, p.4692-4707.
  • Celebi, D., (2017). The Role of Logistics Performance in Promoting Trade. Maritime Economics & Logistics, Volume 21, Issue 3, p.307-323.
  • Çemberci, M., Civelek, M.E., & Canbolat, N. (2015). The Moderator Effect of Global Competitiveness Index on Dimensions of Logistics Performance Index. Procedia-Social and Behavioral Sciences, 95(2015) 514-1524.
  • Chatterjee, P., & Chakraborty, S. (2013). Gear material selection using complex proportional assessment and additive ratio assessment-based approaches: a comparative study. International Journal of Materials Science and Engineering, 1(2), 104-111.
  • Chatterjee , K., Kar, A.S., (2018). Supplier Selection in Telecom Supply Chain Management: A Fuzzy-RASCH Based COPRAS-G Method, Technological and Economic Development of Economy Volume 24 Issue 2: 765–791
  • Civelek, M. E., Uca, N.& Cemberci, M., (2015). The Mediator Effect of Logistics Performance Index on the Relation between Global Competitiveness Index and Gross Domestic Product. European Scientific Journal 11 (3): 368–375.
  • Dekker, M.J., Looff, E.J., Roelofsen, D.S., & Roekel, W.S., (2016). Improving the Logistical Situation of Costa Rica. Delf Uiversity of Technology Report
  • Erkan, B. (2014). The Importance and Determinants of Logistics Performance of Selected Countries. Journal of Emerging Issues in Economics, Finance and Banking, 3(6), 1237-1254.
  • Faria, R. N. D., Souza, C. S. D. & Vieira, J. G. V., (2015). Evaluation of Logistic Performance Indexes of Brazil in the International Trade. Revista de Administração Mackenzie 16 (1) 213–235.
  • Gani, A. (2017). The Logistics Performance Effect in International Trade. The Asian Journal of Shipping and Logistics Vol 33 Issue 4 279-288.
  • Garg , R., Kumar, R., & Garg, S., (2019). MADM-Based Parametric Selection and Ranking of E-Learning Websites Using Fuzzy COPRAS, IEEE Transactions on Education, Vol. 62, No. 1, February 2019
  • Hoekman, B., & Nicita, A., (2011). Trade Policy, Trade Costs, and Developing Country Trade. World Development 39(12), 2069–2079.
  • Iris C., &Tanyas, M., (2011). Analysis of Turkish Logistics Sector and Solutions Selection to Emerging Problems Regarding Criteria Listed in Logistics Performance Index (LPI). International Journal of Business and Management Studies, Volume: 3 Issue:1 93-102.
  • Julong, D., (1982). Control problems of Grey Systems, System and Control Letters, 5,288-94.
  • Jumadi, H., & Zailani, S., (2010). Integrating Green Innovations in Logistics Services Towards Logistics Services Sustainability: A Conceptual Paper. Environmental Research Journal 4(4), 261-271.
  • Khan, S., & Qianli, D., (2017). Does National Scale Economic and Environmental Indicators Spur Logistics Performance? Evidence from UK. Environmental Science and Pollution Research 24 (34) 26692-26705.
  • Liou, J.J.H., Tamošaitienė , J., Zavadskas, E.K., & Tzeng, G.H., (2016). New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. International Journal of Production Research, 2016 Vol. 54, No. 1, 114–134
  • Liu S., Lin Y. (2010) Introduction to Grey Systems Theory. In: Grey Systems. Understanding Complex Systems, vol 68. Springer, Berlin, Heidelberg
  • Martí, L., Puertas, R. & García, L., (2014). The Importance of the Logistics Performance Index in International Trade. Applied Economics 46 (24): 2982–2992.
  • Martí, L., Martín, J.C. & Puertas, R., (2017). A DEA-Logistics Performance Index. Journal of Applied Economics Vol. 20 Issue 1 169-192.
  • Nguyen, H.T., Dawal, S.Z.M., Nukman, Y., Aoyama, H., (2014). A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes. Expert Systems with Applications, Volume 41, Issue 6, May 2014, Pages 3078-3090.
  • Nur Fadiah, M.Z., Wahab, S.A., & Mamun, A.A., (2017). Logistics Capability, Logistics Performance, and The Moderating Effect of Firm Size: Empirical Evidence From East Coast Malaysia. Journal of Developing Areas Vol. 51 Issue 2 171-182.
  • Onsel Ekici, S., Kabak, O., & Ulengin, F., (2016). Linking to Compete: Logistics and Global Competitiveness Interaction. Transport Policy 48:117-128.
  • Pancholi, N., Bhatt, M., (2018). FMECA-Based Maintenance Planning through COPRAS-G and PSI, Journal of Quality in Maintenance Engineering, 2018, Vol. 24, Issue 2, pp. 224-243.
  • Puertas, R., Martí, L. & García, L., (2014). Logistics Performance and Export Competitiveness: European Experience. Empirica, 41 (3) 467–480.
  • Mousavi-Nasab, S.H. and Sotoudeh Anvari, A., (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design. May2017, Vol. 121, p237-253. 17p.
  • Solakivi, T., Ojala, L., Laari, S., Lorentz, H., Toyli, J., Malmsten, J., & Viherlehto, N., (2015). Finland State of Logistics 2014. Turun Kauppakorkeakoulun Julkaisuja Sarja E-1:2015.
  • Tavana, M., Momeni, E., Rezaeiniya, N., Mirhedayatian, S.M., Rezaeiniya, H., (2013). A Novel Hybrid Social Media Platform Selection Model using fuzzy ANP and COPRAS-G. Expert System with Applications 40(14): 5694-5702.
  • Uca, N., Ince, H., Sumen, H., (2016). The Mediator Effect of Logistics Performance Index on the Relation Between Corruption Perception Index and Foreign Trade Volume. European Scientific Journal 12(25) 37-45.
  • Vaillancourt, A., & Haavisto, I., (2015). Country Logistics Performance and Disaster Impact. Disasters 40(2), 262-283. https://doi.org/10.1111/disa.12146
  • Yapraklı, T.S. & Unalan, M., (2017). The Global Logistics Performance Index and Analysis of the Last Ten Years Logistics Performance of Turkey. Ataturk University Journal of Economics & Administrative Sciences Vol. 31 Issue 3 589-606.
  • Yildirim, B.F., Adiguzel Mercangoz, B. (2019). Evaluating the Logistics Performance of OECD Countries by Using Fuzzy AHP and ARAS-G. Eurasian Economic Review, 1-19. https://doi.org/10.1007/s40822-019-00131-3
  • Zavadskas, E. K.; Kaklauskas, A.; Sarka, V. (1994). The new method of multicriteria complex proportional assessment of projects, Technological and Economic Development of Economy, 1(3): 131–139.
  • Zavadskas, E.K., & Kaklauskas, A. (1996). Determination of an Efficient Contractor by using the new Method of Multi Criteria Assessment. In Langford, D. A.; Retik, A. (eds.) International Symposium for “The Organization and Management of Construction”. Shaping Theory and Practice. Vol. 2: Managing the Construction Project and Managing Risk. CIB W 65; London, Weinheim, New York, Tokyo, Melbourne, Madras. - London: E and FN SPON, 94-104.
  • Zavadskas, E.K., Kaklauskas, A., Turskis, Z., & Tamosaitiene, J. (2008). Selection of the Effective Dwelling House Walls by Applying Attributes Values Determined at Intervals. Journal of Civil Engineering and Management, 14(2), 85-93. http://dx.doi.org/10.3846/1392-3730.2008.14.3
  • Zavadskas , E.K., Turskis, Z., Tamošaitienė , J., Marina, V., (2008). Multicriteria Selection of Project Managers by Applying Grey, Technological and economic development 14(4): 462–477.
  • Zhang, Y, Tan, Y., Li, N., Liu, G., (2018). Decision-Making in Green Building Investment Based on Integrating AHP and COPRAS-Grey Approach, In Proceedings of the International Conference on Construction and Real Estate Management 2018, :65-71)
  • Zolfani, S.H., Chen, I.S., Rezaeiniya, N., & Tamosaitiene, J., (2012). A Hybrid MCDM Model Encompassing AHP and Copras-G Methods for Selecting Company Supplier in Iran. Technological and Economic Development of Economy, Volume 18(3): 529-543. DOI:10.3846/20294913.2012.709472

    Adıgüzel Mercangöz, B., Yıldırım, B. F., Kuzu Yıldırım, S. (2020). "Time Period Based COPRAS-G Method: Application on the Logistics Performance Index". LogForum, 16 (2), 239-250. http://doi.org/10.17270/J.LOG.2020.432

    The author(s) received no financial support for the research, authorship, and/or publication of this article.