DEPO YERİ SEÇİM KRİTERLERİNİN ÖNEM DÜZEYİNİN BWM YÖNTEMİ İLE ÖLÇÜLMESİ

Depo performansı özellikle depolama aktivitelerinin gerçekleştirildiği lokasyon açısından tedarik zincirinin performansının arttırılmasında önemli bir role sahiptir. Depo yeri seçim kriterleri tedarik zincirinde operasyonel verimliliği sağlamak ve operasyonel maliyetleri azaltmak için karar vericiler ve araştırmacılar tarafından yaygın olarak kullanılmaktadır. Diğer taraftan, depo yeri seçimi kararı genellikle kişisel yargılara dayanılarak alınır. Bu çalışma, çok kriterli bir karar verme yöntemi olan BWM kullanılarak literatürden elde edilen depo yeri seçim kriterlerinin göreceli ağırlıklarını belirlemektedir. Çalışma kapsamında Türkiye’de yerleşik tedarikçiler, üreticiler ve dağıtıcılar olmak üzere farklı firmalardan beş depo uzmanına anket formu gönderilmiştir. Çalışmada ‘Pazar’ kriterinin depo yeri seçiminde diğer kriterlere göre baskın bir etkiye sahip olduğu görülmüştür.

MEASURING THE IMPORTANCE OF WAREHOUSE LOCATION SELECTION CRITERIA USING BEST-WORST METHOD

Warehouse performance has a major role in improving the effectiveness of supply chain considering especially the location where the warehousing activities occur. Warehouse location selection criteria have been widely used by decision-makers and researchers to achieve operational efficiency and reduce operational costs in the supply chain. On the other hand, the location of the warehouse is usually evaluated and decided on individual basis. This research determines the weights of the warehouse location criteria obtained from literature, using the Best-Worst Method (BWM), a multi-criteria decision-making method. A questionnaire form was sent to five warehouse professional experts in different companies including suppliers, manufacturers, and distributors in Turkey. From the BWM results, we can understand that ‘Market’ criterion has the most influence on decision about warehouse location.

___

  • Amiri M., Tabatabaei, M.H., Ghahremanloo, M., Ghrobaee, M.K., Zavadskas, E.K., Antucheviciene, J. (2020). A new fuzzy approach based on BWM and fuzzy preference programming for hospital performance evaluation: A case study. Applied Soft Computing Journal, 92, (2020), 1-13.
  • Ballou, R.H. (1981). Reformulating a logistics strategy: A concern for the past, present and future. International Journal of Physical Distribution & Materials Management, 11 (8), 71-83.
  • Budak, A., Kaya, İ., Karaşan, A., Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing Journal, 92, (2020), 1-21.
  • Chen, C.L., Yuan, T.W., Lee, W.C.(2007). Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network. Journal of Chinese Institute of Chemical Engineers, 38 (2007), 393-407.
  • Christofides, N., Beasley, J.E. (1982). Extensions to a Lagrangean relaxation approach for the capacitated warehouse location problem. European Journal of Operational Research, 12 (1983), 19-28.
  • Colson, G. and Dorigo, F. (2004). A public warehouses selection support system. European Journal of Operational Research, 153 (2004), 332-349.
  • Demirel, T., Demirel, N.Ç., Kahraman, C. (2010). Multi-criteria warehouse location selection using Choquet integral. Expert Systems with Applications, 37 (2010), 3943-3952.
  • Dey, B., Bairagi, B., Sarkar, B., Sanyar, S.K. (2017). Group heterogeneity in multi member decision making model with an application to warehouse location selection in a supply chain. Computers and Industrial Engineering, 105 (2017), 101-122.
  • Farahani, R.Z., SteadieSeifi, M., Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling 34 (2010), 1689-1709.
  • García, J.L., Alvarado, A., Blanco, J., Jiménez, E., Maldonado, A.A., Cortés, G. (2014). Multi-attribute evaluation and selection of sites for agricultural product warehouses based on an Analytic Hierarchy Process. Computer and Electronics in Agriculture 100 (2014), 60-69.
  • Gupta, H., Barua, M. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, doi: 10.1016/j.jclepro.2017.03.125.
  • He, Y., Wang, X., Lin, Y., Zhou, F., Zhou, L. (2017). Sustainable decision making for joint distribution center location choice. Transportation Research Part D, 55, (2017), 202-216. Hosseini, Z.S., Flapper, S.D., Pirayesh, M. (2021). Sustainable supplier selection and order allocation under demand, supplier availability and supplier grading uncertainties. Computer and Industrial Engineering, doi.org/10.1016/j.cie.2021.107811.
  • Jhawar, A., Garg, S.K., Shikha, N., Khera, N. (2014). Analysis of skilled work force effect on the logistics performance index -case study form India. Logistics Research, 7, (117), 1-10.
  • Kang, S. (2020). Warehouse location choice: A case study in Los Angeles, CA. Journal of Transport Geography, 88, 966-923.
  • Kaviani, M.A., Tavana, M., Michnik, J., Kumar, A. (2020). An integrated framework for evaluating the barriers to successful implementation of reverse logistics in the automotive industry. Journal of Cleaner Production, 272, (122714).
  • Kelly, D.L., Marucheck, A.S. (1984). Planning horizon results for the dynamic warehouse location problem. Journal of Operations Management, 4, (3), 271-294.
  • Korpela, J., Tuominen, M. (1996). A decision aid in warehouse site selection. International Journal of Production Economics, 45 (1996), 169-180.
  • Melachrinoudis, E., Messac, A., Min, H. (2005). Consolidating a warehouse network: A physical programming approach. International Journal of Production Economics, 97, (2005), 1-17.
  • Özcan, T., Çelebi, N., Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision-making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications 38 (2011), 9773-9779.
  • Pamucar, D. Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: the fuzzy full consistency method. Facta Universitatis Series Mechanical Engineering, 3, (2020), 419-437.
  • Qian, X., Fang, S.C., Yin, M., Huang, M., Li, X. (2021), Selecting green third-party logistics providers for a loss-averse fourth party logistics provider in a multiattribute reverse auction. Information Sciences, 547, (2021), 357-377.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53 (2015), 49-57.
  • Rezaei, J., Roekel, W.S., Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68 (2018), 158-169.
  • Rezaei, J., Wang, J., Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42 (2015), 9152-9164.
  • Santosa, B., Kresna, I. G. N. A (2015). Capacitated warehouse location problem. Simulated annealing to solve single stage capacitated warehouse location problem. Procedia Manufacturing, 4 (2015), 62-70.
  • Shahparvari, S., Nasirian, A., Mohammadi, A., Noori, S., Chhetri, P. (2020). A GIS-LP integrated approach for the logistics hub location problem. Computers and Industrial Engineering, 146, (2020), 1-17.
  • Sharma, V., Kumar, A., Kumar, M. (2021). A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains. Materialstoday Proceedings, 47, (16), 5515-5519.
  • Singh, R., Chaudhary, N., Saxena, N. (2018). Selection of warehouse location for a global supply chain: A case study. IIMB Management Review, 30 (2018), 343-356.
  • Torabizadeh, M., Yusof, N. M., Ma’aram, A., Shaharoun, A.M. (2020). Identifying sustainable warehouse management system indicator and proposing new weighting method. Journal of Cleaner Production, 248 (2020), 1-11.
  • Vlachopoulou, M., Silleos, G., Manthou, V. (2001). Geographic information systems in warehouse site selection decisions. International Journal of Production Economics, 71 (2001), 205-212.
  • Żak, J., Weglinski, S. (2014). The selection of the logistics center location based on MCDM/A methodology. Transportation Research Procedia, 3, (2014), 555-564.