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Evaluation of Supply Chain Resilience in N-11 Countries by MEREC Based EDAS, MARCOS, WASPAS Integrated Method

Year 2023, Volume: 9 Issue: 1, 1 - 15, 01.11.2023

Zafer Duran

https://doi.org/10.51803/yssr.1397653

Abstract

Supply chain resilience is an important factor in ensuring the growth and development of economies, as well as profitable operations in businesses. Because, unstable supply chains can cause an increase in operational costs, loss of workforce, and a decrease in economic mobility as a result of possible disruptions. In this study, supply chain resilience was evaluated consider-ing the potential of N-11 countries. The Global Resilience Index data published by FM Global was used in the evaluation process, and the weights of the indicators related to the resilience of the supply chain were determined by the MEREC method. The relative rankings of the coun-tries were then determined by the EDAS, MARCOS, and WASPAS methods. The resulting rankings were combined with the BORDA counting method to form the final rankings for supply chain resilience of N-11 countries. The focus on the subject and the methods used have given the research a unique identity. As a result of the calculations, Supply Chain Visibility and Corporate Governance indicators stand out as the most important indicators affecting supply chain resilience in N-11 countries, while South Korea and Türkiye are the two best countries in terms of supply chain resilience among N-11 countries. Various suggestions were made to researchers and practitioners in line with the findings.

Keywords

N-11 countries Multi-criteria decision-making methods Supply chain resilience

References

  • REFERENCES Alinezhad, A., & Khalili, J. (2019). New methods and applications in multiple attribute decision making (MADM) (Vol. 277). Springer. [CrossRef]
  • Banerjee, T., Trivedi, A., Sharma, G. M., Gharib, M., & Hameed, S. S. (2022). Analyzing organizational barriers towards building post pandemic supply chain resilience in Indian MSMEs: a grey-DEMATEL approach. Benchmarking: An International Journal, Ahead of print. doi: 10.1108/BIJ-11-2021-0677. [CrossRef]
  • Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 44874507. [CrossRef]
  • Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 5573. [CrossRef]
  • Çınaroğlu, E. (2021). Innovative and entrepreneurial university analysis by CRITIC based MARCOS method. Journal of Entrepreneurship and Innovation Management, 10(1), 111133. Das, D., Datta, A., Kumar, P., Kazancoglu, Y., & Ram, M. (2021). Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach. Operations Management Research, 15, 249267. [CrossRef]
  • Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1-2), 414430. [CrossRef]
  • Falasca, M., Zobel, C. W., & Cook, D. (2008, May). A decision support framework to assess supply chain resilience. Proceedings of the 5th International ISCRAM Conference (pp. 596-605).
  • FM Global (2022). FM global resilience index. https://www.fmglobal.com/research-and-resources/tools-and-resources/resilienceindex/explore-the-data
  • Goel, R. K., Saunoris, J. W., & Goel, S. S. (2021). Supply chain performance and economic growth: The impact of COVID-19 disruptions. Journal of Policy Modeling, 43(2), 298316. [CrossRef]
  • Gunasekaran, A., Subramanian, N., & Rahman, S. (2015). Supply chain resilience: role of complexities and strategies. International Journal of Production Research, 53(22), 68096819. [CrossRef]
  • Hearnshaw, E. J., & Wilson, M. M. (2013). A complex network approach to supply chain network theory. International Journal of Operations & Production Management, 33(4), 442469. [CrossRef]
  • Hendricks, K. B., & Singhal, V. R. (2005). Association between supply chain glitches and operating performance. Management Science, 51(5), 695711. [CrossRef]
  • Hsu, C. H., Li, M. G., Zhang, T. Y., Chang, A. Y., Shangguan, S. Z., & Liu, W. L. (2022). Deploying big data enablers to strengthen supply chain resilience to mitigate sustainable risks based on integrated HOQ-MCDM framework. Mathematics, 10(8), Article 1233. [CrossRef]
  • Jafarnejad, A., Momeni, M., Razavi Hajiagha, S. H., & Faridi Khorshidi, M. (2019). A dynamic supply chain resilience model for medical equipment’s industry. Journal of Modelling in Management, 14(3), 816840. [CrossRef]
  • Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G., & Brzakovic, M. (2018). An approach to personnel selection in the IT industry based on the EDAS method. Transformations in Business & Economics, 17(42), 5465.
  • Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), Article 525. [CrossRef]
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435451. [CrossRef]
  • Lansdowne, Z. F., & Woodward, B. S. (1996). Applying the borda ranking method. Air Force Journal of Logistics, 20(2), 2729.
  • Lashgari, S., Antuchevičienė, J., Delavari, A., & Kheirkhah, O. (2014). Using QSPM and WASPAS methods for determining outsourcing strategies. Journal of Business Economics and Management, 15(4), 729743. [CrossRef]
  • Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1), 121. [CrossRef]
  • Piya, S., Shamsuzzoha, A., & Khadem, M. (2022). Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach. Applied Soft Computing, 121, Article 108756. [CrossRef]
  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain Resilience. The International Journal of Logistics Management, 20(1), 124–143. [CrossRef]
  • Rehman, O. U., & Ali, Y. (2022). Enhancing healthcare supply chain resilience: decision-making in a fuzzy environment. The International Journal of Logistics Management, 33(2), 520546. [CrossRef]
  • Roberta Pereira, C., Christopher, M., & Lago Da Silva, A. (2014). Achieving supply chain resilience: the role of procurement. Supply Chain Management: An International Journal, 19(5/6), 626642. [CrossRef]
  • Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471484. [CrossRef]
  • Scholten, K., Scott, P. S., & Fynes, B. (2014). Mitigation processes–antecedents for building supply chain resilience. Supply Chain Management: An International Journal, 19(2), 211228. [CrossRef]
  • Sheffi, Y. (2015), The power of resilience: How the best companies manage the unexpected. MIT Press. [CrossRef]
  • Soni, U., Jain, V., & Kumar, S. (2014). Measuring supply chain resilience using a deterministic modeling approach. Computers & Industrial Engineering, 74, 1125. [CrossRef] Spiegler, V. L., Naim, M. M., & Wikner, J. (2012). A Control Engineering Approach to the Assessment of Supply Chain Resilience. International Journal of Production Research, 50(21), 61626187. Sprecher, B., Daigo, I., Murakami, S., Kleijn, R., Vos, M., & Kramer, G. J. (2015). Framework for resilience in material supply chains, with a case study from the 2010 rare earth crisis. Environmental Science & Technology, 49(11), 67406750. [CrossRef]
  • Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140, Article 106231. [CrossRef]
  • Şahin, M. (2021). Güncel ve uygulamalı çok kriterli karar verme yöntemleri. Nobel Akademik Yayıncılık. [Turkish]
  • Timperio, G., Panchal, G. B., De Souza, R., Goh, M., & Samvedi, A., (2016) Decision making framework for emergency response preparedness: A supply chain resilience approach, 2016 IEEE International Conference on Management of Innovation and Technology (ICMIT), (pp. 78-82). 19-22 September 2016. [CrossRef]
  • Wagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of Purchasing and Supply Management, 12(6), 301312. [CrossRef]
  • Wen, Z., & Liao, H. (2022). Capturing attitudinal characteristics of decision-makers in group decision making: Application to select policy recommendations to enhance supply chain resilience under COVID-19 outbreak. Operations Management Research, 15, 179–194. [CrossRef]
  • Wicher, P., Zapletal, F., Lenort, R., & Staš, D. (2016). Measuring the metallurgical supply chain resilience using fuzzy analytic network process. Metalurgija, 55(4), 783786.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 36. [CrossRef]
  • Zhang, H., Jia, F., & You, J. X. (2021). Striking a balance between supply chain resilience and supply chain vulnerability in the cross-border e-commerce supply chain. International Journal of Logistics Research and Applications, 26, 320344. [CrossRef]

N-11 Ülkelerinde Tedarik Zinciri Dayanıklılığının MEREC Tabanlı EDAS, MARCOS, WASPAS Bütünleşik Yöntemiyle Değerlendirilmesi

Year 2023, Volume: 9 Issue: 1, 1 - 15, 01.11.2023

Zafer Duran

https://doi.org/10.51803/yssr.1397653

Abstract

Tedarik zinciri dayanıklılığı, işletmelerde operasyonların kârlı bir şekilde gerçekleştirilebilmesinin yanı sıra ekonomilerde de büyümenin ve kalkınmanın sağlanabilmesinde önemli bir faktördür. Zira dayanıksız tedarik zincirleri, olası aksaklıklar neticesinde operasyon maliyetlerinin yükselmesine, iş gücü kaybına ve ekonomik hareketliliğin azalmasına neden olabilmektedir. Bu çalışmada N-11 ülkelerinin sahip olduğu potansiyel göz önünde bulundurularak tedarik zinciri dayanıklılıkları değerlendirilmiştir. Değerlendirme işleminde FM Global adlı kuruluş tarafından yayınlanan Küresel Dayanıklılık İndeksi verileri kullanılmış olup tedarik zinciri dayanıklılığına ilişkin göstergelerin ağırlıkları MEREC yöntemiyle, ülkelerin görece sıralamaları EDAS, MARCOS ve WASPAS yöntemleriyle belirlenmiştir. Elde edilen sıralamalar BORDA sayım yöntemiyle birleştirilerek N-11 ülkelerinin tedarik zinciri dayanıklılığına ilişkin nihai sıralamaları oluşturulmuştur. Odaklanılan konu ve kullanılan yöntemler, araştırmaya özgün bir kimlik kazandırmaktadır. Yapılan hesaplamalar sonucunda Tedarik Zinciri Görünürlüğü ve Kurumsal Yönetim göstergeleri N-11 ülkelerinde tedarik zinciri dayanıklılığını etkileyen en önemli göstergeler olarak ön plana çıkarken Güney Kore ve Türkiye’nin N-11 ülkeleri arasında tedarik zinciri dayanıklılığı bakımından en iyi iki ülke olduğu görülmüştür. Elde edilen bulgular doğrultusunda araştırmacılara ve uygulayıcılara çeşitli önerilerde bulunulmuştur.

Keywords

N-11 ülkeleri Tedarik zinciri dayanıklılığı Çok kriterli karar verme yöntemleri.

References

  • REFERENCES Alinezhad, A., & Khalili, J. (2019). New methods and applications in multiple attribute decision making (MADM) (Vol. 277). Springer. [CrossRef]
  • Banerjee, T., Trivedi, A., Sharma, G. M., Gharib, M., & Hameed, S. S. (2022). Analyzing organizational barriers towards building post pandemic supply chain resilience in Indian MSMEs: a grey-DEMATEL approach. Benchmarking: An International Journal, Ahead of print. doi: 10.1108/BIJ-11-2021-0677. [CrossRef]
  • Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 44874507. [CrossRef]
  • Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 5573. [CrossRef]
  • Çınaroğlu, E. (2021). Innovative and entrepreneurial university analysis by CRITIC based MARCOS method. Journal of Entrepreneurship and Innovation Management, 10(1), 111133. Das, D., Datta, A., Kumar, P., Kazancoglu, Y., & Ram, M. (2021). Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach. Operations Management Research, 15, 249267. [CrossRef]
  • Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1-2), 414430. [CrossRef]
  • Falasca, M., Zobel, C. W., & Cook, D. (2008, May). A decision support framework to assess supply chain resilience. Proceedings of the 5th International ISCRAM Conference (pp. 596-605).
  • FM Global (2022). FM global resilience index. https://www.fmglobal.com/research-and-resources/tools-and-resources/resilienceindex/explore-the-data
  • Goel, R. K., Saunoris, J. W., & Goel, S. S. (2021). Supply chain performance and economic growth: The impact of COVID-19 disruptions. Journal of Policy Modeling, 43(2), 298316. [CrossRef]
  • Gunasekaran, A., Subramanian, N., & Rahman, S. (2015). Supply chain resilience: role of complexities and strategies. International Journal of Production Research, 53(22), 68096819. [CrossRef]
  • Hearnshaw, E. J., & Wilson, M. M. (2013). A complex network approach to supply chain network theory. International Journal of Operations & Production Management, 33(4), 442469. [CrossRef]
  • Hendricks, K. B., & Singhal, V. R. (2005). Association between supply chain glitches and operating performance. Management Science, 51(5), 695711. [CrossRef]
  • Hsu, C. H., Li, M. G., Zhang, T. Y., Chang, A. Y., Shangguan, S. Z., & Liu, W. L. (2022). Deploying big data enablers to strengthen supply chain resilience to mitigate sustainable risks based on integrated HOQ-MCDM framework. Mathematics, 10(8), Article 1233. [CrossRef]
  • Jafarnejad, A., Momeni, M., Razavi Hajiagha, S. H., & Faridi Khorshidi, M. (2019). A dynamic supply chain resilience model for medical equipment’s industry. Journal of Modelling in Management, 14(3), 816840. [CrossRef]
  • Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G., & Brzakovic, M. (2018). An approach to personnel selection in the IT industry based on the EDAS method. Transformations in Business & Economics, 17(42), 5465.
  • Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), Article 525. [CrossRef]
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435451. [CrossRef]
  • Lansdowne, Z. F., & Woodward, B. S. (1996). Applying the borda ranking method. Air Force Journal of Logistics, 20(2), 2729.
  • Lashgari, S., Antuchevičienė, J., Delavari, A., & Kheirkhah, O. (2014). Using QSPM and WASPAS methods for determining outsourcing strategies. Journal of Business Economics and Management, 15(4), 729743. [CrossRef]
  • Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1), 121. [CrossRef]
  • Piya, S., Shamsuzzoha, A., & Khadem, M. (2022). Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach. Applied Soft Computing, 121, Article 108756. [CrossRef]
  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain Resilience. The International Journal of Logistics Management, 20(1), 124–143. [CrossRef]
  • Rehman, O. U., & Ali, Y. (2022). Enhancing healthcare supply chain resilience: decision-making in a fuzzy environment. The International Journal of Logistics Management, 33(2), 520546. [CrossRef]
  • Roberta Pereira, C., Christopher, M., & Lago Da Silva, A. (2014). Achieving supply chain resilience: the role of procurement. Supply Chain Management: An International Journal, 19(5/6), 626642. [CrossRef]
  • Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471484. [CrossRef]
  • Scholten, K., Scott, P. S., & Fynes, B. (2014). Mitigation processes–antecedents for building supply chain resilience. Supply Chain Management: An International Journal, 19(2), 211228. [CrossRef]
  • Sheffi, Y. (2015), The power of resilience: How the best companies manage the unexpected. MIT Press. [CrossRef]
  • Soni, U., Jain, V., & Kumar, S. (2014). Measuring supply chain resilience using a deterministic modeling approach. Computers & Industrial Engineering, 74, 1125. [CrossRef] Spiegler, V. L., Naim, M. M., & Wikner, J. (2012). A Control Engineering Approach to the Assessment of Supply Chain Resilience. International Journal of Production Research, 50(21), 61626187. Sprecher, B., Daigo, I., Murakami, S., Kleijn, R., Vos, M., & Kramer, G. J. (2015). Framework for resilience in material supply chains, with a case study from the 2010 rare earth crisis. Environmental Science & Technology, 49(11), 67406750. [CrossRef]
  • Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140, Article 106231. [CrossRef]
  • Şahin, M. (2021). Güncel ve uygulamalı çok kriterli karar verme yöntemleri. Nobel Akademik Yayıncılık. [Turkish]
  • Timperio, G., Panchal, G. B., De Souza, R., Goh, M., & Samvedi, A., (2016) Decision making framework for emergency response preparedness: A supply chain resilience approach, 2016 IEEE International Conference on Management of Innovation and Technology (ICMIT), (pp. 78-82). 19-22 September 2016. [CrossRef]
  • Wagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of Purchasing and Supply Management, 12(6), 301312. [CrossRef]
  • Wen, Z., & Liao, H. (2022). Capturing attitudinal characteristics of decision-makers in group decision making: Application to select policy recommendations to enhance supply chain resilience under COVID-19 outbreak. Operations Management Research, 15, 179–194. [CrossRef]
  • Wicher, P., Zapletal, F., Lenort, R., & Staš, D. (2016). Measuring the metallurgical supply chain resilience using fuzzy analytic network process. Metalurgija, 55(4), 783786.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 36. [CrossRef]
  • Zhang, H., Jia, F., & You, J. X. (2021). Striking a balance between supply chain resilience and supply chain vulnerability in the cross-border e-commerce supply chain. International Journal of Logistics Research and Applications, 26, 320344. [CrossRef]
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Econometrics Theory
Journal Section Makaleler
Authors

Zafer Duran 0000-0002-7227-4196

Publication Date November 1, 2023
Published in Issue Year 2023 Volume: 9 Issue: 1

Cite

APA Duran, Z. (2023). N-11 Ülkelerinde Tedarik Zinciri Dayanıklılığının MEREC Tabanlı EDAS, MARCOS, WASPAS Bütünleşik Yöntemiyle Değerlendirilmesi. Yildiz Social Science Review, 9(1), 1-15. https://doi.org/10.51803/yssr.1397653