Araştırma Makalesi
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Yıl 2025, Sayı: 71
https://doi.org/10.30794/pausbed.1631489

Öz

Kaynakça

  • Allam, Z., Bibri, S. E., & Sharpe, S. A. (2022). The rising impacts of the COVID-19 pandemic and the Russia–Ukraine war: energy transition, climate justice, global inequality, and supply chain disruption. Resources, 11(11), 99.
  • Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in The Global Economy—The Logistics Performance Index and Its Indicators. Washington, DC: World Bank.
  • Arvis, J. F., Ulybina, D., & Wiederer, C. (2024). From survey to big data: The new logistics performance index. Policy Research Working Paper, 10772.
  • Chand, P., Thakkar, J. J., & Ghosh, K. K. (2020). Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model. Resources Policy, 68, 101742.
  • Chatterjee, S., & Chakraborty, S. (2024). A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes. Decision Analytics Journal, 11, 100451.
  • Chithambaranathan, P., Subramanian, N., Gunasekaran, A., & Palaniappan, P. K. (2015). Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, 166, 163-176.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  • Estampe, D., Lamouri, S., Paris, J. L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International journal of production economics, 142(2), 247-258.
  • Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., & Langović, Z. (2022). Novel hybrid MPSI–MARA decision-making model for support system selection in an underground mine. Systems, 10(6), 248.
  • Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinović, A. (2023). Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method) -Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm. CMES-Computer Modeling in Engineering & Sciences, 136(1).
  • Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European journal of operational research, 159(2), 269-295.
  • Hashmi, A. (2022). Factors Affecting the Supply Chain Resilience and Supply Chain Performance: Supply Chain Resilience and Supply Chain Performance. South Asian Journal of Operations and Logistics, 1(2), 65-85.
  • Jusufbašić, A. (2023). MCDM methods for selection of handling equipment in logistics: a brief review. Spectrum of Engineering and Management Sciences, 1(1), 13-24.
  • Kara, K., Yalçın, G. C., Simic, V., Baysal, Z., & Pamucar, D. (2024). The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries. Socio-Economic Planning Sciences, 92, 101822.
  • 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), 525.
  • Oubrahim, I., & Sefiani, N. (2024). An integrated multi-criteria decision-making approach for sustainable supply chain performance evaluation from a manufacturing perspective. International Journal of Productivity and Performance Management.
  • Stefanovic, N. (2014). Proactive supply chain performance management with predictive analytics. The Scientific World Journal, 2014(1), 528917.
  • Štilić, A., Puška, A., Božanić, D., & Tešić, D. (2023). Assessing the role of institutional reform in enhancing Balkan sustainable competitiveness: An Entropy-MARCOS perspective. Journal of Infrastructure, Policy and Development, 7(3), 2167.
  • Sufiyan, M., Haleem, A., Khan, S., & Khan, M. I. (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production and Consumption, 20, 40-57.
  • Sutia, S., Riadi, R., & Fahlevi, M. (2020). The Influence of supply chain performance and motivation on employee performance. International Journal of Supply Chain Management, 9(2), 86-92.
  • Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511.
  • Wang, C. N., & Van Thanh, N. (2022). Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain. Computers, Materials & Continua, 73(2).
  • Zou, Z. H., Yi, Y., & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18(5), 1020-1023.

Yıl 2025, Sayı: 71
https://doi.org/10.30794/pausbed.1631489

Öz

Kaynakça

  • Allam, Z., Bibri, S. E., & Sharpe, S. A. (2022). The rising impacts of the COVID-19 pandemic and the Russia–Ukraine war: energy transition, climate justice, global inequality, and supply chain disruption. Resources, 11(11), 99.
  • Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in The Global Economy—The Logistics Performance Index and Its Indicators. Washington, DC: World Bank.
  • Arvis, J. F., Ulybina, D., & Wiederer, C. (2024). From survey to big data: The new logistics performance index. Policy Research Working Paper, 10772.
  • Chand, P., Thakkar, J. J., & Ghosh, K. K. (2020). Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model. Resources Policy, 68, 101742.
  • Chatterjee, S., & Chakraborty, S. (2024). A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes. Decision Analytics Journal, 11, 100451.
  • Chithambaranathan, P., Subramanian, N., Gunasekaran, A., & Palaniappan, P. K. (2015). Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, 166, 163-176.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  • Estampe, D., Lamouri, S., Paris, J. L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International journal of production economics, 142(2), 247-258.
  • Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., & Langović, Z. (2022). Novel hybrid MPSI–MARA decision-making model for support system selection in an underground mine. Systems, 10(6), 248.
  • Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinović, A. (2023). Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method) -Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm. CMES-Computer Modeling in Engineering & Sciences, 136(1).
  • Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European journal of operational research, 159(2), 269-295.
  • Hashmi, A. (2022). Factors Affecting the Supply Chain Resilience and Supply Chain Performance: Supply Chain Resilience and Supply Chain Performance. South Asian Journal of Operations and Logistics, 1(2), 65-85.
  • Jusufbašić, A. (2023). MCDM methods for selection of handling equipment in logistics: a brief review. Spectrum of Engineering and Management Sciences, 1(1), 13-24.
  • Kara, K., Yalçın, G. C., Simic, V., Baysal, Z., & Pamucar, D. (2024). The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries. Socio-Economic Planning Sciences, 92, 101822.
  • 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), 525.
  • Oubrahim, I., & Sefiani, N. (2024). An integrated multi-criteria decision-making approach for sustainable supply chain performance evaluation from a manufacturing perspective. International Journal of Productivity and Performance Management.
  • Stefanovic, N. (2014). Proactive supply chain performance management with predictive analytics. The Scientific World Journal, 2014(1), 528917.
  • Štilić, A., Puška, A., Božanić, D., & Tešić, D. (2023). Assessing the role of institutional reform in enhancing Balkan sustainable competitiveness: An Entropy-MARCOS perspective. Journal of Infrastructure, Policy and Development, 7(3), 2167.
  • Sufiyan, M., Haleem, A., Khan, S., & Khan, M. I. (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production and Consumption, 20, 40-57.
  • Sutia, S., Riadi, R., & Fahlevi, M. (2020). The Influence of supply chain performance and motivation on employee performance. International Journal of Supply Chain Management, 9(2), 86-92.
  • Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511.
  • Wang, C. N., & Van Thanh, N. (2022). Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain. Computers, Materials & Continua, 73(2).
  • Zou, Z. H., Yi, Y., & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18(5), 1020-1023.

VRUPA ÜLKELERİNİN TEDARİK ZİNCİRİ PERFORMANSINI DEĞERLENDİRMEK İÇİN HİBRİT SPC-MARA KARAR MODELİ

Yıl 2025, Sayı: 71
https://doi.org/10.30794/pausbed.1631489

Öz

Bu makalede çeşitli Avrupa ülkelerinin tedarik zinciri performansı hibrit Çok Kriterli Karar Verme (ÇKKV) modeliyle analiz edilmektedir. Tedarik zinciri performansının değerlendirilmesi, literatür taramasıyla belirlenen on kritere dayanmaktadır. Bu çalışmanın verileri Dünya Bankası raporundan elde edilmiştir. Kriter ağırlıkları, Kriter Simetri Noktası (SPC) yöntemi kullanılarak belirlenirken, Avrupa ülkeleri genelindeki tedarik zinciri performansının değerlendirilmesi Alternatiflerin Sıralanması Alan Büyüklüğü (MARA) yöntemi ile gerçekleştirilmiştir. SPC analizi, deniz bağlantısının en kritik kriter olduğunu, posta bağlantısının ise en az önemli kriter olarak kabul edildiğini göstermektedir. MARA bulguları, Hollanda, Birleşik Krallık, Almanya, İspanya ve Kıbrıs'ın en yüksek tedarik zinciri performans seviyelerini sergilediğini vurgulamaktadır. Tersine, Danimarka, Slovenya, Litvanya, Bulgaristan ve Malta en düşük performansı göstermektedir. Ek olarak, sonuçların tutarlılığını doğrulamak için karşılaştırmalı analiz yapılmıştır.

Kaynakça

  • Allam, Z., Bibri, S. E., & Sharpe, S. A. (2022). The rising impacts of the COVID-19 pandemic and the Russia–Ukraine war: energy transition, climate justice, global inequality, and supply chain disruption. Resources, 11(11), 99.
  • Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in The Global Economy—The Logistics Performance Index and Its Indicators. Washington, DC: World Bank.
  • Arvis, J. F., Ulybina, D., & Wiederer, C. (2024). From survey to big data: The new logistics performance index. Policy Research Working Paper, 10772.
  • Chand, P., Thakkar, J. J., & Ghosh, K. K. (2020). Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model. Resources Policy, 68, 101742.
  • Chatterjee, S., & Chakraborty, S. (2024). A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes. Decision Analytics Journal, 11, 100451.
  • Chithambaranathan, P., Subramanian, N., Gunasekaran, A., & Palaniappan, P. K. (2015). Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, 166, 163-176.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  • Estampe, D., Lamouri, S., Paris, J. L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International journal of production economics, 142(2), 247-258.
  • Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., & Langović, Z. (2022). Novel hybrid MPSI–MARA decision-making model for support system selection in an underground mine. Systems, 10(6), 248.
  • Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinović, A. (2023). Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method) -Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm. CMES-Computer Modeling in Engineering & Sciences, 136(1).
  • Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European journal of operational research, 159(2), 269-295.
  • Hashmi, A. (2022). Factors Affecting the Supply Chain Resilience and Supply Chain Performance: Supply Chain Resilience and Supply Chain Performance. South Asian Journal of Operations and Logistics, 1(2), 65-85.
  • Jusufbašić, A. (2023). MCDM methods for selection of handling equipment in logistics: a brief review. Spectrum of Engineering and Management Sciences, 1(1), 13-24.
  • Kara, K., Yalçın, G. C., Simic, V., Baysal, Z., & Pamucar, D. (2024). The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries. Socio-Economic Planning Sciences, 92, 101822.
  • 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), 525.
  • Oubrahim, I., & Sefiani, N. (2024). An integrated multi-criteria decision-making approach for sustainable supply chain performance evaluation from a manufacturing perspective. International Journal of Productivity and Performance Management.
  • Stefanovic, N. (2014). Proactive supply chain performance management with predictive analytics. The Scientific World Journal, 2014(1), 528917.
  • Štilić, A., Puška, A., Božanić, D., & Tešić, D. (2023). Assessing the role of institutional reform in enhancing Balkan sustainable competitiveness: An Entropy-MARCOS perspective. Journal of Infrastructure, Policy and Development, 7(3), 2167.
  • Sufiyan, M., Haleem, A., Khan, S., & Khan, M. I. (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production and Consumption, 20, 40-57.
  • Sutia, S., Riadi, R., & Fahlevi, M. (2020). The Influence of supply chain performance and motivation on employee performance. International Journal of Supply Chain Management, 9(2), 86-92.
  • Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511.
  • Wang, C. N., & Van Thanh, N. (2022). Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain. Computers, Materials & Continua, 73(2).
  • Zou, Z. H., Yi, Y., & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18(5), 1020-1023.

A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES

Yıl 2025, Sayı: 71
https://doi.org/10.30794/pausbed.1631489

Öz

This paper analyzes the supply chain performance of various European countries through a hybrid Multi-Criteria Decision-Making (MCDM) model. The evaluation of supply chain performance is based on ten criteria identified through a literature review. Data for this study were obtained from the World Bank’s report. The criteria weights are determined using the Symmetry Point of Criterion (SPC) method, while evaluating supply chain performance across European countries is conducted by the Magnitude of the Area for the Ranking of Alternatives (MARA) method. The SPC analysis indicates that maritime connectivity is the most critical criterion, whereas postal connectivity is deemed the least significant. The MARA findings highlight that the Netherlands, the United Kingdom, Germany, Spain, and Cyprus exhibit the highest supply chain performance levels. Conversely, Denmark, Slovenia, Lithuania, Bulgaria, and Malta show the lowest performance. Additionally, a comparative analysis was performed to validate the robustness of the results.

Kaynakça

  • Allam, Z., Bibri, S. E., & Sharpe, S. A. (2022). The rising impacts of the COVID-19 pandemic and the Russia–Ukraine war: energy transition, climate justice, global inequality, and supply chain disruption. Resources, 11(11), 99.
  • Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in The Global Economy—The Logistics Performance Index and Its Indicators. Washington, DC: World Bank.
  • Arvis, J. F., Ulybina, D., & Wiederer, C. (2024). From survey to big data: The new logistics performance index. Policy Research Working Paper, 10772.
  • Chand, P., Thakkar, J. J., & Ghosh, K. K. (2020). Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model. Resources Policy, 68, 101742.
  • Chatterjee, S., & Chakraborty, S. (2024). A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes. Decision Analytics Journal, 11, 100451.
  • Chithambaranathan, P., Subramanian, N., Gunasekaran, A., & Palaniappan, P. K. (2015). Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, 166, 163-176.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
  • Estampe, D., Lamouri, S., Paris, J. L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International journal of production economics, 142(2), 247-258.
  • Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., & Langović, Z. (2022). Novel hybrid MPSI–MARA decision-making model for support system selection in an underground mine. Systems, 10(6), 248.
  • Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinović, A. (2023). Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method) -Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm. CMES-Computer Modeling in Engineering & Sciences, 136(1).
  • Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European journal of operational research, 159(2), 269-295.
  • Hashmi, A. (2022). Factors Affecting the Supply Chain Resilience and Supply Chain Performance: Supply Chain Resilience and Supply Chain Performance. South Asian Journal of Operations and Logistics, 1(2), 65-85.
  • Jusufbašić, A. (2023). MCDM methods for selection of handling equipment in logistics: a brief review. Spectrum of Engineering and Management Sciences, 1(1), 13-24.
  • Kara, K., Yalçın, G. C., Simic, V., Baysal, Z., & Pamucar, D. (2024). The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries. Socio-Economic Planning Sciences, 92, 101822.
  • 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), 525.
  • Oubrahim, I., & Sefiani, N. (2024). An integrated multi-criteria decision-making approach for sustainable supply chain performance evaluation from a manufacturing perspective. International Journal of Productivity and Performance Management.
  • Stefanovic, N. (2014). Proactive supply chain performance management with predictive analytics. The Scientific World Journal, 2014(1), 528917.
  • Štilić, A., Puška, A., Božanić, D., & Tešić, D. (2023). Assessing the role of institutional reform in enhancing Balkan sustainable competitiveness: An Entropy-MARCOS perspective. Journal of Infrastructure, Policy and Development, 7(3), 2167.
  • Sufiyan, M., Haleem, A., Khan, S., & Khan, M. I. (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production and Consumption, 20, 40-57.
  • Sutia, S., Riadi, R., & Fahlevi, M. (2020). The Influence of supply chain performance and motivation on employee performance. International Journal of Supply Chain Management, 9(2), 86-92.
  • Uygun, Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, 102, 502-511.
  • Wang, C. N., & Van Thanh, N. (2022). Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain. Computers, Materials & Continua, 73(2).
  • Zou, Z. H., Yi, Y., & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18(5), 1020-1023.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uluslararası Lojistik
Bölüm Araştırma Makalesi
Yazarlar

Emre Kadir Özekenci 0000-0001-6669-0006

Erken Görünüm Tarihi 16 Ekim 2025
Yayımlanma Tarihi 20 Ekim 2025
Gönderilme Tarihi 2 Şubat 2025
Kabul Tarihi 21 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 71

Kaynak Göster

APA Özekenci, E. K. (2025). A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(71). https://doi.org/10.30794/pausbed.1631489
AMA Özekenci EK. A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES. PAUSBED. Ekim 2025;(71). doi:10.30794/pausbed.1631489
Chicago Özekenci, Emre Kadir. “A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 71 (Ekim 2025). https://doi.org/10.30794/pausbed.1631489.
EndNote Özekenci EK (01 Ekim 2025) A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 71
IEEE E. K. Özekenci, “A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES”, PAUSBED, sy. 71, Ekim2025, doi: 10.30794/pausbed.1631489.
ISNAD Özekenci, Emre Kadir. “A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 71 (Ekim2025). https://doi.org/10.30794/pausbed.1631489.
JAMA Özekenci EK. A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES. PAUSBED. 2025. doi:10.30794/pausbed.1631489.
MLA Özekenci, Emre Kadir. “A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 71, 2025, doi:10.30794/pausbed.1631489.
Vancouver Özekenci EK. A HYBRID SPC-MARA DECISION MODEL FOR ASSESSING THE SUPPLY CHAIN PERFORMANCE OF EUROPEAN COUNTRIES. PAUSBED. 2025(71).