Research Article
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Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

References

  • Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics management practices: examining the mediating influences of market, environmental and social performances. Journal of cleaner production, 258, 120613.
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
  • Aylak, B. L. (2022). Impacts of Sustainability on Supply Chain Management. Avrupa Bilim ve Teknoloji Dergisi, (34), 105-109.
  • Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to compete 2018. Trade Logistics in the Global Economy, the Logistics Performance Index and Its Indicators Report (The International Bank for Reconstruction and Development/The World Bank, Washington, DC, 2018).
  • Bazani, C. L., Pereira, J. M., & Leal, E. A. (2020). Logistics Performance Index: What is Brazil's logistics performance in the international market? International Journal of Logistics Systems and Management, 37(1), 38–54.
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 36(1), 34–42. https://doi.org/10.1016/j.ajsl.2019.10.001
  • Blashfield, R. K. (1976). Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. Psychological Bulletin, 83(3), 377.
  • Bílgín, E. (2021). Industry 4.0 and sustainable supply chain. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 43(1), 123-144.
  • Bucher, S. (2016). Measuring of Environmental Performance Index in Europe. Rocznik Ochrona Środowiska, 18.
  • 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, 195, 1514–1524.
  • Chen, Y., Mi, Z., Xiao, Z., & Zhang, Y. (2021). COVID-19 Influence: A General Analysis using Machine Learning Methods. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 284–290.
  • Civelek, M. E., Uca, N., & Çemberci, M. (2015). The mediator effect of logistics performance index on the relation between global competitiveness index and gross domestic product. European Scientific Journal May.
  • d'Aleo, V. (2015). The mediator role of Logistic Performance Index: A comparative study. Journal of International Trade, Logistics and Law, 1(1), 1–7.
  • Daugherty, P. J., Ellinger, A. E., & Gustin, C. M. (1996). Integrated logistics: Achieving logistics performance improvements. Supply Chain Management: An International Journal, 1(3), 25–33. https://doi.org/10.1108/13598549610155297
  • Demir, H., Erdoğmuş, P., & Kekeçoğlu, M. (2018). Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(1), 47–67.
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2016). Linking to compete: Logistics and global competitiveness interaction. Transport Policy, 48, 117–128. https://doi.org/10.1016/j.tranpol.2016.01.015
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197–207. https://doi.org/10.1016/j.tranpol.2019.06.014
  • El-Nakib, I., & Elzarka, S. (2014). Measuring supply chain efficiency in MENA countries: A green perspective. Proceeding of theLimcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • 19th Logistics Research Network LRN Annual Conference.
  • Environmental Performance Index 2018. (2022, September 22). 2018 Environmental Performance Index. https://doi.org/10.7927/H4X928CF
  • 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.
  • Guo, X., Ren, D., & Shi, J. (2016). Carbon emissions, logistics volume and GDP in China: Empirical analysis based on panel data model. Environmental Science and Pollution Research, 23(24), 24758–24767.
  • Islam, M. S., Moeinzadeh, S., Tseng, M.-L., & Tan, K. (2021). A literature review on environmental concerns in logistics: Trends and future challenges. International Journal of Logistics Research and Applications, 24(2), 126–151. https://doi.org/10.1080/13675567.2020.1732313
  • Jæger, B., Menebo, M. M., & Upadhyay, A. (2021). Identification of environmental supply chain bottlenecks: A case study of the Ethiopian healthcare supply chain. Management of Environmental Quality: An International Journal, 32(6), 1233–1254. https://doi.org/10.1108/MEQ-12-2019-0277
  • Kabak, Ö., Önsel Ekici, Ş., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238–246. https://doi.org/10.1016/j.tranpol.2019.10.007
  • Kálmán, B., & Tóth, A. (2021). Links between the economy competitiveness and logistics performance in the Visegrád Group countries: Empirical evidence for the years 2007-2018. Entrepreneurial Business and Economics Review, 9(3), 169–190.
  • Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: An empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management.
  • Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
  • Khan, S. A. R. (2019). The nexus between carbon emissions, poverty, economic growth, and logistics operations-empirical evidence from southeast Asian countries. Environmental Science and Pollution Research, 26(13), 13210–13220. https://doi.org/10.1007/s11356-019-04829-4
  • Kim, I., & Min, H. (2011). Measuring supply chain efficiency from a green perspective. Management Research Review, 34(11), 1169–1189.
  • Korinek, J., & Sourdin, P. (2011). To what extent are high-quality logistics services trade facilitating?
  • Larson, P. D., & Halldorsson, A. (2004). Logistics versus supply chain management: An international survey. International Journal of Logistics Research and Applications, 7(1), 17–31. https://doi.org/10.1080/13675560310001619240
  • Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020). Sustainability management and performance in the urban corporate economy: a systematic literature review. Sustainability, 12(18), 7705.
  • Limcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282–291.
  • Lukáč, J., Mihalčová, B., Manová, E., Kozel, R., Vilamova, Š., & Čulková, K. (2020). The position of the Visegrád countries by clustering methods based on indicator environmental performance index. Ekológia, 39(1), 16–26.
  • Ma, E. W., & Chow, T. W. (2004). A new shifting grid clustering algorithm. Pattern Recognition, 37(3), 503–514.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • Mariano, E. B., Gobbo Jr, J. A., de Castro Camioto, F., & do Nascimento Rebelatto, D. A. (2017). CO2 emissions and logistics performance: A composite index proposal. Journal of Cleaner Production, 163, 166–178.
  • Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169–192. https://doi.org/10.1016/S1514-0326(17)30008-9
  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982–2992. https://doi.org/10.1080/00036846.2014.916394
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13–34.
  • Miniak-Górecka, A., Podlaski, K., & Gwizdałła, T. (2022). Using k-means clustering in python with periodic boundary conditions. Symmetry, 14(6), 1237.
  • Nguyen, H. (2021). The role of logistics industry in the sustainable economic development of Southeast Asian countries. Accounting, 7(7), 1681–1688.
  • Nikmah, T. L., Harahap, N. H. S., Utami, G. C., & Razzaq, M. M. (2023). Customer Segmentation Based on Loyalty Level Using K-Means and LRFM Feature Selection in Retail Online Store. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, 7(1), 21-28.
  • Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k Means Clustering algorithm for prediction of Students Academic Performance. ArXiv Preprint ArXiv:1002.2425.
  • Phanich, M., Pholkul, P., & Phimoltares, S. (2010). Food Recommendation System Using Clustering Analysis for Diabetic Patients. 2010 International Conference on Information Science and Applications, 1–8. https://doi.org/10.1109/ICISA.2010.5480416
  • Polat, M., Kara, K., & Yalcin, G. C. (2022). Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis. Business and Economics Research Journal, 13(2), 221–238.
  • Revelle, W. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Behavioral Research, 14(1), 57–74.
  • Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23–32. https://doi.org/10.1016/j.rtbm.2017.10.001
  • Sala-i-Martin, X., Blanke, J., Hanouz, M. D., Geiger, T., Mia, I., & Paua, F. (2007). The global competitiveness index: Measuring the productive potential of nations. The Global Competitiveness Report, 2008, 3–50.
  • Sala-i-Martin, X., Crotti, R., Di Battista, A., Hanouz, M. D., Galvan, C., Geiger, T., & Marti, G. (2015). Reaching beyond the new normal: Findings from the global competitiveness index 2015–2016. The Global Competitiveness Report, 2016(2015), 3–41.
  • Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
  • Standard country or area codes for statistical use (M49). (2023, January 1). Standard Country or Area Codes for Statistical Use. https://unstats.un.org/unsd/methodology/m49/overview/ Statistical Annex-World Economic Situation and Prospects 2022. (2023, January 1).
  • Statistical Annex. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf
  • Taşkın, A. G. D. Ç., & Emel, G. G. (2010). Veri Madenciliğinde Kümeleme Yaklaşimlari Ve Kohonen Ağlari İle Perakendecilik Sektöründe Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 395–409.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
  • Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).
  • Wang, Q.-J., Geng, Y., & Xia, X.-Q. (2021). Revisited Globalization's Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
  • Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121

Cluster Analysis on Supply Chain Management-Related Indicators

Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

The supply chain performance of countries has a significant impact on the overall performance of countries. These indices primarily emphasized countries' standings, rankings, and improvement areas. Clustering countries based on a single index does not always yield the desired results. Using cluster analysis may help get critical information when many indicators are evaluated. The supply chain-connected indicators were chosen to be included in the research initially. In this study, three global indices were selected. We chose the Logistics Performance Index(LPI) to evaluate the logistics industry, which is essential in supply chain management. Logistics is one of the critical areas that affect and have also been affected by many fundamental indicators used to evaluate a country's performance. One critical indicator that globally measures the processes is the Logistics Performance Index. We included Environmental Performance Index(EPI) in the study to evaluate environmental policies that impact supply chain operations. The final index used in the study is the Global Competitiveness Index(GCI), which examines the competitiveness of countries with a heavy dependence on supply chain management performance. It is one of the crucial indications in evaluating a country's productivity. We used clustering analysis based on supply chain management-related indicators in the following phase. K-Means clustering algorithm was applied to the extracted data set. Python code is written to implement the K-Means clustering algorithm. In the final part of the study, differences between clusters and submitted research proposals ideas were discussed. This research proposes a three-step methodological framework for mining supply chain indicators derived from the LPI, GCI, and EPI indicators. The research aims to conclude from the analyses of the change in centers based on indicators, the variation based on datasets between clusters, and the grouping of countries based on any combination of the LPI, GCI, and EPI indicators .

References

  • Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics management practices: examining the mediating influences of market, environmental and social performances. Journal of cleaner production, 258, 120613.
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
  • Aylak, B. L. (2022). Impacts of Sustainability on Supply Chain Management. Avrupa Bilim ve Teknoloji Dergisi, (34), 105-109.
  • Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to compete 2018. Trade Logistics in the Global Economy, the Logistics Performance Index and Its Indicators Report (The International Bank for Reconstruction and Development/The World Bank, Washington, DC, 2018).
  • Bazani, C. L., Pereira, J. M., & Leal, E. A. (2020). Logistics Performance Index: What is Brazil's logistics performance in the international market? International Journal of Logistics Systems and Management, 37(1), 38–54.
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 36(1), 34–42. https://doi.org/10.1016/j.ajsl.2019.10.001
  • Blashfield, R. K. (1976). Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. Psychological Bulletin, 83(3), 377.
  • Bílgín, E. (2021). Industry 4.0 and sustainable supply chain. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 43(1), 123-144.
  • Bucher, S. (2016). Measuring of Environmental Performance Index in Europe. Rocznik Ochrona Środowiska, 18.
  • 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, 195, 1514–1524.
  • Chen, Y., Mi, Z., Xiao, Z., & Zhang, Y. (2021). COVID-19 Influence: A General Analysis using Machine Learning Methods. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 284–290.
  • Civelek, M. E., Uca, N., & Çemberci, M. (2015). The mediator effect of logistics performance index on the relation between global competitiveness index and gross domestic product. European Scientific Journal May.
  • d'Aleo, V. (2015). The mediator role of Logistic Performance Index: A comparative study. Journal of International Trade, Logistics and Law, 1(1), 1–7.
  • Daugherty, P. J., Ellinger, A. E., & Gustin, C. M. (1996). Integrated logistics: Achieving logistics performance improvements. Supply Chain Management: An International Journal, 1(3), 25–33. https://doi.org/10.1108/13598549610155297
  • Demir, H., Erdoğmuş, P., & Kekeçoğlu, M. (2018). Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(1), 47–67.
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2016). Linking to compete: Logistics and global competitiveness interaction. Transport Policy, 48, 117–128. https://doi.org/10.1016/j.tranpol.2016.01.015
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197–207. https://doi.org/10.1016/j.tranpol.2019.06.014
  • El-Nakib, I., & Elzarka, S. (2014). Measuring supply chain efficiency in MENA countries: A green perspective. Proceeding of theLimcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • 19th Logistics Research Network LRN Annual Conference.
  • Environmental Performance Index 2018. (2022, September 22). 2018 Environmental Performance Index. https://doi.org/10.7927/H4X928CF
  • 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.
  • Guo, X., Ren, D., & Shi, J. (2016). Carbon emissions, logistics volume and GDP in China: Empirical analysis based on panel data model. Environmental Science and Pollution Research, 23(24), 24758–24767.
  • Islam, M. S., Moeinzadeh, S., Tseng, M.-L., & Tan, K. (2021). A literature review on environmental concerns in logistics: Trends and future challenges. International Journal of Logistics Research and Applications, 24(2), 126–151. https://doi.org/10.1080/13675567.2020.1732313
  • Jæger, B., Menebo, M. M., & Upadhyay, A. (2021). Identification of environmental supply chain bottlenecks: A case study of the Ethiopian healthcare supply chain. Management of Environmental Quality: An International Journal, 32(6), 1233–1254. https://doi.org/10.1108/MEQ-12-2019-0277
  • Kabak, Ö., Önsel Ekici, Ş., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238–246. https://doi.org/10.1016/j.tranpol.2019.10.007
  • Kálmán, B., & Tóth, A. (2021). Links between the economy competitiveness and logistics performance in the Visegrád Group countries: Empirical evidence for the years 2007-2018. Entrepreneurial Business and Economics Review, 9(3), 169–190.
  • Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: An empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management.
  • Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
  • Khan, S. A. R. (2019). The nexus between carbon emissions, poverty, economic growth, and logistics operations-empirical evidence from southeast Asian countries. Environmental Science and Pollution Research, 26(13), 13210–13220. https://doi.org/10.1007/s11356-019-04829-4
  • Kim, I., & Min, H. (2011). Measuring supply chain efficiency from a green perspective. Management Research Review, 34(11), 1169–1189.
  • Korinek, J., & Sourdin, P. (2011). To what extent are high-quality logistics services trade facilitating?
  • Larson, P. D., & Halldorsson, A. (2004). Logistics versus supply chain management: An international survey. International Journal of Logistics Research and Applications, 7(1), 17–31. https://doi.org/10.1080/13675560310001619240
  • Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020). Sustainability management and performance in the urban corporate economy: a systematic literature review. Sustainability, 12(18), 7705.
  • Limcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282–291.
  • Lukáč, J., Mihalčová, B., Manová, E., Kozel, R., Vilamova, Š., & Čulková, K. (2020). The position of the Visegrád countries by clustering methods based on indicator environmental performance index. Ekológia, 39(1), 16–26.
  • Ma, E. W., & Chow, T. W. (2004). A new shifting grid clustering algorithm. Pattern Recognition, 37(3), 503–514.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • Mariano, E. B., Gobbo Jr, J. A., de Castro Camioto, F., & do Nascimento Rebelatto, D. A. (2017). CO2 emissions and logistics performance: A composite index proposal. Journal of Cleaner Production, 163, 166–178.
  • Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169–192. https://doi.org/10.1016/S1514-0326(17)30008-9
  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982–2992. https://doi.org/10.1080/00036846.2014.916394
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13–34.
  • Miniak-Górecka, A., Podlaski, K., & Gwizdałła, T. (2022). Using k-means clustering in python with periodic boundary conditions. Symmetry, 14(6), 1237.
  • Nguyen, H. (2021). The role of logistics industry in the sustainable economic development of Southeast Asian countries. Accounting, 7(7), 1681–1688.
  • Nikmah, T. L., Harahap, N. H. S., Utami, G. C., & Razzaq, M. M. (2023). Customer Segmentation Based on Loyalty Level Using K-Means and LRFM Feature Selection in Retail Online Store. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, 7(1), 21-28.
  • Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k Means Clustering algorithm for prediction of Students Academic Performance. ArXiv Preprint ArXiv:1002.2425.
  • Phanich, M., Pholkul, P., & Phimoltares, S. (2010). Food Recommendation System Using Clustering Analysis for Diabetic Patients. 2010 International Conference on Information Science and Applications, 1–8. https://doi.org/10.1109/ICISA.2010.5480416
  • Polat, M., Kara, K., & Yalcin, G. C. (2022). Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis. Business and Economics Research Journal, 13(2), 221–238.
  • Revelle, W. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Behavioral Research, 14(1), 57–74.
  • Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23–32. https://doi.org/10.1016/j.rtbm.2017.10.001
  • Sala-i-Martin, X., Blanke, J., Hanouz, M. D., Geiger, T., Mia, I., & Paua, F. (2007). The global competitiveness index: Measuring the productive potential of nations. The Global Competitiveness Report, 2008, 3–50.
  • Sala-i-Martin, X., Crotti, R., Di Battista, A., Hanouz, M. D., Galvan, C., Geiger, T., & Marti, G. (2015). Reaching beyond the new normal: Findings from the global competitiveness index 2015–2016. The Global Competitiveness Report, 2016(2015), 3–41.
  • Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
  • Standard country or area codes for statistical use (M49). (2023, January 1). Standard Country or Area Codes for Statistical Use. https://unstats.un.org/unsd/methodology/m49/overview/ Statistical Annex-World Economic Situation and Prospects 2022. (2023, January 1).
  • Statistical Annex. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf
  • Taşkın, A. G. D. Ç., & Emel, G. G. (2010). Veri Madenciliğinde Kümeleme Yaklaşimlari Ve Kohonen Ağlari İle Perakendecilik Sektöründe Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 395–409.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
  • Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).
  • Wang, Q.-J., Geng, Y., & Xia, X.-Q. (2021). Revisited Globalization's Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
  • Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121

Tedarik Zinciri Yönetimine İlişkin Göstergeler ile Kümeleme Analizi

Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

Ülkelerin tedarik zinciri performansı, ülkelerin genel performansı üzerinde önemli bir etkiye sahiptir. Çevresel performans ve rekabet gücü, tedarik zinciri performansıyla doğrudan ilişkili olmakla kalmayıp ülkelerin performansını da önemli ölçüde etkileyen önemli özellikler arasında yer almaktadır. Akademik kurumlar ve uluslararası kuruluşlar bu alanlarda çok sayıda tanınmış endeks oluşturtmuşlardır. Bu endeksler öncelikli olarak ülkelerin mevcut sıralamalarını ve geliştirilmesi gereken alanları ortaya koymaktadır. Ülkeleri tek bir göstergeye göre kümelemek her zaman istenen sonuçları vermemektedir. Birçok gösterge değerlendirildiğinde, kritik bilgilere ulaşılmasında küme analizi kullanılabilmektedir. Araştırmanın başlangıç aşamasında, tedarik zinciri ile bağlantılı üç küresel temel endeksler seçilmiştir. Tedarik zinciri yönetiminde önemli bir rol oynayan lojistik sektörünü değerlendirmesinde Lojistik Performans Endeksini kullanılmıştır. Lojistik, bir ülkenin performansını değerlendirmek için kullanılan birçok temel göstergeyi etkileyen ve aynı zamanda bu göstergelerden etkilenen kritik alanlardan biridir. Süreçleri küresel olarak ölçen temel göstergelerin başında, Lojistik Performans Endeksi gelmektedir. Tedarik zinciri operasyonları üzerinde etkisini her geçen gün artıran çevre politikalarını değerlendirilmesi amacıyla, Çevresel Performans Endeksi çalışmaya dâhil edilmiştir. Çalışmada kullanılan son endeks, tedarik zinciri yönetimi performansına büyük ölçüde bağımlı olan ülkelerin rekabet edebilirliğini inceleyen Küresel Rekabet Edebilirlik Endeksi’dir. Bir ülkenin üretkenliğini değerlendirmede en önemli göstergeler arasında gösterilmektedir. Bir sonraki aşamada ise, tedarik zinciri yönetimiyle ilgili göstergelere dayalı kümeleme analizi gerçekleştirilmiştir K-Means kümeleme algoritması çalışmada kullanılmıştır. K-means algoritması, Python programlama dili kullanılarak kodlanmıştır. 2018 yılına ait veri setleri kullanılarak küme analizleri yapılmıştır. Çalışmanın son bölümünde ise kümeler arasındaki farklılıklar ve sunulan araştırma önerileri fikirleri tartışılmıştır. Bu çalışmanın araştırma amacı, göstergelere dayalı olarak merkez noktalardaki değişimi, kümeler arasındaki veri setlerine dayalı değişimi ve her veri seti kombinasyonuna dayalı olarak ülkelerin gruplandırılmasını analiz etmektir.

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Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

References

  • Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics management practices: examining the mediating influences of market, environmental and social performances. Journal of cleaner production, 258, 120613.
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
  • Aylak, B. L. (2022). Impacts of Sustainability on Supply Chain Management. Avrupa Bilim ve Teknoloji Dergisi, (34), 105-109.
  • Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to compete 2018. Trade Logistics in the Global Economy, the Logistics Performance Index and Its Indicators Report (The International Bank for Reconstruction and Development/The World Bank, Washington, DC, 2018).
  • Bazani, C. L., Pereira, J. M., & Leal, E. A. (2020). Logistics Performance Index: What is Brazil's logistics performance in the international market? International Journal of Logistics Systems and Management, 37(1), 38–54.
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 36(1), 34–42. https://doi.org/10.1016/j.ajsl.2019.10.001
  • Blashfield, R. K. (1976). Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. Psychological Bulletin, 83(3), 377.
  • Bílgín, E. (2021). Industry 4.0 and sustainable supply chain. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 43(1), 123-144.
  • Bucher, S. (2016). Measuring of Environmental Performance Index in Europe. Rocznik Ochrona Środowiska, 18.
  • 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, 195, 1514–1524.
  • Chen, Y., Mi, Z., Xiao, Z., & Zhang, Y. (2021). COVID-19 Influence: A General Analysis using Machine Learning Methods. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 284–290.
  • Civelek, M. E., Uca, N., & Çemberci, M. (2015). The mediator effect of logistics performance index on the relation between global competitiveness index and gross domestic product. European Scientific Journal May.
  • d'Aleo, V. (2015). The mediator role of Logistic Performance Index: A comparative study. Journal of International Trade, Logistics and Law, 1(1), 1–7.
  • Daugherty, P. J., Ellinger, A. E., & Gustin, C. M. (1996). Integrated logistics: Achieving logistics performance improvements. Supply Chain Management: An International Journal, 1(3), 25–33. https://doi.org/10.1108/13598549610155297
  • Demir, H., Erdoğmuş, P., & Kekeçoğlu, M. (2018). Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(1), 47–67.
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2016). Linking to compete: Logistics and global competitiveness interaction. Transport Policy, 48, 117–128. https://doi.org/10.1016/j.tranpol.2016.01.015
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197–207. https://doi.org/10.1016/j.tranpol.2019.06.014
  • El-Nakib, I., & Elzarka, S. (2014). Measuring supply chain efficiency in MENA countries: A green perspective. Proceeding of theLimcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • 19th Logistics Research Network LRN Annual Conference.
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  • 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.
  • Guo, X., Ren, D., & Shi, J. (2016). Carbon emissions, logistics volume and GDP in China: Empirical analysis based on panel data model. Environmental Science and Pollution Research, 23(24), 24758–24767.
  • Islam, M. S., Moeinzadeh, S., Tseng, M.-L., & Tan, K. (2021). A literature review on environmental concerns in logistics: Trends and future challenges. International Journal of Logistics Research and Applications, 24(2), 126–151. https://doi.org/10.1080/13675567.2020.1732313
  • Jæger, B., Menebo, M. M., & Upadhyay, A. (2021). Identification of environmental supply chain bottlenecks: A case study of the Ethiopian healthcare supply chain. Management of Environmental Quality: An International Journal, 32(6), 1233–1254. https://doi.org/10.1108/MEQ-12-2019-0277
  • Kabak, Ö., Önsel Ekici, Ş., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238–246. https://doi.org/10.1016/j.tranpol.2019.10.007
  • Kálmán, B., & Tóth, A. (2021). Links between the economy competitiveness and logistics performance in the Visegrád Group countries: Empirical evidence for the years 2007-2018. Entrepreneurial Business and Economics Review, 9(3), 169–190.
  • Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: An empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management.
  • Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
  • Khan, S. A. R. (2019). The nexus between carbon emissions, poverty, economic growth, and logistics operations-empirical evidence from southeast Asian countries. Environmental Science and Pollution Research, 26(13), 13210–13220. https://doi.org/10.1007/s11356-019-04829-4
  • Kim, I., & Min, H. (2011). Measuring supply chain efficiency from a green perspective. Management Research Review, 34(11), 1169–1189.
  • Korinek, J., & Sourdin, P. (2011). To what extent are high-quality logistics services trade facilitating?
  • Larson, P. D., & Halldorsson, A. (2004). Logistics versus supply chain management: An international survey. International Journal of Logistics Research and Applications, 7(1), 17–31. https://doi.org/10.1080/13675560310001619240
  • Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020). Sustainability management and performance in the urban corporate economy: a systematic literature review. Sustainability, 12(18), 7705.
  • Limcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282–291.
  • Lukáč, J., Mihalčová, B., Manová, E., Kozel, R., Vilamova, Š., & Čulková, K. (2020). The position of the Visegrád countries by clustering methods based on indicator environmental performance index. Ekológia, 39(1), 16–26.
  • Ma, E. W., & Chow, T. W. (2004). A new shifting grid clustering algorithm. Pattern Recognition, 37(3), 503–514.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • Mariano, E. B., Gobbo Jr, J. A., de Castro Camioto, F., & do Nascimento Rebelatto, D. A. (2017). CO2 emissions and logistics performance: A composite index proposal. Journal of Cleaner Production, 163, 166–178.
  • Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169–192. https://doi.org/10.1016/S1514-0326(17)30008-9
  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982–2992. https://doi.org/10.1080/00036846.2014.916394
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13–34.
  • Miniak-Górecka, A., Podlaski, K., & Gwizdałła, T. (2022). Using k-means clustering in python with periodic boundary conditions. Symmetry, 14(6), 1237.
  • Nguyen, H. (2021). The role of logistics industry in the sustainable economic development of Southeast Asian countries. Accounting, 7(7), 1681–1688.
  • Nikmah, T. L., Harahap, N. H. S., Utami, G. C., & Razzaq, M. M. (2023). Customer Segmentation Based on Loyalty Level Using K-Means and LRFM Feature Selection in Retail Online Store. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, 7(1), 21-28.
  • Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k Means Clustering algorithm for prediction of Students Academic Performance. ArXiv Preprint ArXiv:1002.2425.
  • Phanich, M., Pholkul, P., & Phimoltares, S. (2010). Food Recommendation System Using Clustering Analysis for Diabetic Patients. 2010 International Conference on Information Science and Applications, 1–8. https://doi.org/10.1109/ICISA.2010.5480416
  • Polat, M., Kara, K., & Yalcin, G. C. (2022). Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis. Business and Economics Research Journal, 13(2), 221–238.
  • Revelle, W. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Behavioral Research, 14(1), 57–74.
  • Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23–32. https://doi.org/10.1016/j.rtbm.2017.10.001
  • Sala-i-Martin, X., Blanke, J., Hanouz, M. D., Geiger, T., Mia, I., & Paua, F. (2007). The global competitiveness index: Measuring the productive potential of nations. The Global Competitiveness Report, 2008, 3–50.
  • Sala-i-Martin, X., Crotti, R., Di Battista, A., Hanouz, M. D., Galvan, C., Geiger, T., & Marti, G. (2015). Reaching beyond the new normal: Findings from the global competitiveness index 2015–2016. The Global Competitiveness Report, 2016(2015), 3–41.
  • Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
  • Standard country or area codes for statistical use (M49). (2023, January 1). Standard Country or Area Codes for Statistical Use. https://unstats.un.org/unsd/methodology/m49/overview/ Statistical Annex-World Economic Situation and Prospects 2022. (2023, January 1).
  • Statistical Annex. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf
  • Taşkın, A. G. D. Ç., & Emel, G. G. (2010). Veri Madenciliğinde Kümeleme Yaklaşimlari Ve Kohonen Ağlari İle Perakendecilik Sektöründe Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 395–409.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
  • Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).
  • Wang, Q.-J., Geng, Y., & Xia, X.-Q. (2021). Revisited Globalization's Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
  • Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121
Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

References

  • Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics management practices: examining the mediating influences of market, environmental and social performances. Journal of cleaner production, 258, 120613.
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
  • Aylak, B. L. (2022). Impacts of Sustainability on Supply Chain Management. Avrupa Bilim ve Teknoloji Dergisi, (34), 105-109.
  • Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to compete 2018. Trade Logistics in the Global Economy, the Logistics Performance Index and Its Indicators Report (The International Bank for Reconstruction and Development/The World Bank, Washington, DC, 2018).
  • Bazani, C. L., Pereira, J. M., & Leal, E. A. (2020). Logistics Performance Index: What is Brazil's logistics performance in the international market? International Journal of Logistics Systems and Management, 37(1), 38–54.
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 36(1), 34–42. https://doi.org/10.1016/j.ajsl.2019.10.001
  • Blashfield, R. K. (1976). Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. Psychological Bulletin, 83(3), 377.
  • Bílgín, E. (2021). Industry 4.0 and sustainable supply chain. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 43(1), 123-144.
  • Bucher, S. (2016). Measuring of Environmental Performance Index in Europe. Rocznik Ochrona Środowiska, 18.
  • 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, 195, 1514–1524.
  • Chen, Y., Mi, Z., Xiao, Z., & Zhang, Y. (2021). COVID-19 Influence: A General Analysis using Machine Learning Methods. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 284–290.
  • Civelek, M. E., Uca, N., & Çemberci, M. (2015). The mediator effect of logistics performance index on the relation between global competitiveness index and gross domestic product. European Scientific Journal May.
  • d'Aleo, V. (2015). The mediator role of Logistic Performance Index: A comparative study. Journal of International Trade, Logistics and Law, 1(1), 1–7.
  • Daugherty, P. J., Ellinger, A. E., & Gustin, C. M. (1996). Integrated logistics: Achieving logistics performance improvements. Supply Chain Management: An International Journal, 1(3), 25–33. https://doi.org/10.1108/13598549610155297
  • Demir, H., Erdoğmuş, P., & Kekeçoğlu, M. (2018). Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(1), 47–67.
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2016). Linking to compete: Logistics and global competitiveness interaction. Transport Policy, 48, 117–128. https://doi.org/10.1016/j.tranpol.2016.01.015
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197–207. https://doi.org/10.1016/j.tranpol.2019.06.014
  • El-Nakib, I., & Elzarka, S. (2014). Measuring supply chain efficiency in MENA countries: A green perspective. Proceeding of theLimcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • 19th Logistics Research Network LRN Annual Conference.
  • Environmental Performance Index 2018. (2022, September 22). 2018 Environmental Performance Index. https://doi.org/10.7927/H4X928CF
  • 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.
  • Guo, X., Ren, D., & Shi, J. (2016). Carbon emissions, logistics volume and GDP in China: Empirical analysis based on panel data model. Environmental Science and Pollution Research, 23(24), 24758–24767.
  • Islam, M. S., Moeinzadeh, S., Tseng, M.-L., & Tan, K. (2021). A literature review on environmental concerns in logistics: Trends and future challenges. International Journal of Logistics Research and Applications, 24(2), 126–151. https://doi.org/10.1080/13675567.2020.1732313
  • Jæger, B., Menebo, M. M., & Upadhyay, A. (2021). Identification of environmental supply chain bottlenecks: A case study of the Ethiopian healthcare supply chain. Management of Environmental Quality: An International Journal, 32(6), 1233–1254. https://doi.org/10.1108/MEQ-12-2019-0277
  • Kabak, Ö., Önsel Ekici, Ş., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238–246. https://doi.org/10.1016/j.tranpol.2019.10.007
  • Kálmán, B., & Tóth, A. (2021). Links between the economy competitiveness and logistics performance in the Visegrád Group countries: Empirical evidence for the years 2007-2018. Entrepreneurial Business and Economics Review, 9(3), 169–190.
  • Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: An empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management.
  • Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
  • Khan, S. A. R. (2019). The nexus between carbon emissions, poverty, economic growth, and logistics operations-empirical evidence from southeast Asian countries. Environmental Science and Pollution Research, 26(13), 13210–13220. https://doi.org/10.1007/s11356-019-04829-4
  • Kim, I., & Min, H. (2011). Measuring supply chain efficiency from a green perspective. Management Research Review, 34(11), 1169–1189.
  • Korinek, J., & Sourdin, P. (2011). To what extent are high-quality logistics services trade facilitating?
  • Larson, P. D., & Halldorsson, A. (2004). Logistics versus supply chain management: An international survey. International Journal of Logistics Research and Applications, 7(1), 17–31. https://doi.org/10.1080/13675560310001619240
  • Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020). Sustainability management and performance in the urban corporate economy: a systematic literature review. Sustainability, 12(18), 7705.
  • Limcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282–291.
  • Lukáč, J., Mihalčová, B., Manová, E., Kozel, R., Vilamova, Š., & Čulková, K. (2020). The position of the Visegrád countries by clustering methods based on indicator environmental performance index. Ekológia, 39(1), 16–26.
  • Ma, E. W., & Chow, T. W. (2004). A new shifting grid clustering algorithm. Pattern Recognition, 37(3), 503–514.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • Mariano, E. B., Gobbo Jr, J. A., de Castro Camioto, F., & do Nascimento Rebelatto, D. A. (2017). CO2 emissions and logistics performance: A composite index proposal. Journal of Cleaner Production, 163, 166–178.
  • Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169–192. https://doi.org/10.1016/S1514-0326(17)30008-9
  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982–2992. https://doi.org/10.1080/00036846.2014.916394
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13–34.
  • Miniak-Górecka, A., Podlaski, K., & Gwizdałła, T. (2022). Using k-means clustering in python with periodic boundary conditions. Symmetry, 14(6), 1237.
  • Nguyen, H. (2021). The role of logistics industry in the sustainable economic development of Southeast Asian countries. Accounting, 7(7), 1681–1688.
  • Nikmah, T. L., Harahap, N. H. S., Utami, G. C., & Razzaq, M. M. (2023). Customer Segmentation Based on Loyalty Level Using K-Means and LRFM Feature Selection in Retail Online Store. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, 7(1), 21-28.
  • Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k Means Clustering algorithm for prediction of Students Academic Performance. ArXiv Preprint ArXiv:1002.2425.
  • Phanich, M., Pholkul, P., & Phimoltares, S. (2010). Food Recommendation System Using Clustering Analysis for Diabetic Patients. 2010 International Conference on Information Science and Applications, 1–8. https://doi.org/10.1109/ICISA.2010.5480416
  • Polat, M., Kara, K., & Yalcin, G. C. (2022). Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis. Business and Economics Research Journal, 13(2), 221–238.
  • Revelle, W. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Behavioral Research, 14(1), 57–74.
  • Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23–32. https://doi.org/10.1016/j.rtbm.2017.10.001
  • Sala-i-Martin, X., Blanke, J., Hanouz, M. D., Geiger, T., Mia, I., & Paua, F. (2007). The global competitiveness index: Measuring the productive potential of nations. The Global Competitiveness Report, 2008, 3–50.
  • Sala-i-Martin, X., Crotti, R., Di Battista, A., Hanouz, M. D., Galvan, C., Geiger, T., & Marti, G. (2015). Reaching beyond the new normal: Findings from the global competitiveness index 2015–2016. The Global Competitiveness Report, 2016(2015), 3–41.
  • Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
  • Standard country or area codes for statistical use (M49). (2023, January 1). Standard Country or Area Codes for Statistical Use. https://unstats.un.org/unsd/methodology/m49/overview/ Statistical Annex-World Economic Situation and Prospects 2022. (2023, January 1).
  • Statistical Annex. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf
  • Taşkın, A. G. D. Ç., & Emel, G. G. (2010). Veri Madenciliğinde Kümeleme Yaklaşimlari Ve Kohonen Ağlari İle Perakendecilik Sektöründe Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 395–409.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
  • Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).
  • Wang, Q.-J., Geng, Y., & Xia, X.-Q. (2021). Revisited Globalization's Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
  • Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121
Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

References

  • Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics management practices: examining the mediating influences of market, environmental and social performances. Journal of cleaner production, 258, 120613.
  • Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
  • Aylak, B. L. (2022). Impacts of Sustainability on Supply Chain Management. Avrupa Bilim ve Teknoloji Dergisi, (34), 105-109.
  • Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., Raj, A., Dairabayeva, K., & Kiiski, T. (2018). Connecting to compete 2018. Trade Logistics in the Global Economy, the Logistics Performance Index and Its Indicators Report (The International Bank for Reconstruction and Development/The World Bank, Washington, DC, 2018).
  • Bazani, C. L., Pereira, J. M., & Leal, E. A. (2020). Logistics Performance Index: What is Brazil's logistics performance in the international market? International Journal of Logistics Systems and Management, 37(1), 38–54.
  • Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 36(1), 34–42. https://doi.org/10.1016/j.ajsl.2019.10.001
  • Blashfield, R. K. (1976). Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. Psychological Bulletin, 83(3), 377.
  • Bílgín, E. (2021). Industry 4.0 and sustainable supply chain. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 43(1), 123-144.
  • Bucher, S. (2016). Measuring of Environmental Performance Index in Europe. Rocznik Ochrona Środowiska, 18.
  • 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, 195, 1514–1524.
  • Chen, Y., Mi, Z., Xiao, Z., & Zhang, Y. (2021). COVID-19 Influence: A General Analysis using Machine Learning Methods. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 284–290.
  • Civelek, M. E., Uca, N., & Çemberci, M. (2015). The mediator effect of logistics performance index on the relation between global competitiveness index and gross domestic product. European Scientific Journal May.
  • d'Aleo, V. (2015). The mediator role of Logistic Performance Index: A comparative study. Journal of International Trade, Logistics and Law, 1(1), 1–7.
  • Daugherty, P. J., Ellinger, A. E., & Gustin, C. M. (1996). Integrated logistics: Achieving logistics performance improvements. Supply Chain Management: An International Journal, 1(3), 25–33. https://doi.org/10.1108/13598549610155297
  • Demir, H., Erdoğmuş, P., & Kekeçoğlu, M. (2018). Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(1), 47–67.
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2016). Linking to compete: Logistics and global competitiveness interaction. Transport Policy, 48, 117–128. https://doi.org/10.1016/j.tranpol.2016.01.015
  • Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197–207. https://doi.org/10.1016/j.tranpol.2019.06.014
  • El-Nakib, I., & Elzarka, S. (2014). Measuring supply chain efficiency in MENA countries: A green perspective. Proceeding of theLimcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • 19th Logistics Research Network LRN Annual Conference.
  • Environmental Performance Index 2018. (2022, September 22). 2018 Environmental Performance Index. https://doi.org/10.7927/H4X928CF
  • 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.
  • Guo, X., Ren, D., & Shi, J. (2016). Carbon emissions, logistics volume and GDP in China: Empirical analysis based on panel data model. Environmental Science and Pollution Research, 23(24), 24758–24767.
  • Islam, M. S., Moeinzadeh, S., Tseng, M.-L., & Tan, K. (2021). A literature review on environmental concerns in logistics: Trends and future challenges. International Journal of Logistics Research and Applications, 24(2), 126–151. https://doi.org/10.1080/13675567.2020.1732313
  • Jæger, B., Menebo, M. M., & Upadhyay, A. (2021). Identification of environmental supply chain bottlenecks: A case study of the Ethiopian healthcare supply chain. Management of Environmental Quality: An International Journal, 32(6), 1233–1254. https://doi.org/10.1108/MEQ-12-2019-0277
  • Kabak, Ö., Önsel Ekici, Ş., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238–246. https://doi.org/10.1016/j.tranpol.2019.10.007
  • Kálmán, B., & Tóth, A. (2021). Links between the economy competitiveness and logistics performance in the Visegrád Group countries: Empirical evidence for the years 2007-2018. Entrepreneurial Business and Economics Review, 9(3), 169–190.
  • Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: An empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management.
  • Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
  • Khan, S. A. R. (2019). The nexus between carbon emissions, poverty, economic growth, and logistics operations-empirical evidence from southeast Asian countries. Environmental Science and Pollution Research, 26(13), 13210–13220. https://doi.org/10.1007/s11356-019-04829-4
  • Kim, I., & Min, H. (2011). Measuring supply chain efficiency from a green perspective. Management Research Review, 34(11), 1169–1189.
  • Korinek, J., & Sourdin, P. (2011). To what extent are high-quality logistics services trade facilitating?
  • Larson, P. D., & Halldorsson, A. (2004). Logistics versus supply chain management: An international survey. International Journal of Logistics Research and Applications, 7(1), 17–31. https://doi.org/10.1080/13675560310001619240
  • Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020). Sustainability management and performance in the urban corporate economy: a systematic literature review. Sustainability, 12(18), 7705.
  • Limcharoen, A., Jangkrajarng, V., Wisittipanich, W., & Ramingwong, S. (2017). Thailand logistics trend: Logistics performance index. International Journal of Applied Engineering Research, 12(15), 4882–4885.
  • Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282–291.
  • Lukáč, J., Mihalčová, B., Manová, E., Kozel, R., Vilamova, Š., & Čulková, K. (2020). The position of the Visegrád countries by clustering methods based on indicator environmental performance index. Ekológia, 39(1), 16–26.
  • Ma, E. W., & Chow, T. W. (2004). A new shifting grid clustering algorithm. Pattern Recognition, 37(3), 503–514.
  • Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.
  • Mariano, E. B., Gobbo Jr, J. A., de Castro Camioto, F., & do Nascimento Rebelatto, D. A. (2017). CO2 emissions and logistics performance: A composite index proposal. Journal of Cleaner Production, 163, 166–178.
  • Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169–192. https://doi.org/10.1016/S1514-0326(17)30008-9
  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982–2992. https://doi.org/10.1080/00036846.2014.916394
  • Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13–34.
  • Miniak-Górecka, A., Podlaski, K., & Gwizdałła, T. (2022). Using k-means clustering in python with periodic boundary conditions. Symmetry, 14(6), 1237.
  • Nguyen, H. (2021). The role of logistics industry in the sustainable economic development of Southeast Asian countries. Accounting, 7(7), 1681–1688.
  • Nikmah, T. L., Harahap, N. H. S., Utami, G. C., & Razzaq, M. M. (2023). Customer Segmentation Based on Loyalty Level Using K-Means and LRFM Feature Selection in Retail Online Store. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, 7(1), 21-28.
  • Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k Means Clustering algorithm for prediction of Students Academic Performance. ArXiv Preprint ArXiv:1002.2425.
  • Phanich, M., Pholkul, P., & Phimoltares, S. (2010). Food Recommendation System Using Clustering Analysis for Diabetic Patients. 2010 International Conference on Information Science and Applications, 1–8. https://doi.org/10.1109/ICISA.2010.5480416
  • Polat, M., Kara, K., & Yalcin, G. C. (2022). Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis. Business and Economics Research Journal, 13(2), 221–238.
  • Revelle, W. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Behavioral Research, 14(1), 57–74.
  • Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23–32. https://doi.org/10.1016/j.rtbm.2017.10.001
  • Sala-i-Martin, X., Blanke, J., Hanouz, M. D., Geiger, T., Mia, I., & Paua, F. (2007). The global competitiveness index: Measuring the productive potential of nations. The Global Competitiveness Report, 2008, 3–50.
  • Sala-i-Martin, X., Crotti, R., Di Battista, A., Hanouz, M. D., Galvan, C., Geiger, T., & Marti, G. (2015). Reaching beyond the new normal: Findings from the global competitiveness index 2015–2016. The Global Competitiveness Report, 2016(2015), 3–41.
  • Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
  • Standard country or area codes for statistical use (M49). (2023, January 1). Standard Country or Area Codes for Statistical Use. https://unstats.un.org/unsd/methodology/m49/overview/ Statistical Annex-World Economic Situation and Prospects 2022. (2023, January 1).
  • Statistical Annex. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf
  • Taşkın, A. G. D. Ç., & Emel, G. G. (2010). Veri Madenciliğinde Kümeleme Yaklaşimlari Ve Kohonen Ağlari İle Perakendecilik Sektöründe Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 395–409.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
  • Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).
  • Wang, Q.-J., Geng, Y., & Xia, X.-Q. (2021). Revisited Globalization's Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
  • Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121
Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023
https://doi.org/10.15869/itobiad.1251841

Abstract

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  • Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23–32. https://doi.org/10.1016/j.rtbm.2017.10.001
  • Sala-i-Martin, X., Blanke, J., Hanouz, M. D., Geiger, T., Mia, I., & Paua, F. (2007). The global competitiveness index: Measuring the productive potential of nations. The Global Competitiveness Report, 2008, 3–50.
  • Sala-i-Martin, X., Crotti, R., Di Battista, A., Hanouz, M. D., Galvan, C., Geiger, T., & Marti, G. (2015). Reaching beyond the new normal: Findings from the global competitiveness index 2015–2016. The Global Competitiveness Report, 2016(2015), 3–41.
  • Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
  • Standard country or area codes for statistical use (M49). (2023, January 1). Standard Country or Area Codes for Statistical Use. https://unstats.un.org/unsd/methodology/m49/overview/ Statistical Annex-World Economic Situation and Prospects 2022. (2023, January 1).
  • Statistical Annex. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf
  • Taşkın, A. G. D. Ç., & Emel, G. G. (2010). Veri Madenciliğinde Kümeleme Yaklaşimlari Ve Kohonen Ağlari İle Perakendecilik Sektöründe Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 395–409.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
  • Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).
  • Wang, Q.-J., Geng, Y., & Xia, X.-Q. (2021). Revisited Globalization's Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
  • Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121
There are 61 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Metin Yıldırım 0000-0003-0424-9834

Early Pub Date December 7, 2023
Publication Date December 31, 2023
Published in Issue Year 2023 Volume: 12 Issue: 5

Cite

APA Yıldırım, M. (2023). Cluster Analysis on Supply Chain Management-Related Indicators. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 12(5), 2499-2520. https://doi.org/10.15869/itobiad.1251841

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