Cluster Analysis on Supply Chain Management-Related Indicators

Year 2023, Volume: 12 Issue: 5, 2499 - 2520, 31.12.2023

Metin Yıldırım

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 .

Keywords

Cluster Analysis, K-Means, Logistics Performance Index, Environmental Performance Index, Global Competitiveness Index

Tedarik Zinciri Yönetimine İlişkin Göstergeler ile Kümeleme Analizi

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.

Keywords

Kümeleme Analizi, K-Means, Lojistik Performans Endeksi, Çevresel Performans Endeksi, Küresel Rekabet Edebilirlik Endeksi

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