供应链管理相关指标的聚类分析

Metin Yildirim
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引用次数: 0

摘要

各国的供应链绩效对各国的整体绩效有着重要影响。这些指数主要强调各国的排名、名次和改进领域。根据单一指标对国家进行聚类分析并不总能获得理想的结果。在对许多指标进行评估时,使用聚类分析可能有助于获得关键信息。研究最初选择了与供应链相关的指标。本研究选择了三个全球指数。我们选择了物流绩效指数(LPI)来评估供应链管理中至关重要的物流业。物流业是影响许多用于评估国家绩效的基本指标的关键领域之一。在全球范围内衡量物流过程的一个重要指标就是物流绩效指数。我们在研究中纳入了环境绩效指数(EPI),以评估影响供应链运营的环境政策。研究中使用的最后一个指数是全球竞争力指数(GCI),该指数用于考察严重依赖供应链管理绩效的国家的竞争力。它是评估一个国家生产力的重要指标之一。在下一阶段,我们采用了基于供应链管理相关指标的聚类分析。对提取的数据集采用 K-Means 聚类算法。我们编写了 Python 代码来实现 K-Means 聚类算法。在研究的最后部分,讨论了聚类与提交的研究提案想法之间的差异。本研究提出了一个三步方法框架,用于挖掘从 LPI、GCI 和 EPI 指标中得出的供应链指标。研究旨在通过分析基于指标的中心变化、基于集群间数据集的差异以及基于 LPI、GCI 和 EPI 指标任意组合的国家分组得出结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cluster Analysis on Supply Chain Management-Related Indicators
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 .
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