Exploring temporal dependencies among country-level logistics performance indicators

Abroon Qazi, M. Al-Mhdawi, M. Simsekler
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Abstract

PurposeThe Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).Design/methodology/approachA Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.FindingsThe prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.Originality/valueThis paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.
探索国家一级物流绩效指标之间的时间依赖性
目的 世界银行发布的物流绩效指数(LPI)是衡量国家层面物流绩效的一个关键指标。它包括六个指标:海关、基础设施、国际货运、服务质量、及时性以及跟踪和追踪。本研究的目的是探索六项 LPI 指标之间的时间依赖关系,同时将世界银行的 LPI 框架操作化,将输入指标(海关、基础设施和服务质量)映射到结果指标(代表成本的国际货运、代表可靠性的及时性和代表可靠性的跟踪和追踪)。利用贝叶斯信念网络推理的前向和后向传播特征,还确定了关键变量。利用世界银行 2010 年、2012 年、2014 年、2016 年、2018 年和 2023 年的 LPI 数据集开发了 BBN 模型,涵盖 118 个国家的六项 LPI 指标。该模型的预测准确率为 88.1%,发现随着时间的推移,六项 LPI 指标之间存在很强的依赖关系。模型的前向传播分析表明,"物流能力和质量 "是最关键的输入指标,会随着时间的推移影响所有三个结果指标。后向传播分析表明,"海关 "是提高 "国际货运 "指标绩效的最关键指标,而 "物流能力和质量 "则能显著提高 "及时性 "和 "跟踪与追踪 "指标的绩效。对模型的敏感性分析表明,"物流能力与质量 "和 "基础设施 "是影响三个结果指标结果的关键指标。这些发现为研究人员提供了有益的启示,使他们认识到探索 LPI 指标间依赖关系的时间模型的重要性。此外,政策制定者也可以利用这些发现来帮助他们的国家瞄准特定的投入指标,以改善国家层面的物流绩效。 本文通过探讨过去 14 年 118 个国家的六项物流绩效指标之间的时间依赖关系,为物流管理方面的文献做出了贡献。此外,本文还在这一独特背景下提出了数据驱动的 BBN 建模方法,并将其付诸实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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