Humaira Nafisa Ahmed, Zannatul Maoua, Sayem Ahmed, Syed Mithun Ali
{"title":"Humanitarian Relief Supply Chain Performance Measurement: A Framework and Validation","authors":"Humaira Nafisa Ahmed, Zannatul Maoua, Sayem Ahmed, Syed Mithun Ali","doi":"10.1016/j.jii.2025.100898","DOIUrl":null,"url":null,"abstract":"The concept of humanitarian relief supply chain management has gained a lot of interest among academics and practitioners since the number of natural or human-made disasters has increased drastically. Humanitarian organizations assist in disaster relief operations by planning, sourcing, procuring, transporting, and distributing essential goods and services during emergency operations, known as the humanitarian relief supply chain. This research develops a Bayesian belief network-based framework for predicting the performance of the humanitarian relief supply chain in case of catastrophic events, such as natural disasters and man-made crises. The study begins with identifying performance metrics through factor analysis that directly or indirectly affect the overall performance of a humanitarian organization. Then, with the aid of a Bayesian belief network, a probabilistic graphical model capable of predicting any organization's relief supply chain based on performance metrics was developed. The model demonstrates the interdependencies among the performance metrics within a network setting. The network is constructed through mediating variables by establishing causal relationships among performance metrics and mediating variables. The model has been validated through numerical examples, extreme condition testing, scenario analysis, sensitivity analysis, and diagnostics analysis. Extreme condition tests, diagnostic, and scenario analysis validate the model as reliable and stable. The sensitivity analysis result shows financial performance and monetary support as crucial factors in measuring the performance of the humanitarian relief supply chain. The performance measurement model will assist organizations' decision-makers and policymakers in controlling, monitoring, and enhancing their relief supply chain.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"2 1","pages":""},"PeriodicalIF":10.4000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial Information Integration","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.jii.2025.100898","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The concept of humanitarian relief supply chain management has gained a lot of interest among academics and practitioners since the number of natural or human-made disasters has increased drastically. Humanitarian organizations assist in disaster relief operations by planning, sourcing, procuring, transporting, and distributing essential goods and services during emergency operations, known as the humanitarian relief supply chain. This research develops a Bayesian belief network-based framework for predicting the performance of the humanitarian relief supply chain in case of catastrophic events, such as natural disasters and man-made crises. The study begins with identifying performance metrics through factor analysis that directly or indirectly affect the overall performance of a humanitarian organization. Then, with the aid of a Bayesian belief network, a probabilistic graphical model capable of predicting any organization's relief supply chain based on performance metrics was developed. The model demonstrates the interdependencies among the performance metrics within a network setting. The network is constructed through mediating variables by establishing causal relationships among performance metrics and mediating variables. The model has been validated through numerical examples, extreme condition testing, scenario analysis, sensitivity analysis, and diagnostics analysis. Extreme condition tests, diagnostic, and scenario analysis validate the model as reliable and stable. The sensitivity analysis result shows financial performance and monetary support as crucial factors in measuring the performance of the humanitarian relief supply chain. The performance measurement model will assist organizations' decision-makers and policymakers in controlling, monitoring, and enhancing their relief supply chain.
期刊介绍:
The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers.
The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.