{"title":"基于信念网络的供给风险建模:一个应用于药品配送的过程","authors":"K. Leerojanaprapa, R. van der Meer, L. Walls","doi":"10.1109/IEEM.2013.6962403","DOIUrl":null,"url":null,"abstract":"We propose a modeling approach based on belief networks to capture and understand the systemic nature of risks affecting supply networks. By aligning the purpose of a model with the nature of supply management decisions, we provide a mechanism for identifying relevant supply risks so that we can visualize inter-dependencies between risks and predict their effects on supply performance. By using a belief network modeling formalism we can use diagnostics to understand the key drivers of unwanted risk scenarios and to explore the efficacy of possible risk mitigating actions. We illustrate how belief network modeling can be used to manage the risk/reward position and provide new insights into supply risks through an example for the medicine supply chain of a regional health service provider.","PeriodicalId":6454,"journal":{"name":"2013 IEEE International Conference on Industrial Engineering and Engineering Management","volume":"93 1","pages":"201-205"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Modeling supply risk using belief networks: A process with application to the distribution of medicine\",\"authors\":\"K. Leerojanaprapa, R. van der Meer, L. Walls\",\"doi\":\"10.1109/IEEM.2013.6962403\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a modeling approach based on belief networks to capture and understand the systemic nature of risks affecting supply networks. By aligning the purpose of a model with the nature of supply management decisions, we provide a mechanism for identifying relevant supply risks so that we can visualize inter-dependencies between risks and predict their effects on supply performance. By using a belief network modeling formalism we can use diagnostics to understand the key drivers of unwanted risk scenarios and to explore the efficacy of possible risk mitigating actions. We illustrate how belief network modeling can be used to manage the risk/reward position and provide new insights into supply risks through an example for the medicine supply chain of a regional health service provider.\",\"PeriodicalId\":6454,\"journal\":{\"name\":\"2013 IEEE International Conference on Industrial Engineering and Engineering Management\",\"volume\":\"93 1\",\"pages\":\"201-205\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Industrial Engineering and Engineering Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEM.2013.6962403\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Industrial Engineering and Engineering Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM.2013.6962403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling supply risk using belief networks: A process with application to the distribution of medicine
We propose a modeling approach based on belief networks to capture and understand the systemic nature of risks affecting supply networks. By aligning the purpose of a model with the nature of supply management decisions, we provide a mechanism for identifying relevant supply risks so that we can visualize inter-dependencies between risks and predict their effects on supply performance. By using a belief network modeling formalism we can use diagnostics to understand the key drivers of unwanted risk scenarios and to explore the efficacy of possible risk mitigating actions. We illustrate how belief network modeling can be used to manage the risk/reward position and provide new insights into supply risks through an example for the medicine supply chain of a regional health service provider.