{"title":"供应链中断模型:批判性回顾","authors":"Niels Bugert, R. Lasch","doi":"10.23773/2018_5","DOIUrl":null,"url":null,"abstract":"Enterprises affected by supply chain disruptions have reported adverse consequences and dramatic financial losses. Within the research area of supply chain risk management, researchers use simulation models and algorithms to analyze disruption risks and their potential effects on the supply chain. Supply chain disruption risk models focus on ways to quantify and assess disruption risks, study interdependencies between them, and explore the dynamic behavior of risks as they propagate through the network. So far, no review has covered and evaluated quantitative decision models which focus on these specific network-related risk characteristics. This paper derives a definition for supply chain disruption risk models and analyzes existing approaches on the basis of requirements derived from the literature. Its aims are to structure existing approaches, reveal their shortcomings, and guide future research efforts to improve prospective models systematically. This analysis reveals potential improvements regarding the simultaneous integration of dynamic and interdependent aspects of disruption risks in the supply chain model as well as their propagation through the network. More process steps of a supply chain risk management framework should be supported and more mitigation strategies should be incorporated to expand the scope and usefulness of the models.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":"3 1","pages":"5"},"PeriodicalIF":2.1000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Supply Chain Disruption Models: A Critical Review\",\"authors\":\"Niels Bugert, R. Lasch\",\"doi\":\"10.23773/2018_5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Enterprises affected by supply chain disruptions have reported adverse consequences and dramatic financial losses. Within the research area of supply chain risk management, researchers use simulation models and algorithms to analyze disruption risks and their potential effects on the supply chain. Supply chain disruption risk models focus on ways to quantify and assess disruption risks, study interdependencies between them, and explore the dynamic behavior of risks as they propagate through the network. So far, no review has covered and evaluated quantitative decision models which focus on these specific network-related risk characteristics. This paper derives a definition for supply chain disruption risk models and analyzes existing approaches on the basis of requirements derived from the literature. Its aims are to structure existing approaches, reveal their shortcomings, and guide future research efforts to improve prospective models systematically. This analysis reveals potential improvements regarding the simultaneous integration of dynamic and interdependent aspects of disruption risks in the supply chain model as well as their propagation through the network. More process steps of a supply chain risk management framework should be supported and more mitigation strategies should be incorporated to expand the scope and usefulness of the models.\",\"PeriodicalId\":49772,\"journal\":{\"name\":\"Naval Research Logistics\",\"volume\":\"3 1\",\"pages\":\"5\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Naval Research Logistics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.23773/2018_5\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Naval Research Logistics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.23773/2018_5","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Enterprises affected by supply chain disruptions have reported adverse consequences and dramatic financial losses. Within the research area of supply chain risk management, researchers use simulation models and algorithms to analyze disruption risks and their potential effects on the supply chain. Supply chain disruption risk models focus on ways to quantify and assess disruption risks, study interdependencies between them, and explore the dynamic behavior of risks as they propagate through the network. So far, no review has covered and evaluated quantitative decision models which focus on these specific network-related risk characteristics. This paper derives a definition for supply chain disruption risk models and analyzes existing approaches on the basis of requirements derived from the literature. Its aims are to structure existing approaches, reveal their shortcomings, and guide future research efforts to improve prospective models systematically. This analysis reveals potential improvements regarding the simultaneous integration of dynamic and interdependent aspects of disruption risks in the supply chain model as well as their propagation through the network. More process steps of a supply chain risk management framework should be supported and more mitigation strategies should be incorporated to expand the scope and usefulness of the models.
期刊介绍:
Submissions that are most appropriate for NRL are papers addressing modeling and analysis of problems motivated by real-world applications; major methodological advances in operations research and applied statistics; and expository or survey pieces of lasting value. Areas represented include (but are not limited to) probability, statistics, simulation, optimization, game theory, quality, scheduling, reliability, maintenance, supply chain, decision analysis, and combat models. Special issues devoted to a single topic are published occasionally, and proposals for special issues are welcomed by the Editorial Board.