G. Iqbal, Matthew Rosenberger, Lidan Ha, S. Gregory, E. Anoruo
{"title":"供应链中产品缺货概率预测的分类与回归树模型","authors":"G. Iqbal, Matthew Rosenberger, Lidan Ha, S. Gregory, E. Anoruo","doi":"10.59160/ijscm.v12i4.6199","DOIUrl":null,"url":null,"abstract":"Supply chain uncertainties pose a massive and ever-present challenge for modern companies. These uncertainties can manifest in two contrasting scenarios: supply surplus, where companies have excess items, and supply shortages, where there is an insufficient quantity of goods. Each situation demands a different approach from businesses to adapt to the varying outcomes and maintain a competitive edge in the market. Product backordering is one of the important things that companies need to deal with in an uncertain supply chain. A backorder occurs when a customer-ordered product or service is not in stock or cannot be supplied immediately, and the customer has to wait. Companies striving for a balance in managing backorders. Machine learning models can help to determine the probability of a product being backordered. In this research, we develop Classification and Regression Tree (CART) model that uses previously known parameters to predict the likelihood of a product being backordered. We also use different model parameters to evaluate the accuracy of the model.","PeriodicalId":37872,"journal":{"name":"International Journal of Construction Supply Chain Management","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification and Regression Tree Model to Predict the Probability of a Product being Backordered in Supply Chain\",\"authors\":\"G. Iqbal, Matthew Rosenberger, Lidan Ha, S. Gregory, E. Anoruo\",\"doi\":\"10.59160/ijscm.v12i4.6199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Supply chain uncertainties pose a massive and ever-present challenge for modern companies. These uncertainties can manifest in two contrasting scenarios: supply surplus, where companies have excess items, and supply shortages, where there is an insufficient quantity of goods. Each situation demands a different approach from businesses to adapt to the varying outcomes and maintain a competitive edge in the market. Product backordering is one of the important things that companies need to deal with in an uncertain supply chain. A backorder occurs when a customer-ordered product or service is not in stock or cannot be supplied immediately, and the customer has to wait. Companies striving for a balance in managing backorders. Machine learning models can help to determine the probability of a product being backordered. In this research, we develop Classification and Regression Tree (CART) model that uses previously known parameters to predict the likelihood of a product being backordered. We also use different model parameters to evaluate the accuracy of the model.\",\"PeriodicalId\":37872,\"journal\":{\"name\":\"International Journal of Construction Supply Chain Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Construction Supply Chain Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.59160/ijscm.v12i4.6199\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Construction Supply Chain Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59160/ijscm.v12i4.6199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Classification and Regression Tree Model to Predict the Probability of a Product being Backordered in Supply Chain
Supply chain uncertainties pose a massive and ever-present challenge for modern companies. These uncertainties can manifest in two contrasting scenarios: supply surplus, where companies have excess items, and supply shortages, where there is an insufficient quantity of goods. Each situation demands a different approach from businesses to adapt to the varying outcomes and maintain a competitive edge in the market. Product backordering is one of the important things that companies need to deal with in an uncertain supply chain. A backorder occurs when a customer-ordered product or service is not in stock or cannot be supplied immediately, and the customer has to wait. Companies striving for a balance in managing backorders. Machine learning models can help to determine the probability of a product being backordered. In this research, we develop Classification and Regression Tree (CART) model that uses previously known parameters to predict the likelihood of a product being backordered. We also use different model parameters to evaluate the accuracy of the model.