{"title":"Supply chain collaborative forecasting methods on the basis of factors","authors":"T. Shu, Shou Chen, Shouyang Wang, K. Lai","doi":"10.1109/ICMIT.2008.4654571","DOIUrl":null,"url":null,"abstract":"This paper proposes the supply chain collaborative forecasting methods on the basis of factors and presents the relevant empirical studies. In the light of the past actual sales data, factors of Spring Festival transportation, shutting down for examinations and repairs and minor repairs are extracted and quantified in different hierarchies and domains. At the same time, they are reverted in the corporate sales forecasting. The empirical studies indicate that factors play an important part in supply chain sales forecasting. Their application can greatly improve the specific and general forecasting accuracy and represents the thought of collaborative forecasting. They can contribute to the supply chain implication and prominent information application; they can contribute to positively employing the potential negative constraints of supply chain enterprises; they can contribute to the management of supply chain as the information whole. All these can be considered as an extension of the economic information filter in different hierarchies and modules.","PeriodicalId":332967,"journal":{"name":"2008 4th IEEE International Conference on Management of Innovation and Technology","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th IEEE International Conference on Management of Innovation and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMIT.2008.4654571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes the supply chain collaborative forecasting methods on the basis of factors and presents the relevant empirical studies. In the light of the past actual sales data, factors of Spring Festival transportation, shutting down for examinations and repairs and minor repairs are extracted and quantified in different hierarchies and domains. At the same time, they are reverted in the corporate sales forecasting. The empirical studies indicate that factors play an important part in supply chain sales forecasting. Their application can greatly improve the specific and general forecasting accuracy and represents the thought of collaborative forecasting. They can contribute to the supply chain implication and prominent information application; they can contribute to positively employing the potential negative constraints of supply chain enterprises; they can contribute to the management of supply chain as the information whole. All these can be considered as an extension of the economic information filter in different hierarchies and modules.