Francielly Hedler Staudt, M. B. Gonçalves, C. Rodriguez
{"title":"部署包含专家判断的需求预测模型的程序","authors":"Francielly Hedler Staudt, M. B. Gonçalves, C. Rodriguez","doi":"10.1590/0103-6513.054612","DOIUrl":null,"url":null,"abstract":"When marketing information is well interpreted and incorporated into a quantitative forecast by an expert, forecast accuracy may be enhanced. However, human judgment might introduce biases into the forecast. One way to avoid these biases is to use structured adjustment approaches. This article presents a procedure to help companies implement a demand forecasting system with a judgmental adjustment of statistical forecasts. The use of this procedure in a small company shows its implementation. The results demonstrated that judgmental adjustments improved quantitative forecast accuracy by an average of 5%. The results also showed that the product with the greatest variability in a time series had the best adjustment performance and that the best outcomes came from the larger adjustments.","PeriodicalId":263089,"journal":{"name":"Production Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Procedimento para implantar um modelo de previsão de demanda com incorporação de julgamento de especialistas\",\"authors\":\"Francielly Hedler Staudt, M. B. Gonçalves, C. Rodriguez\",\"doi\":\"10.1590/0103-6513.054612\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When marketing information is well interpreted and incorporated into a quantitative forecast by an expert, forecast accuracy may be enhanced. However, human judgment might introduce biases into the forecast. One way to avoid these biases is to use structured adjustment approaches. This article presents a procedure to help companies implement a demand forecasting system with a judgmental adjustment of statistical forecasts. The use of this procedure in a small company shows its implementation. The results demonstrated that judgmental adjustments improved quantitative forecast accuracy by an average of 5%. The results also showed that the product with the greatest variability in a time series had the best adjustment performance and that the best outcomes came from the larger adjustments.\",\"PeriodicalId\":263089,\"journal\":{\"name\":\"Production Journal\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Production Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1590/0103-6513.054612\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1590/0103-6513.054612","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Procedimento para implantar um modelo de previsão de demanda com incorporação de julgamento de especialistas
When marketing information is well interpreted and incorporated into a quantitative forecast by an expert, forecast accuracy may be enhanced. However, human judgment might introduce biases into the forecast. One way to avoid these biases is to use structured adjustment approaches. This article presents a procedure to help companies implement a demand forecasting system with a judgmental adjustment of statistical forecasts. The use of this procedure in a small company shows its implementation. The results demonstrated that judgmental adjustments improved quantitative forecast accuracy by an average of 5%. The results also showed that the product with the greatest variability in a time series had the best adjustment performance and that the best outcomes came from the larger adjustments.