{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T17:59:09Z","timestamp":1778954349678,"version":"3.51.4"},"reference-count":0,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,10,1]]},"abstract":"<p>Effective supply chain management is one of the key determinants of success of today\u2019s businesses. However, communication patterns between participants that emerge in a supply chain tend to distort the original consumer\u2019s demand and create high levels of noise. In this article, we compare the performance of new machine learning (ML)-based forecasting techniques with the more traditional methods. To this end we used the data from a chocolate manufacturer, a toner cartridge manufacturer, as well as from the Statistics Canada manufacturing survey. A representative set of traditional and ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As a group, based on ranking, the average performance of the ML techniques does not outperform the traditional approaches. However, using a support vector machine (SVM) that is trained on multiple demand series has produced the most accurate forecasts.<\/p>","DOI":"10.4018\/jiit.2007100103","type":"journal-article","created":{"date-parts":[[2011,2,15]],"date-time":"2011-02-15T18:53:59Z","timestamp":1297796039000},"page":"40-57","source":"Crossref","is-referenced-by-count":30,"title":["Machine Learning-Based Demand Forecasting in Supply Chains"],"prefix":"10.4018","volume":"3","author":[{"given":"Real","family":"Carbonneau","sequence":"first","affiliation":[{"name":"Concordia University, Canada"}]},{"given":"Rustam","family":"Vahidov","sequence":"additional","affiliation":[{"name":"Concordia University, Canada"}]},{"given":"Kevin","family":"Laframboise","sequence":"additional","affiliation":[{"name":"Concordia University, Canada"}]}],"member":"2432","container-title":["International Journal of Intelligent Information Technologies"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=2426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T15:15:59Z","timestamp":1654096559000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jiit.2007100103"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2007,10,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2007,10]]}},"URL":"https:\/\/doi.org\/10.4018\/jiit.2007100103","relation":{},"ISSN":["1548-3657","1548-3665"],"issn-type":[{"value":"1548-3657","type":"print"},{"value":"1548-3665","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,10,1]]}}}