Information Technology Reference
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Chapter XVIII
Forecasting Supply Chain
Demand Using Machine
Learning Algorithms
Réal Carbonneau
Department of Management Sciences, HEC Montréal, Canada
Rustam Vahidov
John Molson School of Business, Concorida University, Canada
Kevin Laframboise
John Molson School of Business, Concorida University, Canada
aBstract
Managing supply chains in today's complex, dynamic, and uncertain environment is one of the key
challenges affecting the success of the businesses. One of the crucial determinants of effective supply
chain management is the ability to recognize customer demand patterns and react accordingly to the
changes in face of intense competition. Thus the ability to adequately predict demand by the partici-
pants in a supply chain is vital to the survival of businesses. Demand prediction is aggravated by the
fact that communication patterns between participants that emerge in a supply chain tend to distort the
original consumer's demand and create high levels of noise. Distortion and noise negatively impact
forecast quality of the participants. This work investigates the applicability of machine learning (ML)
techniques and compares their performances with the more traditional methods in order to improve de-
mand forecast accuracy in supply chains. To this end we used two data sets from particular companies
(chocolate manufacturer and toner cartridge manufacturer), as well as data 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, Machine
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