Machine learning demand forecasting and supply chain performance
Name
Machine learning demand forecasting and supply chain performance.pdf
Size
3.29 MB
Format
Adobe PDF
Checksum (MD5)
e4144cc5122e455a1ae581bdcc334277
Author(s)
Feizabadi, Javad
Date Issued
August 4, 2020
Journal
International Journal of Logistics Research and Applications
Publisher
Taylor & Francis
Citation
Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142.
Version
Final published version
Abstract
In many supply chains, firms staged in upstream of the chain suffer from variance amplification emanating from demand information distortion in a multi-stage supply chain and, consequently, their operation inefficiency. Prior research suggest that employing advanced demand forecasting, such as machine learning, could mitigate the effect and improve the performance; however, it is less known what is the extent and magnitude of savings as tangible supply chain performance outcomes. In this research, hybrid demand forecasting methods grounded on machine learning i.e. ARIMAX and Neural Network is developed. Both time series and explanatory factors are feed into the developed method. The method was applied and evaluated in the context of functional product and a steel manufacturer. The statistically significant supply chain performance improvement differences were found across traditional and ML-based demand forecasting methods. The implications for the theory and practice are also presented.
MIT Department
Massachusetts Institute of Technology. Supply Chain Management Program
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/13675567.2020.1803246
