An Investigation on Machine Learning Approaches in Supply Chain Forecasting: A Survey
DOI:
https://doi.org/10.61841/shef8216Keywords:
Supply Chain, Forecast, Demand, Artificial Neural Network, Logistics, Support Vector Machine, Bullwhip Effect, Inventory.Abstract
Forecasting necessitates the important decision-making in the supply chain network. Recently, machine learning techniques have been leveraged towards increasing forecast accuracy, thereby reducing errors. In this work, a brief analysis of various machine learning techniques in demand forecasting, demand uncertainty, intermittent demand, and reducing bullwhip effect available in the literature has been surveyed. Demand across echelons of the chain varies as each participant creates various demands. Hence, there is a need for forecasting such scenarios where meeting uncertainties in the future might effectively contribute to the efficient functioning of the supply chain.
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