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Using artificial intelligence (AI) and machine learning to improve demand forecasting is one of the most promising applications of AI for supply chains. The technology “learns” from past experience and can analyze the multitude of complex relationships and factors that influence product demand.
However, AI-enabled demand forecasting is still at a relatively early stage of development. A key question for supply chain professionals is:
How do the non-traditional methods compare in performance with established forecasting practices? And, to what extent does it affect supply chain efficiency? A thesis research project at the Malaysia Institute of Supply Chain Innovation (MISI) made such a comparison. The project affirms the value of AI in demand forecasting for certain product types, and highlights areas where more research is needed.
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Sorry, but your login has failed. Please recheck your login information and resubmit. If your subscription has expired, renew here.
Using artificial intelligence (AI) and machine learning to improve demand forecasting is one of the most promising applications of AI for supply chains. The technology “learns” from past experience and can analyze the multitude of complex relationships and factors that influence product demand.
However, AI-enabled demand forecasting is still at a relatively early stage of development. A key question for supply chain professionals is:
How do the non-traditional methods compare in performance with established forecasting practices? And, to what extent does it affect supply chain efficiency? A thesis research project at the Malaysia Institute of Supply Chain Innovation (MISI) made such a comparison. The project affirms the value of AI in demand forecasting for certain product types, and highlights areas where more research is needed.
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