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Leveraging AI and Machine Learning in Demand Forecasting 
S. Aaron McClendon, Applied Analytical Sciences Practice Lead  Aimpoint Digital

 

Artificial intelligence is uprooting the way that we commonly deal with demand planning and forecasting. The impact of Covid-19 on business operations drove this point home to many businesses, as many were unprepared to deal with rapidly changing behavior and erratic demand patterns of their customer bases. Machine learning has been usurping traditional forecasting models and techniques as it is able to incorporate large amounts of information and external data at highly detailed levels. This talk will discuss some of the most innovative forecasting methods being used today from a machine learning standpoint, and best practices around external drivers and deployment strategies.

Attendees will hear why traditional forecasting models failed during COVID,  how AI based techniques and strategies can dramatically improve forecasting performance, and best practices around implementing AI based strategies, including leveraging external data and optimization 

 

S. Aaron McClendon specialize in AI/ML within time series and demand forecasting. He has built several currently used software's bringing automated machine learning to business users, and numerous custom builds within various demand planning organizations, including builds which leveraged 100's of algorithms predicting over 10k skus per day, at 100's of locations in a distributed cloud environment. At Aimpoint Digital, he leads the Applied Analytical Sciences practice, which is the wing of our business focused on AI and ML applications within business facing data science problems. His educational background includes nuclear engineering, experimental material science, and theoretical physics.