The automotive industry has always been challenging for manufacturers and distributors serving it. You might have ultra-thin margins with every year the OEMs threatening to reduce the costs further. You also have had traditional demand forecasting issues where the demand forecasted by industry groups may be off by as much as 50%. Also, with COVID and supply chain issues, the job of forecasters has become even more challenging. Now your choice is to overstock or overwork to meet the unpredictable demand. Or figure out how to take advantage of newer technologies such as machine learning platforms that are helping automotive manufacturers become leaner and operationally efficient.
In today's episode, our guest, Ryan Knox from Flexfab, Tony Lancione from Lancione Group, and Eamonn Barrett from Remi AI, share their insights into how AI and Machine learning have solved their traditional demand forecasting issues. They also describe why automotive is unique with its demand forecasting needs and why this market has unique challenges to forecast the demand compared to other manufacturing verticals such as Aerospace or consumer goods. Finally, they describe the nuances of the machine learning algorithm and how the traditional approaches might fall short or maybe cost prohibitive to achieve the same forecasting accuracy.
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