Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.
|Talk Title||Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs|
|Speakers||Alex (Tianchu) Liang (American Tire Distributors)|
|Conference||O’Reilly Artificial Intelligence Conference|
|Conf Tag||Put AI to Work|
|Location||San Jose, California|
|Date||September 10-12, 2019|
Deep learning has been a sweeping revolution in the current world of AI and machine learning. It uses convolutional neural networks (CNNs) to help Teslas see the road properly; it uses reinforcement deep learning to help SpaceX land rockets automatically, and it uses recurrent neural networks (RNNs) to make machines translate better. The list goes on and on. But traditional industries may not see how this new, hot technology can help them. Alex Liang details how the data science team at American Tire Distributors (ATD) uses machine learning solutions to rejuvenate the company. He used LSTM RNN models ensembled with fbProphet to generate staffing-level forecasts and further optimized with CVXPY for maximum optimality of staffing schedules. He also implemented deep neural nets as part of a pricing optimization pipeline, where DNNs are used for clustering as well as product demand modeling. The warehouse solution is now being used every day across the entire US in 140 distribution centers to cost-effectively staff more than 2,000 people daily and is on track to realize ~10% in labor cost savings and the pricing solutions are now being fully productionalized into the revenue management process, automating and optimizing pricing decisions for the majority of products. He outlines the overall business problem context, initial machine learning prototyping, resolving challenges in data and compute, and application automation. You’ll leave with key takeaways in developing this solution, including both technical and business lessons.