Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs

THURS, oct 10TH, 2019

When: 12pm - 1pm
Where: UNC Charlotte Center City Building (320 E 9th St, Charlotte, NC 28202).

Lunch: A brown bag lunch may be brought in or Einstein’s Bagels is located in the lobby of Center City.
Parking: Parking will not be covered. We recommend using the 7th street parking deck. The pay to park lots near the building are full during week days. Parking is very limited in the area and we suggest that you walk, take the lightrail or carpool if possible. Please refrain from parking in UNCC's parking lots, as you could be ticketed.

For more information on the Center City Building, visit




Principal Data Scientist at American Tire Distributors (ATD)

Tianchu Liang is a Principal Data Scientist with American Tire Distributors. He’s a physicist and mathematician turned computer scientist turned machine learning enthusiast. He develops and deploys machine learning solutions to solve real world business problems, such as using LSTM to forecast staffing needs and using XGBoost models to execute real-time online customer behavior classifications. As one of the first two data scientists in company history to join American Tire Distributors, he helped grow the data science team to a size of 12 within a year and is now developing machine learning solutions to help the company in supply chain, sales, and warehousing, as well as e-commerce.


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 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.