Alex Liang

Deep Learning Coming to Tire Industries: Forecasting Warehouse Staffing Needs with LSTM RNNs

Deep Learning has been a sweeping revolution in the current world of AI and machine learning; it helps Teslas see the road properly (CNNs), it helps SpaceX lands rockets automatically (Reinforcement Deep Learning), and it makes machines translate better (RNNs). The list goes on and on. But how does this new, hot, technology help traditional industries? What about for atire distributor company? At American Tire Distributors (ATD), our data science team is rejuvenating the company with machine learning solutions ranging from sales to supplychain, from warehouse operations to online dynamic pricing. In this talk, I will go over a warehouse staffing solution, where I utilized LSTM recurrent neural network model ensembled with fbProphet to generate staffing level forecasts and further optimized with CVXPY for maximum optimality of staffing schedules.

Key Takeaways:

  • Deep Learning is NOT hard to be implemented in traditional industries.
  • However, many challenges exist in data and application deployment and adoption.
  • Successful deep learning application needs to align with real business adoption and impact.

I am a physicist/mathematician turned computer scientist, and then later turned machine learning enthusiast. Through my years working as a data scientist, I develop and deploy machine learning solutions to solve real world business problems, such as using LSTM to forecast staffing needs, using xgboost models to execute real-time online customer behavior classifications. As data scientist #2, I joined American Tire Distributors 12 months ago and helped grow the data science team to a size of 12 within a year; and we are now developing machine learning solutions to help the company in supply chain, sales, warehousing, as well as eCommerce.

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