Ryan is a Data Science Tech Lead Manager at DoorDash, since early 2020, where he works on DoorDash's forecasting platform. He holds a Masters degree i

Increasing Operational Efficiency with Scalable Forecasting

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2021-08-31 18:30:02

Ryan is a Data Science Tech Lead Manager at DoorDash, since early 2020, where he works on DoorDash's forecasting platform. He holds a Masters degree in Analytics from North Carolina State University.

Forecasting is essential for planning and operations at any business — especially those where success is heavily indexed on operational efficiency. Retail businesses must ensure supply meets demand across volatile changes in seasonal preferences and consumer demand. Manufacturers need to ensure they have the right amount of supplies and inventory in order to fulfill orders without locking up money in idle or unused resources. Other industries rely on forecasting for staffing, vendor commitments, and financial planning among a host of other applications.

Similarly, DoorDash has many forecasting needs covering everything from deliveries to marketing to financial performance. Ensuring that our internal business partners get the information they need, our Data Science team developed what we call our Forecast Factory, a platform allowing all our teams to set up their own forecasts without the help of a dedicated team of data scientists. We’ll discuss the general characteristics of forecasts and the challenges managing them at scale then explain how we were able to overcome these challenges by building our Forecast Factory. 

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