Jack Levis of UPS
The payoff from investments in predictive and prescriptive analytics comes when front-line workers use the findings to make better business decisions. Companies that hire data scientists to seek new patterns in corporate data also need a workforce with a deeper knowledge of statistics who can act on the results.
“You have to understand how to interpret the answers,” says Jack Levis, director of process management with United Parcel Service. Levis is rolling out an initiative, On Road Integrated Optimization and Navigation (ORION) that crunches business rules, map data, customer information and employee work rules, among other factors, to optimize package delivery routes.
When fully deployed, the system will offer more than 55,000 front-line supervisors and drivers the tools to test scenarios and make tradeoffs. To reach a performance objective, is it better to save a mile of driving? Or to make a premium delivery 15 minutes early?
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One Comment
Great article. Thank you for talking about the challenges of implementing big data analytics in large organizations. So much focus has been placed on data management, predictive modeling and BI. Such little discussion about the practical aspects of getting business people to use