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How UPS uses analytics to drive down costs (and no, it doesn't call it big data)

John Dix | Dec. 2, 2014
Network World Editor in Chief John Dix caught up with UPS Senior Director of Process Management Jack Levis for an update on their latest achievements.

When you have an organization the size of UPS with 99,000 vehicles and 424,000 employees every single little bit of efficiency that can be squeezed out of daily operations translates into a big deal. UPS has been using analytics to do just that for a long time now, and keeps getting better and better at it. Network World Editor in Chief John Dix caught up with UPS Senior Director of Process Management Jack Levis for an update on their latest achievements.

How does UPS use analytics to optimize its operations?

Let me take you back 15 years ago and then work our way back to today, and then I'll give you a glimpse into the future. Also, to frame the discussion, let's think of analytics in three forms: descriptive analytics says, "Where am I today?"; predictive analytics says, "With my current trajectory, where will I be headed tomorrow?"; and then at the highest level you have prescriptive analytics, and that's where you say, "Where should I be?"

The research says that as you move up this hierarchy your data needs grow, the skill set of your people increases, and the business impact grows significantly, and that's been exactly our experience.

Gartner says that in the descriptive space only about 70% of organizations really understand where they are. For us that's just old news. We've been doing that for more than 20 years with our drivers' hand-held computers, called the Delivery Information Acquisition Device (DIAD). In the predictive space they say only about 16% of organizations are doing that, and we deployed some predictive models 10 years ago. Then the prescriptive space where optimizations occur, they say only about 3% are there yet, and that's where our ORION system comes in — On-Road Integrated Optimization and Navigation — which we're deploying now.

I bring all of this up because your vision as an organization can't stop at descriptive analytics, because there's lots of value beyond that.

Do you avoid the term big data by design?

Big data is a "how"; it's not the "what". The "what" is big insight and big impact, and if you do that through big data, great. But the key is the impact and the insight. It wasn't called big data when we started describing deliveries of more than 16 million packages a day and building terabytes and terabytes worth of data. We've been doing that since the early '90s. So it's just data to us. I care about what we do with it, since that is how we derive value from it. Analytics is about making better decisions. That's why I generally don't use the term big data.

 

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