See a boxy brown van, and chances are "UPS" immediately pops to mind. Those vans are iconic symbols of package delivery, maybe even business as a whole. I don't think I'd be venturing too far off course in saying analytics deserves some of the credit.
As much as that color brown is steeped in the public consciousness, so too is analytics at the company itself. UPS is a poster child for building an analytics culture if ever there was one.
Jack Levis, director of process management at UPS, credits the analytics culture with his ability to undertake the massive route optimization project I've written about in depth in
Inside Analytics: UPS Delivers the Goods. If you haven't had a chance to read the piece yet, take some time to do so and get a peek inside one of the most sophisticated analytics programs around.
"The No. 1 thing enabling this is our culture." Levis told me in a phone interview. "We've been gathering data and analyzing our operations long before it became a trend. As part of our culture, everything is measured, everything is managed, everything is planned."
Maybe you think Levis is exaggerating. But he's not. Consider this statement from former UPS CEO George Smith:
If we did not have operational research, our rate of growth might have been affected. As we grow in size, our problems increase geometrically. Without operational research, we would be analyzing our problems intuitively only, and we would miss many opportunities to get maximum efficiency out of our operation.
Sounds current. But guess what -- this quote is from 1954.
Over the years, that foundational understanding has fueled UPS's journey from descriptive analytics, to predictive analytics, to prescriptive analytics. For decades, the company relied on telematics, which helped it figure out exactly what a driver did the day before, and then provide coaching and prompt corrections. But by the 1990s, it realized that looking in the rearview wasn't good enough. So UPS developed a way to look forward through what it calls package-flow technology.
"This is a predictive analytics type of technology that says, 'Where will deliveries occur tomorrow, and what should a driver do tomorrow?' " Levis said.
And, because it would need some way of communicating that information, it gave drivers a new device, too. UPS initially called these "delivery information acquisition devices" because they "were what drivers input information into so that we could move it to our data stores and serve customers with where their packages are." But as predictive analytics developed, these acquisition devices evolved into "assistant" devices through which UPS "gave drivers information so they could make better decisions."
Moving into predictive analytics also meant the development of new planning tools -- not just for the long term but for the short term, too. Levis explained:
We know what packages are coming tomorrow, and we'll use some forecasting information where we can create a plan for tomorrow even before we’ve picked up the packages for delivery. The planning tools also will predict which drivers have too much work, which drivers have too little work, and which drivers have inefficient work and let us make changes to tomorrow's plan.
And then we print out a special label, just before the package is touched by an employee that says, 'Here's exactly where that package goes, here's the vehicle it goes into, here's where in the vehicle it goes, and how it fits into the driver's day.' And we give the driver the same information so he can better serve the customer.
The prescriptive analytics project, called ORION for short, takes that a step further and allows the company to optimize operational -- i.e., route -- decisions. I won't elaborate here, as I've already done so in the case study, but I will point to one further piece of evidence that an analytics culture is alive and well at UPS: The ORION team worked for 10 years in getting the optimization system ready for rollout. Surely, such commitment comes from a top-down, data-driven culture!
UPS will be all the wiser for the commitment, Levis concluded:
People talk about going from data to information to knowledge as they go from descriptive to predictive analytics. Well, beyond knowledge is where the prescription comes in, and I think when you get to prescriptive analytics, you move from knowledge to wisdom. You're making decisions that are so much wiser than before.
What other companies can you think of that were well ahead of their time with analytics? Share below.
Related posts:
Inside Analytics: UPS Delivers the Goods
3 Levels of Analytical Sophistication
Point/Counterpoint: Debating the Feasibility of an Analytics Culture
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