Back in the late 90s we had a lot of descriptive data that told us where we were, and it was very detailed. We measured things in hundredths of a thousandths of a second. Why? Because if we can reduce one mile per driver per day in the United States alone, that's worth up to $50 million to the bottom line at the end of the year. So we measure things very granularly because little things matter to us. Our culture is, if you worry about the pennies, the dollars will take care of themselves.
So in the late 90s we had lots of information about what happened yesterday, but changing tomorrow was hard. We were a knowledge-, methods-, and procedures-driven organization and some of the data was in the heads of employees, some was in corporate repositories, some was in Excel spreadsheets and some was distributed. But in the late 90s we didn't have a predictive data model that described how UPS operates. So we took on a project called Package Flow Technologies.
The idea was, if we knew where every package was at every moment of the day, and where it needs to go and why, then we could just flip a bit and change where a package is headed tomorrow and that would help make us more efficient.
We deployed that in 2003. And the deployment of these predictive models and planning tools meant that, rather than a driver starting the day with an empty DIAD and filling it up, drivers started the day with a DIAD full of things we wanted them to do. So instead of an acquisition device, it became an assistant. With that predictive nature of looking forward, we reduced 85 million miles driven a year. That's 8.5 million gallons of fuel that we're not buying and 85,000 metric tons of CO2 not going in the atmosphere.
We have what we call "all services on board." One driver, one vehicle, one service area, and one facility. But we have different services — deferred service and premium service on the same vehicle so that driver has some packages that have to be delivered at 10:30 am, some that have to be delivered at noon, and some that have to be delivered at 2:00 pm. So that meant the driver had to decide how he was going to service these multiple commodities at the same time.
And even though we saved 8.5 million gallons of fuel, we wanted to take it further by prescribing, using some very advanced mathematics that says to the driver, "Let me reorganize today's route based on today's customers, today's needs, today's packages, and put it in a very specific order." It used to be the driver would figure out how to handle anomalies. Now it's in a very specific order, optimized with data and analytics.
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