Big Data Diary: Negotiating Delivery’s Critical “Last Mile”

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Big data’s potential has been a big story for some time now, as the things in the Internet of Things multiply by the billions, and all those connected sensors stream out huge amounts of data. This is information we never thought we could capture, unleashing insights we didn’t know we could uncover.

But making sense all this information when and where we need to remains a huge task, and companies are years from truly exploiting it. Just finding data scientists with the skills to analyze and interpret all of the data companies now have at their disposal is extremely tough.

Still, there are plenty of answers to the question “What does big data do for us right now?” In our new Big Data Diary, we’ll focus on big data applications that are interesting and relatable, to see how big data can influence our lives in small and not-so-small ways.

First up: a look at how big data analytics can help cut through congestion for better “last mile” delivery.

A faster last mile

Companies have gotten good at speeding up the delivery of goods from manufacturing hubs to urban markets. But as the Wall Street Journal reported, the so-called “last mile” from the local distribution center to the customer is often where things really slow down. Enter big data.

Matthias Winkenbach, director of the Massachusetts Institute of Technology’s Megacity Logistics Lab, said many companies are now sitting on tons of data that can help them, but only if they can figure out what to do with it.

Winkenbach said shipping route efficiency has long been measured simply by when a package left a given depot, how far it went, and how much fuel was used to get it there. But now GPS-enabled devices and widely proliferated IoT sensors can give shippers a near total view of any delivery route at any time, and they can combine this data with analytics to discern patterns for insights into achieving greater efficiencies.

For instance, geospatial data indicates the longest door stops come in the densest areas, where parking is scarce and there are high-rise apartments, where customers are harder to reach. Big data also reveals customer behavioral characteristics, such as which ones are chronically not home. All this information can be used to improve route planning and training programs, and even to better select delivery vehicles. For example, bikes making multiple short deliveries might be better in some places than larger trucks making bulk deliveries.

Winkenbach said that, overall, the data shows that multi-tiered systems almost always speed delivery times. Those systems can include smaller, more dispersed distribution centers or pre-designated spaces where larger vehicles can park, and smaller vehicles can finish the delivery. In Madrid, for example, the Legazpi Transshipment Center enables companies to shift packages from larger trucks to electric vans or tricycles with delivery baskets to complete that last mile.

Of course, Winkenbach noted, certain things remain beyond the grasp of big data.

“It’s a challenge to get accurate data on weather,” he said. “And all it takes is a big rainstorm and deliveries slow way down.”

Equinix also can’t do anything about the weather, but we can help companies get a firm grasp on big data. Learn more about our Data Hub solution, which enables organization to protect and store massive amounts of data close to where they need it, for direct, superior interconnection and faster insights.

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Check out the latest entry in our Big Data Diary series, Name that Organism!

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