“Data-driven analytics” describes an approach to warehouse and distribution center management that addresses the challenges of the omnichannel era. But is it ready for prime time? Matt Davidson, vice president of product and marketing with Locix, answers the question.
SCB: What changes in the industry are currently affecting warehouse and logistics operators?
Davidson: The broader market is shifting drastically toward just-in-time, next-day and same-day delivery. What used to be massive warehouses existing hours outside cities are now closer in and urbanized, so they have a higher cost per square foot. At the same time, they’re trying to push through higher volumes, while accelerating delivery times and being cost-conscious.
SCB: We hear about the concept of data-driven analytics. What does that mean, and how can it address the challenges that you just outlined?
Davidson: Everyone talks about the importance of data, but the bigger thing is how you get information out of it — how you make sure you're collecting the right data to make the right decisions. Warehouse operators intuitively understand what's happening in their facilities, and can see generally where there are inefficiencies. But they don't have a clear, specific set of information to go forward and make the business case for adjusting staffing or altering delivery times. Data-driven analytics tell us what's actually happening day-to-day. It helps to pull all that information into a concerted form, and provide a more purposeful approach to handling operations in the warehouse.
SCB: Where is the data coming from? What are the inputs?
Davidson: There's already a ton of data coming in, because almost all distribution centers have some level of warehouse management system. But then there's this other piece, where we're trying to get a better handle on transportation. You might understand where the trucks are coming from, but there's even more granular data about how they interface with the warehouse.
SCB: What are some of the most essential aspects of the shipping process where data-driven analytics can be used to drive productivity?
Davidson: Dwell time immediately comes to mind. I've heard estimates of over $40bn a year in lost productivity among warehouse operations, truck drivers and logistics providers. Anytime you've got a truck that's not operating, you have some sort of loss, whether in the form of drive time or availability of product within the warehouse. The dock is the gatekeeper. Having resources tied up there generates far-reaching problems throughout the industry.
SCB: So you've got to create a smarter trucker berth, and a smarter warehouse. Give us some tips on how you'd go about doing that.
Davidson: The first thing you have to do is realize what the problem is. People will say, "It takes about two hours to turn around a truck on average. But everybody knows that almost never happens." But they don't know the actual percentage of dwells. Defining the problem is the first step toward understanding how you deal with it. What's leading to this issue? Is it because the truck driver doesn't know his load is ready? Is the warehouse operator unaware that the truck has arrived? It comes back to the data — how you pull it together and bridge the gap between the logistics network and the warehouse.
SCB: The downstream benefits seem obvious — better customer service and order fulfillment. But isn’t there's an upstream advantage as well? If you create a smarter truck berth and a smarter warehouse, you become a shipper of choice for the trucker, and you get the best service and availability of capacity.
Davidson: Absolutely. It also helps you with better scheduling and contracts, if you can actually turn around a truck in an hour. You start to make everything run a little tighter, a little more seamlessly.
SCB: What’s the link between data-driven analytics and artificial intelligence?
Davidson: With A.I., we're trying to recreate the human mind in a computer. The basis of how we make decisions is our senses. Without good data, you can't make good decisions. So the true limitation around A.I. right now is what data can we feed into it.
SCB: Where are we right now in terms of A.I.’s ability to make good decisions, or at least suggestions?
Davidson: We're getting close. As you start to solve one problem, you encounter another. It’s about pulling those decisions together into a more general viewpoint. If I know a truck is caught in traffic and is going to be an hour delayed, then all warehouse operations can respond based upon that change. As the system gets better at each specific task, it becomes more intelligent overall.
SCB: Once you adopt data analytics, do you need human analytics experts — people with high math skills?
Davidson: At this point, if you're trying to automate your entire network, then it's probably necessary. Everyone’s talking about autonomous warehouses, but it’s not going to happen overnight. It’s when the system gets smarter that you no longer need an expert to program your entire operations.
SCB: So where do you think we are on the road today, in terms of the ability of the A.I. system to make good recommendations?
Davidson: In the logistics sector, we're really in the early stages.
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