Stanley Black & Decker Shares How Lean AI is Changing the Work Behind its Supply Chain

Hey Ben, thanks for joining me today to touch on the relationship between Stanley Black and Decker and Robinson Managed Solutions. Going back about six years to the end of 2020, early 2021. Going back to the initial decision to partner with RMS, what business challenges or opportunities led you to rethink how your logistics organization operates?

Well Brian, historically Black & Decker operated a decentralized network with activity driven at the site level. SBD made the strategic decision to move to a more centralized model with a supportive Control Tower to help manage the network. The goal of this was to rapidly improve our routing compliance, increase consolidation opportunities, and drive capability improvements so that we could remove some cost and waste from our supply chain.

Historically, what challenges did incomplete or inconsistent order data create for you and the SBD team? With Stanley Black & Decker, we have over 40K unique SKUs, so it’s really incredibly difficult to keep the master data clean and updated, especially across all the different products lines, vendors, and locations.

Traditionally we’ve seen about 2,000 manual orders every month where there’s missing or incomplete master data in the order when they come through in Navisphere. Those orders traditionally couldn’t move forward until someone from the team would go in and correct those order in the system. And this usually resulted in a much longer cycle time for those orders.

Some of the order inconsistencies were by design. We had some issues with rounding of weights and other specific criteria. And many of the other issues just weren’t able to be caught proactively upstream before an order was placed.

Now let’s talk about where we are today. How is the Lean AI Planner stepping in and helping correct those orders instead of that work needing to be done by one of the team members?

For us, the Lean AI Planner has been a real help. It goes in and leverages its contextual learning and uses our business rules, product attributes, historical shipping behavior, vendor requirements, all kinds of other information, to determine what’s missing and then it goes in and makes the correction.

We still have team members who oversee the corrections, but the Lean AI Planner has been able to go in immediately in real-time and autonomously correct all of the orders that ended in the hold status historically.

This results in reducing the cycle time to less than a minute per order. Almost 90% of what had traditionally been going into the hold status was being corrected proactively by the Lean AI Planner. Basically, that process saved us over 35 hours a month of non-value-added labor from our load planners. We’re moving towards a supply chain that can correct issues before they create downstream disruption and delay. And we’re eliminating a lot of waste in the process.

When we think of a traditional, historical supply chain assessment, that might take weeks, could take months, could be partial, might not be your whole network. Can you touch on what did that process look like for Stanley Black & Decker before the rollout of the Lean AI Engineer?

Before Lean AI Engineer, we had expert engineers who are dedicated to our account. They would review large chunks of shipment volume to look for areas of improvement and optimization. That process was ad hoc. So, as we though there was opportunity, we would go pull the data and go do the analysis. But it could only be completed periodically because of the high level of effort that it took.

With the Lean AI Engineer that solves the problem inherent with having to manually pull data and review it. Because we’re able to look at all data from all sites continuously. Where we look at the same opportunities that the engineers used to look at manually, but now we’re able to do it in real time. This allows our engineers to really focus on evaluating the output rather than creating the output.

So, we’re talking about moving from weeks, maybe months, to minutes and then focus on actually delivering on those opportunities and that cost takeout or cost savings. Yep. It also allows us to look at more information and more opportunities because the system is able to look at it in real time and we just respond to it.

Can you maybe share with the group, what’s an example of an insight or opportunity that you’ve seen from the Lean AI Engineer specific to the Stanley business? So far, Lean AI has identified several new opportunities for SBD. One of the other big things it’s done is it has confirmed that the previous, manual studies were valid.

The one example of the new Lean AI Engineer: it took a specific type of aggregation opportunity that we identified with one customers’ opportunity and we did it manually. But they were able to take the data and apply it across all our customers without having to run separate studies or pull separate data. We’re able to use that information to better support business decisions in real time and it’s really driven a lot of savings and removed waste.

What advice would you give other supply chain leaders who are exploring AI, but they’re just not really sure where to start? For us, the real key is having a cutting-edge partner like RMS. What I would recommend is that they work with RMS to find out a specific use case within their business that will help them drive value.

If they solve for that, try small things first, iterate, move quickly, expect that not everything is going to be perfect, but then you’ll get the result that’s quantifiable. Once you have that initial result, you can share that tangible pieces back with the organization, the stakeholder, to not just talk about AI, but actually show the value it’s bringing to your business.

Ben, want to say thank you very much for joining. Always appreciate these conversations. Excited to share some of the work we’ve been doing together and most excited for what’s still to come here in the future. Thank you.

Thanks so much Brian, my pleasure.