Leveraging data to proactively maximize inbound pallet productivity

When it comes to optimizing a fulfillment operation, there are usually a couple schools of thought.

We talk about selection travel reduction a lot in this blog because, quite frankly, it’s the lowest hanging fruit in most operations. Selection travel represents a considerable cost to the average case pick operation, so any increase in picking efficiency (including a reduction in pick path) can yield significant results.

Our strategic opportunity assessments cover the full spectrum of opportunity discovery: strategic, tactical and operational and, without fail, savings in pick path reduction are the most significant – sometimes in the low 7 figures per year.

Now, while the savings from a reduction in pick path (or refinements to the order selection process) are usually always significant, it doesn’t come without effort and, potentially, some expense. In our experience, it’s usually still well worth it but we get that it’s not always feasible to make tactical changes to an existing layout.

Luckily, there are other ways to create significant savings opportunities, disruption-free.

Increasing efficiency surrounding inbound processes like pallet configuration and putaway is an often overlooked as an area for potential improvement but mostly because it requires consistent maintenance and an advanced slotting optimization process.

Though proper execution of an inbound optimization strategy requires know-how and, typically, a fulfillment optimization system (or similar), the principle is simple: reduce the number of pallets re-configured on the dock and the number of pallets putaway into the operation. While we are big proponents of keeping mind of re-slotting opportunities – especially for the more seasonal and fast-moving items – the inbound optimization strategy applies just to items showing up on the dock each day, as that’s the best time to ensure they are putaway and slotted appropriately.

This strategy doesn’t necessarily negate opportunities to reduce pick path but these strategies are on different ends of the spectrum. To fully commit to an inbound optimization strategy, you are committing to utilizing the space you have today to its maximum potential. Although this doesn’t always mean keeping more product on the floor, you are not actively looking to reduce it in a lot of cases.

The key to this strategy – like most optimization strategies – is in the data. More specifically, vendor TI/HI.

To illustrate an example of this strategy in action, I’m going to use a screenshot from a client using our fulfillment optimization system – SKUStream – to help execute it on a daily basis. Though this example, you’ll gain an appreciation for the importance of not just having the right data at the right time but also why it’s so critical to fold this into the current daily slotting routine. This is truly the difference between slotting and slotting optimization.

Now, the above screenshot is one of the many execution screens found in our fulfillment optimization system, SKUStream. This one focuses on highlighting pallet reduction opportunities and it was personalized for a client that placed heavy emphasis on this metric, network wide. This means the process was standardized and the benefits were realized over multiple high-volume operations. I’m showing a single operation in this example, so visualize these results multiplied by 7.

Drawing your attention back up to the screenshot above, you might be able to tell by the slot number variable that each record is an active item within the operation – new items are handled on a different dedicated screen.

What SKUStream is doing within this screen is highlighting re-slot opportunities for all incoming items that currently have a slot location. Although some left over product is likely in the current pick slot, this is the most opportune time to make the adjustment, not just because it requires minimal labor but because the system is directly highlighting labor savings opportunities associated with items that are right there on the dock, or expected to arrive in the near future (you can do this proactively as well). This is real savings that can be applied to the labor plan directly.

The first column outlined in green (BST) is the slot type recommendation. Without getting into too much detail, this recommendation is the product of both the propriety algorithms that make SKUStream the most advanced fulfilment optimization system on the market and operational data from the clients’ management system. The algorithms have been specifically refined to reflect this strategy.

The second column outlined in green (putaway delta) shows the expected number of pallets reduced by re-slotting the item in the recommended slot type. This is based on current inbound volumes as well as average weekly outbound volumes.

As you can see, on a single day, by employing this strategy, this client was able to save 62 extra pallets, just by maximizing existing space and material handling options. As you can imagine, this not only saved forklift labor, it also reduced pallet congestion on the dock, reduced pick slot replenishments for faster moving items, and saved 62 pallet positions that would have otherwise been used to store the same amount of product.

The financial results? Well, they were significant, especially for a strategy that required no cap ex and no disruption.

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Through the first 5 weeks of employing this strategy, our client was saving an average of $5,000 per week, trending towards $260,000 per year, per facility. Not bad for a strategy that did not require cap ex or disruption.

It goes to show that with the right data, the right tools and the right execution strategy, there is more an one way to optimize your operation and achieve significant gains.

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