Leveraging data and order commonality for extreme efficiency

In this blog, we often talk about how a distribution operation can move from average performance to best-in-class a lot more easily than one would think. The reality is, if you’re running at an average productivity / efficiency rate or your costs are steadily increasing (or spiraling out of control during your busier peaks), you won’t have to look that far to uncover improvement opportunity. You may need some guidance and the right tools but the opportunity is there.

Trust me, in the average Strategic Opportunity Assessment that we do using our fulfillment optimization system, SKUStream, we uncover nearly 500k in disruption-free annual savings. For those operations that have been running within the “status quo” for years while their requirements change with the turn of every season, improvement opportunity is usually rampant and there are also usually a few common tweaks that can be made to generate disruption-free – and 6-figure savings – right away.

But that’s low hanging fruit. What if you’re already running close to best-in-class, you’ve maximized your “low-hanging fruit” opportunity but you’re still not satisfied? Or, maybe you have a large investment in mind (automation or even a new site activation) and you’re looking to find some additional room the in budget?

I mean, why would you want to be paying more than you have to? You would never buy a Ford Escort at a Ferrari’s price point, would you?

Analogies aside, there’s some good news, there are some additional strategies that you can employ to get a little extra out of an already high-functioning operation.

Here’s an example of one of those strategies. I’m using this one (out of potentially a dozen others) because, while it may require a bit of set-up and some investment – if you want to do it properly – your existing processes don’t need to change. Now that we’re into the more tactical adjustments, this is as minimally invasive to an existing operation as it gets.

I’m talking about commonality-based slotting. Now, before you read ahead, I know you’re going to say that this concept only works if you are working with different order profiles i.e. you’re shipping wholesale and retail orders or you’re working with multiple unique brands / private labels. You’d be right but you may also be surprised, once you get into the data, at how much your operation may be applicable to this concept. That’s where your data comes in, and maybe some advanced tools. It just so happens that we have a fulfillment optimization system that has this analysis built in, so when we work with a company that may be a candidate, we have this analysis at the click of a button.

We’re not inventing the wheel here or coming up with a new concept that was previously unheard of. Not at all. The reality is, you can set up a commonality-based concept relatively easily. We’ve seen it done. Unfortunately, we’ve also seen those same concepts fail in a matter of weeks due to a lack of maintenance. Not to downplay proper set-up, which obviously has a huge impact on the concept’s success, but consistent maintenance will make or break this concept very quickly.

Getting back to the example, I don’t want to breeze through the details too much but I’ll outline the steps we took from requirement planning to execution and maintenance. As I said, if you believe your operation may be a candidate for this concept or you’re struggling to find new ways to tighten up your budget, reach out to us and we can help you do it right.

1. Candidacy

First thing is first. You need to find out if your operation is right for this concept. Again, if you’re on the fence about it, or maybe tried unsuccessfully in the past, make sure to let data dictate this part of the process. You may be surprised.

Now, what we do at this stage is run an order commonality analysis using a set of operational data. As is suggested in the name, this analysis looks for commonality among outbound orders, trying to isolate a group of common items that are usually picked separately from the full variety. Again, this is common for retailers that also ship wholesale, or for operations that ship to multiple banners, etc.

What we’re looking for here is a subset of orders that, 90% of the time, are picked separately. It’s important to do this analysis by hit rate (how often the item shows up on an order) and not by cubic volume as product size has a tendency to skew the results. Typically, if we can isolate a group that represents at least 10% of the full SKU variety in the building / zone, the operation is a prime candidate for this concept. Generally speaking, the higher the percentage, the more savings you will get from it. This is why we’re not looking for items that are 100% exclusive.

2. Requirement Planning

Once you’ve let the data dictate your operations’ candidacy for this concept, you’ll have to plan out your requirements. To do this properly, we suggest optimally slotting the full building and potentially adjusting profiles but, at the very least, try to ensure your items are slotted appropriately within the rack types you do have. Additionally, this step will let you know how many racks you need for your unique area and if you can make it work in execution (space, tunnel location, etc.). If you don’t have the tools to do this properly, we highly suggest bringing in a 3rd party. While maintenance will make or break the concept eventually, faulty requirement planning will ensure the concept never gets off the ground in the first place.

3. Layout / Pick Path Adjustment

This is where the rubber hits the road, so to speak – the culmination of your requirement planning process. If you’ve engaged a third party to assist, this is typically where they will assist in putting together a few different layout concepts that each have their own pros and cons. If you’re doing this yourself, get out the pencil, eraser and the most recent layout, as you’ll have some work to do. The idea here is to match your requirements (let’s say 100 racks) with the items you have in your “unique” zone. You don’t want to group them all together as that will require a systematic break – something we want to avoid if we can. You’ll want to try and fold this unique area as concisely as possible into your existing layout, taking advantage of tunnels (if they exist) as much as possible.

Before we get any further, the idea of this concept (and where your savings is going to come from) is in crafting your unique area so that your common orders (the other 90%~ of your active items) can avoid it altogether. Your savings, ultimately, are from a reduction in pick path for your common orders but, inherently, you will also reduce the pick path for your unique items through the use of tunnels or the avoidance of aisles (if planned safely).

Here’s a quick illustration showing a layout with a common and unique slotting concept.

This represents one of potentially many workable options for this layout. The yellow, of course represents the unique items that fell into the 10% commonality threshold and the other colors represent the different regular turn zones – all picked together. Again, the idea behind this concept is that, while the unique items can be picked mostly by avoiding the common area (whenever the opportunity presents itself), the common orders can avoid these unique items completely.

As you can see, in this example, the potential for pick path savings is considerable. This concept above (in practice) made it possible for 53% of orders to bypass 29% of the warehouse.

This concept alone had the potential to generate nearly $250,000 in annual savings, just in pick path reduction. No systematic changes required and no capital expenditure. All that was required to execute the above concept was minor re-profiling of existing racks and re-slotting of items to make commonality plan. Don’t forget, this is just a single site. If you’re talking about a potential network roll-out of this concept, you’re looking at quite a bit more.

Again, the execution all rests on slotting and consistent maintenance.

4. Item Slotting

Once the conceptual layout is finalized and all required racking changes have been made, you will need to slot your new areas appropriately. If you’re using a tool like a fulfillment optimization system, these items will already be separated, and you’ll be ready to execute at the click of a button. Another benefit of using a fulfillment optimization system at this stage is that you’ll have the choice to either create a mass-slotting list and move all items at once, or plan out your moves through the system’s execution tracking capabilities, and make the moves slowly but consistetly until the concept is fully operational. Either option works well. The former allows you to realize savings right away while the latter allows you to execute the concept without any disruption.

5. Maintenance

The final – and most important step if you want to achieve long-term success – is maintenance. Again, this is where a fulfillment optimization system is worth its weight in gold. Slotting optimization in general is a high-value activity. If you’re reading this blog you probably already know that, but it’s even more valuable when trying to execute a more advanced slotting concept like this. Similar to how you would use a slotting system to govern a velocity-based slotting concept like very narrow aisle, you would use a slotting system to consistently determine candidates for your commonality zones. Essentially, as items come and go (and potentially change from common to unique or vice versa) your slotting needs to be adjusted accordingly. It may seem like a lot of work and that’s because, without the right system, it is. There’s no sugar-coating it. If you want to reach that upper echelon of performance, you need to ditch the spreadsheets and ‘shot-gun’ approach to slotting.

There you have it. Your tried and tested checklist to planning and executing a commonality-based slotting concept.

if you want to know whether or not you’re a candidate, sign up for a no-obligation Strategic Opportunity Assessment and we’ll tell you using your own operational data.

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