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Warehouse Slotting.By Dave Piasecki
What is slotting?
So let’s start with the basics. We actually do quite a bit of slotting in our day-to-day activities. If you look in your kitchen cupboards, you will likely find that the dishes and cooking items you use most frequently are located near the front of easily accessible cupboards. If you live in an area that gets snow, you probably keep a snow shovel close to your garage door or the door to your house. The credit card you use the most frequently is likely located in an easily accessible spot in your wallet. This is all slotting, and you’re right in thinking it ain’t exactly rocket science. All we’re doing here is making sure those things we use the most frequently are the most easily accessible. In doing so, the things that we don’t use frequently will often naturally work their way back into those less accessible areas (slots). In the warehouse, we essentially want to apply the same logic. The items in your warehouse each have their own unique combination of characteristics. One of the most important characteristics is the frequency of physical touches for each specific item. By “physical touches”, I’m referring to each time someone has to go to the location the item is stored in to do something (pick an item for an order, put away receipt, etc.). As physical touch activities go, the one that gets the most attention is order picking. So not surprisingly, most slotting is focused on improving the efficiency of order picking.
A very basic slotting project. The typical slotting project will start by ranking all items in the warehouse based on the frequency each is picked. This is very easy to do and simply involves creating a report or query(s) based on either your sales order detail file, a transaction history file, or a movement history file (if you have a WMS). You will generally run this against a full year’s transactions, and count all sales transactions for each item, then sort the report or query by the total sales transactions in descending order. So the first item on the report is your fastest moving item, the second is the second fastest moving, and so on. Now remember, we’re talking about the total number of times an item is picked here, not the total quantity that is picked. If we did the latter, then an item that was picked a thousand times last year with a pick quantity of one unit would be ranked the same as an item that was picked one time with a pick quantity of a thousand. Obviously, from and order-picking efficiency perspective, we want that item that was picked a thousand times in a more accessible slot than the one that was picked once, even if they both represent the same unit sales. Now that we have our times-picked ranking that shows what our fast movers are, we need to determine where we want our fast movers to go. So now we need to look at the characteristics of our locations. Primarily, we want to look at the distance an order picker would need to travel to get to the location, and how accessible the location will be once the picker gets there. A location that requires a ladder or lift truck to access is not as easily accessible as one that can be directly accessed from floor level. And even within those locations that can be accessed from floor level, those that don’t require a worker to bend over or reach up are more easily accessible than those that do. As to the distance an order picker would need to travel, things get a little more complicated. That’s because we need to consider exactly how orders are picked in your operation. If a picker has a fixed starting point and moves to a location for a single pick, then returns to that fixed location, we can simply rank the locations by the distance from the fixed starting point. This would often be the case in a full-pallet picking operation where a lift truck operator can only pick one pallet at a time before returning to the starting point (probably the shipping dock). But what if a picker follows a fixed picking path through several aisles before returning to the starting point? In this case, each location along that fixed picking path will be ranked the same as far as distance goes, because the picker is going to go past each location anyway. In many warehouses, you actually have a combination of the previous examples, so you need to take all this into consideration when ranking your locations. Now that we have our locations ranked and our items ranked, we can just take our fastest moving item and slot it in our best location, then take our second fastest moving item and put it in our second best location, and so on.
But wait, there's more.. In some operations, the above example is about all you really need to do. Well, that is assuming that your items are all similar in size, and your locations are all similar, and your quantity per pick is all similar, and there are no relationships between products, and . . . As you can see, there are a few more things that need to be considered.
Firstly, when you have items with greatly varying sizes you will also likely
have locations (slots) of varying sizes. So you obviously need to make sure the
items are going into slots that are appropriately sized. But an item’s size
combined with the quantity picked also needs to be considered. In both these
cases, the size of the slot is driven by the size of the item and how much of
that item needs to be stored there. Slot size is important when looking at order
picking efficiency because larger slots result in your order pickers needing to
travel further when moving past these slots to get to other slots. So what we
really need to look at here is how many picks we can get out of a specific
amount of pick face. Which brings up the question of how much (quantity) should you expect a slot to hold? Larger slots will stretch out your pick paths and therefore result in less efficient order picking. But smaller slots require more frequent replenishment. You can actually apply costs to these activities and calculate the point that makes the most sense financially, but this can get complicated. I will typically do some rough calculations by product group, and then put together some guidelines to apply to each product group. Other slotting considerations would include the weight of the items being stored or other characteristics that may make some slots more appropriate than others. Generally speaking, you want to keep your heaviest items lower, but this doesn't necessarily mean the need to go in floor locations. You really need to look at how you pick and stock these items. If, for example, you are picking and stocking these heavy items from a raised picking cart, the best slots for these items would be levels close to the level of the cart. And then there are more obvious characteristics, such as hazardous items being stored in specific areas designed for hazardous items, or cold storage items being slotted in cold storage. Some operations may need to consider load building when slotting their products. This is important if you are picking directly to your shipping pallets and need to make sure heavy items go on the bottom, and more fragile items go on the top.
We may also want to consider related items that are often picked together on the
same order. This takes a bit of analysis, but can be very beneficial in some
operations. Obviously you need to consider this within the context of how you
pick orders (your pick paths), because the benefits of slotting these items
together will vary based on your pick paths. And then there's "dynamic slotting". You may run into this term when looking at the functionality of certain Warehouse Management Systems (WMS). Dynamic slotting is not yet a standardized term, so it can mean just about anything. But what it often means is that the WMS has the capabilities to set up new or temporary slots for items allocated for known shipments. So if you know you are going to have a lot of picks for a specific item today, you may choose to create a new temporary slot right by the picking/shipping area rather than sending the picker to the normal slot. We've talked about order picking, and a little about replenishment, but slotting also affects putaway, cycle counting and physical inventories, and overall space efficiency. We also need to determine when to replenish pick slots (assuming you are using forward picking locations) and when to reslot. Remember earlier when I said this ain't exactly rocket science? Well, as you start taking into account all the potential variables, it does begin to feel like rocket science. The reality is, absolute optimization of slotting is simply impractical for most operations. But that doesn't mean you give up and do nothing. By understanding the variables in your specific environment, you can use your knowledge of these variables to help you approximate their impact on groups of products and locations. If a variable doesn't make a significant difference in your specific operation, don't waste your time trying to incorporate it. If a variable is important, but is way too complicated to calculate on an item-by-item basis, try to put together some guidelines that help to take this variable into account. And try not to get too overwhelmed by the complexity of all these calculations. While many of them have interdependencies that need to be considered, it is often easier to build up from a series of variable-specific calculations than to attempt to make one master slotting calculation.
Dave Piasecki, CPIM is owner/operator of Inventory Operations Consulting LLC, a consulting firm providing services related to inventory management, material handling, and warehouse operations to manufacturers and distributors in Southeast Wisconsin and Northeast Illinois, and author of the books Inventory Management Explained and Inventory Accuracy: People Processes, and Technology. He has over 20 years experience in warehousing and inventory management and can be reached through his website (http://www.inventoryops.com), where he maintains additional relevant information and links Copyright © 2010 David J Piasecki |
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