Understanding Priority in Container Optimization

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Note: If you reached this article directly, you may also want to check out our Introduction to Container Optimization.

What is Container Optimization?

When ordering more products purely on demand and stock level, it would be a coincidence to exactly fill a number of containers. More like, you'll end up with a container and half - but a half-filled container is a waste of space and money. Container Optimization is a process that reviews an existing order and recommends additional products to add so that all containers always ship full. Picking the best-suited products as fillers is key to this.

What is Priority in Container Optimization?

In order to have a good chance of success, our optimization algorithm goes through several steps. First, of course, that actual products on the order are added. Next, products that really make a lot of sense get added, then some that are less clear but still beneficial, and so on. This sequence of steps is called prioritization, and the priority of each SKU is shown in our Container Optimization module (see screenshot below).

This article will explain in detail, how this prioritization actually works.

How does the Prioritization actually work?

Container Optimization is a multi-step-process: First, the required products are optimally distributed among the smallest possible number of containers, then additional products that are likely to be ordered soon are added to ensure that all containers are full.

Priority refers to the different steps in this process, and in this article, we describe exactly what happens at the various steps. You can see the priority of a given SKU in the Container Optimization table. There are four steps, numbered 1 through 4. Definitions of the four steps are given below. Container_Optimization_Priority.png

Priority 1: Required Items - "need to order"

In priority 1, we have the Required Items - all items that we need to order anyway. These were on the list before optimization, and they might even be assigned customer orders. While it is possible to manually order fewer items, this should be avoided. Therefore, priority 1 should only be manually increased, while Priority 2, 3, and 4 can be manually increased and also manually reduced.

Priority 2: Larger quantity - "more of the same"

In priority 2, we add more units of SKUs that are already on the order. Let's say the system recommends ordering 31 bikes, but we know that a container can hold 34 bikes; the obvious best choice is to just add 3 additional bikes. The same principle applies to orders with multiple SKU's, where we just order more of the same. When there are multiple SKU's in the order, we have to make a choice about which SKUs to use as fillers; in order to choose which of the existing SKUs to "overorder", the system evaluates things like velocity (how fast do these turn), price, etc., and selects those that make the most sense.

Priority 3: Additional items - "will also sell soon"

In priority 3, we start having additional items that were otherwise not in the order - all items that will also sell soon. These items are the first additional ones that get added during Container Optimization, and to do so, the optimization engine picked items that are just about to hit the threshold for reordering; while not strictly required at this time, they would likely appear on one of the next orders. Therefore the tradeoff between optimized freight costs and slightly higher stock-keeping costs will likely pay off, and do so soon.

Priority 4: Optimal Space - "avoid empty space"

In priority 4, we have items that are always a good opportunity - they are the best candidates to fill any empty space that remains. These are Key Value items with a good margin, and the risk of them sitting in stock forever is low. While the tradeoff between shipping cost and storage cost is less clear that for Priority 3 items, there is little risk that these items will end up in the discount bin.

Want to learn more?

We have an excellent article on our blog that describes the entire Container Optimization process in even more detail.

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