In warehouse operations, there is a fair amount of confusion between mixing rules and allocation or fulfillment rules. This is understandable, as the two constrain each other. Understanding how they differ, and how they constrain each other, gives one a powerful set of tools for achieving efficiency.
Allocation rules are what most warehouse workers and logistics operators are familiar with, so it would be helpful to start there.
Allocation Rules for Fulfillment
There are three basic ways in which items stored in a warehouse can be picked for shipping: Strong FIFO, weak FIFO, and labor optimized.
First In First Out Warehousing:
FIFO warehousing stands for “First In, First Out warehousing” a term many people are familiar with. What most warehouse personnel don’t know is that there are different “strengths” of FIFO warehousing rules, depending on how strict one needs to be about which lots are picked first.
Take strong First In First Out Warehousing. If an SKU is to be fulfilled using strong FIFO, it means that items must be fulfilled in a specific order, usually to maintain a “paper trail” where items can be tracked down to the individual end-user. This is important for goods with a shelf-life and a possibility of recall—pharmaceuticals, for example. Strong FIFO ensures both that the oldest items are used first, and that, should a recall occur, individual items from a given lot can be traced.
The downside of strong FIFO is two-fold. First, it means that one must have very strict lot mixing rules to prevent items from different POs from being stored together. This makes storage less efficient. Second, it prevents your operation from choosing the most efficient picking paths, because pickers are limited to picking from very specific locations.
Some of these downsides can be mitigated by using weak FIFO. Weak FIFO maintains the same basic “First In, First Out” concept, but does not demand strict adherence according to PO and lot. This option makes sense for items that have a shelf-life but that do not need precise information for tracking recalls. Batteries are a good example: Generally, one wants to ship older stock first, because batteries have a shelf-life. But strict lot control is not needed.
(Note that there could be reasons to use a different allocation rule—for example, LIFO, or Last in, First Out Warehousing. While the exact order differs with this kind of rule, it acts more or less the same as FIFO, and all the preceding points about strength obtain.)
Finally, one can use an allotment rule that optimizes labor. The time it takes for a picker to process a sizable order in a large warehouse can vary greatly, depending on how efficient his or her picking path is, how easy it is to find exact items, and a number of other factors. Indeed, a pick that takes 30 minutes when done efficiently can take a few hours when not done efficiently.
A labor-optimized allocation rule simply states that pickers need not worry about the specific lot or space that an item is picked from. This allows warehouse software to define the picking path with the greatest efficiency, saving travel time. Labor-optimized allocation overcomes the greatest weakness of FIFO—inefficiency—but at the cost of controlling which specific lots employees choose from.
Mixing, Allocation, and Exceptions
Again, mixing rules and allocation rules are not the same thing, but they do constrain each other. For example, if one has a strong FIFO rule, picking in accordance with that rule requires knowing which locations contain which POs, ordered by lot date. If one’s mixing rules did not specify separating inventory in this way, the data is essentially “lost” and the strong FIFO rules cannot be accommodated.
This might make mixing and allocation rules sound as if they are fixed and set in stone. This is not the case. For example, suppose a company ships goods both nationally and internationally—cosmetics, say. For the most part, these cosmetics have a long shelf life, especially when compared with the velocity with which they are ordered. And they are rarely if ever recalled. Thus, the warehouse manager chooses a labor-optimized allocation rule for national shipments.
When it comes to international shipments, however, the manager needs to account for a long overseas voyage. He or she will want to ensure that the warehouse is sending fresher batches of cosmetics overseas. In that case, an “exception” is needed that specifies that international orders be picked according to LIFO warehousing instead of in a labor-optimized way.
Exceptions can be based on any aspect of an order, including time. A seller of paper, for example, might choose a labor-optimized allocation rule, because most paper stock varies little from lot to lot and there are no recalls. Still, heat, humidity, and time will cause paper to curl, yellow, dull, or otherwise lose its quality. So the paper seller might specify that, for one quarter out of the year, stock is to be fulfilled using a FIFO rule. This way, labor is optimized most of the year, but older stock gets moved before quality suffers.
A Final Word
Most warehouse mixing and allocation or fulfillment rules are simply a matter of organizing activities according to available information. Efficiencies can be found only if that information is recorded and tracked. These days, doing all three of these requires a robust software solution.
There is an added bonus to automating storage and fulfillment, however: The ability to get a true picture of the cost of goods sold. Prices on goods vary, as does the cost of storing and insuring them. All too often, reports underestimate the cost of procuring, storing, and shipping goods. This can give rise to illusory profits and misinformed business decisions.
This means that rules for mixing and allocation are not just technical details about the storage and fulfillment of goods. They are an important part of doing business. Not only are they used to maintain the balance between efficiency and information, they dictate what kind of information is available for making important business decisions.