When companies implement a Warehouse Management System (WMS), the primary payback they receive typically comes from improved labor productivity, and wave management functionality is often critical to generating that productivity. Wave management is based on intelligently grouping a batch of warehouse activities together for later release to the floor. This batch of work (or “wave”) will be executed concurrently in a particular set period of time.
When I think about wave management, I always think about creating waves of picking activities. But in a recent briefing with Manhattan Associates (an ARC client) about its wave management capabilities, they reminded me that there are different types of waves and picking is just one of them. The Manhattan solution also supports routing and replenishment waves. Further, you can break pick waves into a smaller type of wave—i.e., pack waves. My goal today is to describe these different types of waves.
Routing waves often occur prior to pick waves when the wave engine determines how outbound orders should be shipped—e.g., by parcel or less than truckload (LTL), and which carrier to use. To a certain extent, this functionality overlaps with the functionality in a transportation management systems (TMS). However, consumer goods companies serving large retail clients often must use routing guides – i.e., they must follow the retailer’s guidelines on how to tag and ship their purchased goods. For example, the routing guide might say, “If you are shipping garments to our Missouri DC, and the weight is less than 500 pounds, ship parcel, and use FedEx”. This routing guide logic is located in the WMS, not a TMS. The wave engine determines how many parcel and LTL pick-ups there will be on a given day (or week) based on the routing guides, what dock doors will be used, and schedules the dock doors. In this scenario, routing is independent and picking is dependent. Once the routing wave determines that a FedEx truck will show up at Dock 4 on Tuesday at 3 pm, the wave engine can determine how to do the picks associated with that shipment.
Because picking is generally the most labor intensive activity in a warehouse, pick waves are the most important type of wave. You can generate pick waves based on a group of “Ship To” locations (like stores in retail), customer (pick all Walmart orders in this wave), order prioritization (after 3 pm, only pick and ship high priority orders), product attributes (pick dresses in one wave, accessories in the next), or other parameters. A pick wave generally consists of 45 minutes to 2 hours worth of work.
Pack waves are a subtype of pick waves that honor the constraints of material handling equipment. For example, once garments are picked, they may need to be placed on a conveyor where they will move to a bomb bay chute or tilt tray. These sortation systems have throughput constraints. In this instance, pick waves are broken into smaller pack waves that honor these material handling constraints.
Replenishment waves often occur in high-throughput facilities that need to regularly move goods from bulk storage to forward pick locations. These waves are often linked to the picking waves. So after a pick wave is constructed, the replenishment wave ensures that the necessary inventory to support the picking will be in the forward pick locations. Replenishment waves are particularly important in DCs that have more stock keeping units (SKUs) then forward pick slots. In these warehouses, a slot is filled with a SKU to meet the needs of a pick wave, and then filled with a different SKU to meet the needs of the next pick wave. This form of replenishment wave is dependent on the logic of the pick waves.
But in smaller throughput facilities, replenishment waves can be independent of a particular pick wave. For example, an analyst might look at next week’s orders, determine where SKUs should be slotted in the forward pick areas and what the min/max levels at an individual picking location should be, and then create a wave of work at the beginning of the week to set up the forward pick locations.
In conclusion, waving improves labor productivity, particularly if the wave engine groups work in a manner that allows for reduced travel paths.
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