Most DCs are operating daily with the minimum level of associates and in some cases with reduce hours. Recently, during an interview with a logistics executive, we asked a question relative to handling peaks and valleys in orders received and volumes shipped. The response we received could be characterized as wishful thinking – “Wouldn’t it be wonderful if order volume was predictable and steady?” We understood this to be a rhetorical question. But, it got us thinking.

Not only are DCs subjected to the unpredictable ebb and flow of incoming orders, but they are also subjected to a changing mix of work content for those orders. Some orders may be dominated by pallet load and/or full case quantities, while others have a high piece-picking content and, as in the case of internet orders, may be single line, or single-piece orders. In addition, many consumer orders these days require special processing i.e., monogramming, serial number capture, and gift wrapping just to mention a few. These variations challenge logistics managers to plan processes that are efficient for each, and yet can be turned on and off as the activity shifts.

Flexible Process Planning Models

Normally, MHS planners use historical data to establish capacity requirements for each separate process and then design the processes and system flows accordingly. If the data is very granular, you can spot extreme peaks in demand. But, planning each process for its most extreme peak provides considerable excess system capacity most of the time, and can be very difficult to cost justify. On the other hand, you must avoid using annual averages – that would be like the guy who drowned while fly fishing in a stream having an average depth of only 6 inches.

The following are a few planning models that can help you outsmart the variation problem:

• First, be cautious of highly automated processes whose efficiency depends on running at full speed. They may not be so efficient when volumes are low, and still have the high capital investment to amortize.

• Choose the simplest process that works well for the majority of the orders and absorb the inefficiency for the others. This can be accomplished by establishing criteria for a theoretical “design day” based on an average day during the peak order processing period (week/month in a better than average sales year) plus a growth factor bump-up.

• Create a mini-DC or independent line of flow (like a “slapper” line) for orders that have common characteristics. These might include:

o a small sub-set of the product line, single lines or single units;
o the same shipping package and/or compliance labeling requirements;
o the same carrier mode;
o workstation designed for picking and packing as a single-step process, and/or;
o a separate processing area for high-volume flow of small light weight cartons.

• In a conveyorized system, provide a path that permits flow from any process area into the main line and/or to other process areas so that changes in order process requirements or sequence are easier to handle.

• Design workstations that have the capability to perform a variety of tasks and locate them so that they are easily accessed from different points in the primary line of flow. These might be configured to support several activities. And cross-train employees so that as the mix of requirements changes, they can be quickly re-assigned to a different workstation.

A real and sometimes overlooked variable is a company’s distribution processing model in terms of shifts and overtime and the ability to “throw labor” at peak volume periods. For example, if a DC currently works one shift, how will the ability to add overtime hours during peak periods allow a DC to compensate for a process and/or system that can’t meet those volumes on a single 8-hour shift? Can weekend operations or a second or third shift be added to expand capacity?

These are key questions, because companies that are more conservative with regards to operating hours have to design their processes and systems to handle more volume variability – and, therefore, make a greater investment verses companies that expect to use overtime and additional shifts to add capacity.

Final Thoughts

No doubt managing DC variability is a major challenge. But, you don't have to be victimized by it. Careful data analysis and a little bit of well thought-out process planning can put you back in control.

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