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Batching, Waving, and Other Ways to Increase Picking Productivity

Working within the distribution practice of an independent engineering consulting firm, the most common question that we are asked by our clients is, “What can we do to increase picking performance?” Sometimes it is within the context of finding the best new technology to invest in, and sometimes new capital equipment is out of the question. Regardless of the context, we’ve found that understanding a client’s flexibility to wave orders and SKU-type affinity patterns on orders, or lack thereof, is one of the most effective ways to increase an operation’s performance through improved waving, zoning, and batching.

Theoretically, it is pretty easy to grasp that if you are batch picking, pick density will increase with wave size. It becomes a little more difficult to estimate the pick density impact when you also take into consideration other complex, interconnected operational factors. A good approach to developing tangible technology or operating concept solutions will consider how a given operation’s order profiles react to four key variables:

  1. Candidate Order Pool. It is important to understand the flexibility, or lack of flexibility, to select orders to create waves based on the candidate order pool size across an active day. This will be impacted by order arrival patterns, service level requirements, dispatch times, and IT restrictions.
  2. Wave Size. What incremental improvement is possible to effectively group orders together as wave size increases? Alternately, what are the smallest possible waves that can be supported without giving up significant performance improvements? Often, smaller waves can allow for more waves to be run concurrently, minimizing utilization penalties for wave ramp-up ramp-down. Smaller waves can also frequently reduce capital expense (e.g., you may need a smaller sorter).
  3. Zoning Strategy. Zoning strategy is one of the more powerful levers to improve the flow of work through the building. Zoning strategy evaluation should consider:
    • The number of zones (4, 8, 20, etc.).
    • The zone size profile. Often there are unique zones in an operation (e.g., larger or smaller zones to accommodate fast or slow movers, floor or cart pick zones outside of a pick module, etc.). One benefit of taking a scenario modeling approach to solution development is that it is easy to test many different strategies to find the highest performing or most flexible solution.
    • SKU zone assignment strategy (considering if zones should be assigned by product type, velocity, random, etc.).
    • Hardbreaks (how picking activity can be routed between zones or zone hardbreaks impacts logical work assignment, as well as layout and technology application).
  4. Picking and Consolidation Strategy. Scenarios can be tested to measure the performance of discrete order picking, cluster picking, batch picking to small batches (e.g., putwall), or batch picking to large batches (e.g., unit sorter). Performance can be compared between strategies, as well as cluster picking batch sizes and consolidation batch sizes.

Evaluating changes to these four key variables through data-based scenario modeling allows for an understanding of the specific improvements that can truly impact an operation’s performance, while also considering the complex patterns in an operation’s order and SKU profiles.

It is possible to find improvements to an existing operation by locking in variables related to existing capital equipment or IT restraints (e.g., sorter size) and focusing on variables that you can change. Alternatively, it is possible to model the system “wide open” to measure improvements possible with big changes or investment, such as introducing a shuttle system, AMRs, or a unit sorter. Either way, it is important to conduct an evaluation that tests an array of values for each of the four key variables, as is appropriate within any established design criteria, to understand the benefit curves of making changes. Often, when there are changes to wave size, picking cluster batch size, or order batches for consolidation, you will find diminishing returns as sizes continue to increase.

A good understanding of how performance changes with different wave sizes, zoning strategies, or pick and consolidation concepts allows for impactful existing operation recommendations. It also results in a much more effective selection of new technologies that meet physical requirements and enable the most efficient, logical work plan.

— Jason Gryszkowiec, St. Onge Company


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