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About the Author

Cliff Holste is Supply Chain Digest's Material Handling Editor. With more than 30 years experience in designing and implementing material handling and order picking systems in distribution, Holste has worked with dozens of large and smaller companies to improve distribution performance.

Logistics News

By Cliff Holste

May 6, 2015

Logistics News: Building A Comprehensive Data Base – First Step In Planning For DC Upgrade

Developing & Maintaining an Operational Data Base Speeds-up Planning Process

Holste Says:

In the hands of an experienced system planner/developer, the company's historical data base is the best indicator of what types of handling equipment and automation technologies will be needed to accomplish the stated objectives.
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Previous Columns by Cliff Holste

Sorting It Out: Shippers Looking To Increase System Capacity Are Surprised To Find It May Already Exist!

Sorting It Out: For Shippers - Benefits Of Real-Time Control In The DC Are Huge!

Sorting It Out: Shippers Looking to Improve Operations Choose Customer Centric Approach

Sorting It Out: Productivity is a Crucial Factor in Measuring Production Performance

Sorting It Out: Packaging Construction Impacts on Logistics Operations


Trade shows, like this year’s ProMat 2015 and next year’s MODEX 2016, always generate high interest in new logistics automation and materials handling technologies among progressive companies. Of course, that is exactly what the show organizers and exhibitors hope for.

Unfortunate, few companies have the data needed to begin the planning process. In some cases, data is available, or largely available, but spread over the various departments making it difficult to build an inclusive and comprehensive data base. In other cases, data is not available or is very hard to get at. Examples include: weights and dimensions of active SKUs (referred to as the “Product Item Master”), weekly sales activity for each SKU (referred to as the “SKU Velocity Report”), and order-line item detail (referred to as the “Customer Order Profile Report”).

The magnitude of the data collection effort can be, and often is, overwhelming. Usually, internal resources are just not available to build a proper planning data base in a reasonable timeframe.

Basic Planning Data Needed for Planning an Order Fulfillment System Upgrade

To determine appropriate picking strategies and automation technologies that could improve operations, companies need to be prepared with the following types of data:

Product Item Master

Regardless of the scope of a project being considered, if it involves conveyors, picking and sorting equipment, system planners need to know the details of what’s being handled. This is often referred to as a Product Item Master. Every product or SKU is identified along with it physical characteristics (length, width, cube, weight) and any special handling issues (fragile, top heavy, plastic wrapped, etc). Collecting all and properly organizing all this detail for every SKU can take some considerable amount of time and effort. Dimensioning and Weighing Systems, such as CubiScan ( ), are economical to purchase of rent, and will greatly speedup the data collection process.

Once a company has an up-to-date and comprehensive Product Item Master it can then focus on developing a Customer Order Profile Report.

  • Order mix distribution (family mix, handling unit, order increment)
  • Lines per order distribution
  • Cube per order distribution
  • Lines and cube per order distribution

With this information a SKU velocity profile, similar to the chart below, can be constructed which is an important “starting point” for understanding the company’s business profile.

SKU Velocity Report:

Every product that is listed on the Product Item Master has a sales movement (velocity) history. System planners must have this critical information in order to determine the size and capacity of the material handling equipment and the picking/shipping system. This date is then analyzed and summarize by category as shown in the following chart:

Planning will then typically involve breaking this data down into movement volumes by different handling/picking units, such as full pallet, full case and split case activity. Marketing channels will also impact on planning.

The next step is an iterative process constrained by many factors, especially if the planning is for an existing operation rather than a new or “green field site”. It may also be that a company wants to look only at one area, such a piece picking.

A comprehensive planning effort will include analyzing where groups of products will be stored, in what type of storage mode (bulk, high-density, active pick face, etc.), and what picking strategy is best suited for the various SKUs (Batch-Order Picking or Product-to-Person) will be most beneficial.

The analysis can get very sophisticated; for example, in some businesses it makes sense to look at items that are almost always ordered as single line items, and store those in a separate area of the DC. It can also pay to look at SKU/Order relationships – a relative slow moving SKU might make sense to slot in the high-velocity picking area, if when it is ordered, it almost always is ordered with a Category “A” product.

With today’s advanced 3D computer simulation and emulation programs, modeled equipment and systems behave credibly so models operate realistically from the start providing a clear, sharp vision of the proposed solution. Because the models are data driven they are useful in proving system behavior under different operating conditions before finalizing the design.

Key takeaway points:

Order picking system decisions are closely tied to location and storage mode decisions.

Different order picking technologies are best suited to customer order profiles and SKU velocity.

Different order picking technology investments will have different ROIs depending on the level of activity.

Analysis and planning must be “adjusted” to include future marketing channels as well as emerging consumer trends.

Computer simulation of proposed solutions reduces design errors and omissions.

Final Thoughts

The importance of having a complete and accurate planning data base cannot be overstated. In the hands of an experienced system planner/developer, the company’s historical data base is the best indicator of what types of handling equipment and automation technologies will be needed to accomplish the stated objectives. Proper interpretation of the data reduces the risk of over or under sizing the scope of the project – either of which can result in a costly mistake.


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