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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

February 15, 2012

Whether Planning A New DC or Upgrading Existing Operations -- Product And Sales Data Drive Automation Strategies & Technologies

Maintaining A Comprehensive and Accurate Data Base Speeds-up The Planning Process

Based on the amount of activity at MODEX 2012, it is apparent that many companies are giving some serious thought to upgrading their DC operations. Seeking a bigger “bite-of-the-apple” they are looking to launch new marketing and sales programs that will impact on current order fulfillment operations.

Holste Says:
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In the hands of an experienced systems planner/developer, the company's "adjusted" 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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What is not so apparent (to many of these companies) is that they will need to become much more intimate with the critical data that drives order fulfillment strategies and automation technologies. They must become aware of what data is needed, what data is available within their business systems, and how accurate that data is. Another critical question in that regard is – does the company’s historical data base represent what future activity will be? If not, how will it be different?

The unfortunate truth is that few companies seem to have the basic data needed to begin the planning process - even in this age of ERP, WMS, data warehouses and other business software systems. In some cases, the data is available, or largely available, but spread over different operating systems. However, the data from these different systems may not be easily consolidated.

In other cases, key data is not available or is very hard to get at. Examples of the former might include product dimensions and weights; examples of the latter might include order line item detail, which can be obtained, but only after a time consuming effort by the IT department to extract and format it.

 

Basic Data Needed for Order Fulfillment System Planning

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

 
  • 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 companies business.

Example SKU Velocity Profile:

Category

# of SKUs

Percent

Order Lines

Percent

A – Very Fast

711

5.7%

1,728,000

54.0%

B - Fast

1,834

14.7%

940,800

29.4%

C - Medium

2,969

23.8%

384,000

12.0%

D - Slow

4,641

37.2%

147,200

4.6%

F – Dead/Obsolete

2,320

18.6%

0

0.0%

Totals

12,475

100.0%

3,200,000

100%


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.

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 (Person-to-Product 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 store in the high-velocity area, if when it is ordered, it almost always is ordered with a Category “A” product.

The key points:
 
  • Order picking system decisions are closely tied to location and storage mode decisions.
  • Different order picking technologies are best suited to SKU velocity profiles for a given unit of measure.
  • Different order picking technology investments will have different ROI depending on the level of activity within a pick zone. SKU and order activity profiling is essential to optimizing these decisions.
  • Analysis and planning must be “adjusted” to include future marketing programs such as the launching of an eCommerce sales channel as well as emerging consumer trends.


Final Thoughts

The importance of having a complete and accurate SKU/order data base cannot be overstated. In the hands of an experienced systems planner/developer, the company’s “adjusted” 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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