The Dangers of Data Generalization in ASRS Design

Automated storage and retrieval systems (ASRS) offer numerous benefits for warehouses and distribution centers in many industries. A well-designed system can improve the accuracy and efficiency of order picking, increase safety, address labor constraints and maximize space utilization. At the same time, a poorly designed system may not meet productivity requirements or deliver an adequate return on investment (ROI).

What makes the difference between an effective and an ineffective ASRS design? Data.

When the design and sizing of the ASRS is driven by detailed historical and operational data, the completed system will use the most appropriate type and quantity of technologies to meet throughput requirements.

Three Data Generalizations to Avoid

Data collection and analysis can be complex and time-consuming, so it may be tempting to generalize the data. However, this presents a number of problems:

1. Looking at a subset of the data may lead you to select the wrong technology. When designing an ASRS, many companies tend to think in terms of throughput, but you can’t focus on that one data point alone. Likewise, it’s not enough to look at the number of stock keeping units (SKUs) independent of volume or vice versa. Rather, you have to consider all of the variables that could affect the operation of the system to select the most efficient technology for your needs.
2. Using less than a full year of data may result in a system that is too small or too big. Most companies experience cyclical demand, so averaging data from one or two months won’t produce reliable information. If you size the system based on data from a slow period, the ASRS will be too small and unable to keep up with demand year-round. If you size it based on data from a peak period, it will be too big and unable to deliver the necessary ROI. It may even be too expensive to build.
3. Basing decisions on past needs may prevent you from designing the optimal solution. Though you may already be familiar with some ASRS technologies, what was right for your last project may not be ideal for your current needs. Starting with the data allows you to consider all possible options and choose the one that is best for your current product mix, order volume and throughput requirements.

When You Don’t Have Enough Data

In a perfect world, every ASRS project would begin with a complete and detailed data set. Sometimes, though, comprehensive data simply isn’t available. This is where experience comes in. Skilled ASRS designers can use their knowledge of other systems to fill the gaps in your data as much as possible. For example, if you know your SKU count, but don’t have the sizes and weights of those SKUs, an experienced ASRS designer might organize the data into categories to size the different systems each item would go into.

Nonetheless, whenever you calculate data there is always the chance you didn’t make the right assumptions. That’s why it’s critical to collect as close to a year’s worth of data on all aspects of your process as possible, even if the data set isn’t 100 percent complete.

Learn how data drives the design and sizing of ASRS solutions.