Identify Capacity in Your DC | FORTNA

Leveraging Data to Identify Capacity in Your DC

Using data-driven analytical tools, FORTNA shows two different approaches to identifying capacity and gaining key insights into a distribution center’s operations and throughput.

by Darren Jorgenson

The supply chain industry continues to evolve and change and many organizations are looking for continual optimization to their processes and operations.

As e-Commerce and customer demand continue to grow, supply chain professionals will need to determine if their current capacity and throughput levels will be enough for 2023 and beyond. While some might believe that capacity is a simple calculation of available floor and vertical space, true capacity is a complex analysis that addresses an organization’s distribution center network, physical space, layout, throughput, reserve storage and utilization.

In this blog, we will highlight two approaches to help companies identify and adjust capacity and throughput within their current operations as well as plan for the demands of the future.

A Top-Down Approach

A national grocery outlet with 29 warehouses at 14 separate locations needed to analyze physical space usage as well as what their true capacity would be as they planned to add more shifts to keep up with demand. Another part of this study was to see if capacity could be extended at key locations to support growing business service targets.

Using a top-down approach, FORTNA data scientists examined the data from all facilities noting specific use warehouses (chilled, cold and normal use). Analysis was completed based on volume, shifts, stock turns and docks. Using the FORTNA proprietary process, algorithms and data science models, they were able to discover the following:

  • Current throughput and capacity per group of warehouses
  • Potential throughput and capacity per group of warehouses

Doing a deeper dive into the data revealed that there was an opportunity to reclaim additional capacity within the facilities, current sites and layouts. The most interesting discovery was the wide variation of utilization. The data revealed that certain locations had been over utilized by as much as 21% and other locations underutilized by more than 30%. In fact, eight of the warehouses were found to have double-digit spare capacity.

Armed with this new data, the grocer was able to evaluate the crucial questions below and make data-based decisions.

  • Was higher utilization paired with higher transportation costs at some locations a better approach than extending or adding infrastructure elsewhere in their network?
  • How far could the life of their facilities be extended based on optimal throughput?
  • Was their network truly positioned for expected growth?

A Bottom-Up Approach

A global retail third party logistics (3PL) company had the need to increase current capacity and throughput in its existing operations while a new facility was 2 years away from opening. The 3PL’s intention was to optimize the facility and understand what operational, process, design and automation levers could be pulled to extend the life of the building while maintaining or improving the cost per unit.

Using a bottom-up approach, FORTNA assessed the 3PL’s current processes in storage, picking and shipping. The goal of the data analysis was to understand current operational capacity, as well as provide key insights for future capacity and throughput.

By focusing on processes and storage media instead of footprint and physical capacity, the FORTNA team of data scientists discerned the following:

  • 70% of volume was picked in 10% of the locations; 85% of the volume was picked in 30% of the locations
  • 15% of total SKUs moved only once every 6 months
  • 60% of the orders were single line/single unit orders
  • Picking during peak times was creating congestion
  • Some automation was creating bottlenecks and affecting productivity
  • The shipping area was limited due to associate and equipment traffic



By collecting the baseline data, FORTNA was able to compare business service requirements and future needs of the 3PL to offer several options that could help gain more capacity. Some of the recommendations included:

  • Create fast-moving area for the 60 SKUs that represent 15% of the volume (this creates faster replenishment and a substantial increase in productivity)
  • Begin picking sequence in fast moving area
  • Switch to a batch picking methodology
  • Create single unit order packing station

These recommendations helped the 3PL not only create the needed capacity but reduce future labor needs by increasing the productivity of its current labor pool. The investment to make these changes was also minimal as they did not rely on any new automation or software.


Need to find capacity in your operations?

FORTNA and its team of data scientists can help. Doing a deep dive into operational data helps gain a thorough understanding of business data, opportunities and needs. Proprietary tools, process and algorithms allow FORTNA to quickly extrapolate meaningful insights from data and begin developing solution concepts that balance costs and services without a bias towards a particular supplier or technology.

Contact us today and discover how we can help increase your capacity and throughput.

About the author


Darren Jorgenson

Practice Lead, Global Strategy

Darren Jorgenson is the Global Strategy Practice Leader for FORTNA and has been in the industry for 20+ years, serving in multiple industries and consulting roles. Darren is a member of the Council of Supply Chain Management Professionals and has been recognized as a Pro to Know by Supply & Demand Chain Executive magazine.