Retail Distribution Footprint Optimization with Cosmic Frog

How a discount retail giant transformed its supply chain strategy

Who was the team behind this transformation?

A national discount retailer with over 16,000 stores and aggressive expansion goals—500 new stores annually—is passionate about solving supply chain puzzles with speed, precision, and creativity, so they partnered with Optilogic to use the Cosmic Frog supply chain design solution.

What were the business challenges?

The retailer’s supply chain team faced recurring, urgent strategic questions:

• Will our DCs run out of capacity?

• Do we need a new DC? Where? How soon?

• How do we keep up with realignments as stores grow?

• What’s the best way to respond to disruptions like a tornado-hit DC?

• Can we reduce network cost without impacting service?

They needed a faster, more flexible way to answer these questions—ideally before leadership even asked.

What tools were they using before?

Their legacy network design tool was powerful but outdated, slow, and poorly supported. Scenario building could take days, collaboration was clunky, and the manual workload was burning out the team.

“I’ve had people come to me saying, ‘I can’t do this anymore.’ That’s how broken the system was,” said the network planning director.

Why did they switch to Cosmic Frog?

After evaluating eight tools and piloting three, the team selected Optilogic’s Cosmic Frog for its:

• Scenario efficiency – Rapid “what if?” testing and reuse of scenario items.

• Collaboration – Team members could work in parallel on one model.

• Manual work reduction – Automation cut manual data edits by 80–90%.

• Improved quality – Less rework, fewer errors, better QA processes.

• New capabilities – Like store count constraints, user-defined step costs, and dynamic cost-to-serve analysis.

How did their process change?

With Cosmic Frog, they built annual models using Alteryx and refreshed them regularly. They no longer needed to rebuild models from scratch for every scenario. Instead, they:

• Created a production baseline model from FY23 data

• Layered on scenario items to test changes

• Ran 600+ iterations in hours—not weeks

• Visualized results immediately using Tableau or SQL-connected dashboards

What use cases did they tackle?

Strategic: Long-Term Network Roadmap

• Greenfield DC placement based on 5-year demand forecasts

• Sensitivity analysis for DC capacity and store growth

• What-if analysis for M&A, new markets, or macro disruptions

Tactical: DC-to-Store Realignment

• Biannual updates to optimize which stores are served by which DC

• Event-based modeling: emergency closures, acquisitions, or surge volume

“We used to take 4–6 weeks to deliver results. Now it’s one week.” – network planning director

What was the impact on team productivity?

Before:

• Hours of Excel edits

• Manual model copies

• Rebuilding scenarios from scratch

After:

• 80–90% reduction in manual effort

• Real-time team collaboration

• Easy tracking and comparison across scenarios

“We’re not just modelers anymore. We’re strategic thinkers,” said the company’s supply chain network modeler.

What metrics and visuals guided decision-making?

Using Cosmic Frog’s scenario engine and visualization tools, they analyzed:

• Cost-to-Serve per store, including:
  • Ocean freight
  • Drayage
  • DC fixed cost
  • Outbound transportation
  • Unload and handling costs

• Causal cost analysis – Understanding what caused a change (e.g., a servicing DC switch)
• Geographic heatmaps to identify “red” (high-cost) stores

How did they drive internal buy-in?

Buy-in took time—two years to be exact. The team:

• Started by building trust with transportation and inbound partners

• Brought visibility to sourcing trade-offs, like port-of-entry decisions

• Created checkpoints for input validation and QA

• Integrated output into long-range planning and stakeholder presentations

“People feel threatened when they don’t understand the data. We worked hard to make them part of the process.” – Network planning director

What’s next for the team?

They’re now exploring Optilogic’s Hopper transportation optimization to:

• Optimize routing and reduce manual realignment

• Automatically reassign 400–500 edge-case stores per cycle

• Connect strategic modeling with tactical execution for faster rollout

Key Takeaways

• Replacing legacy tools unlocked speed, quality, and capacity

• Scenario-based design enabled smarter decisions—fast

• Cost-to-serve metrics guided impactful changes

• Collaboration, flexibility, and transparency built trust across the org

According to the retailer’s supply chain network modeler, “Now when something breaks or a new idea comes up, we already have the answer—or we’re modeling it.”