This case study is an illustrative example based on publicly available information and system-level modeling. Hylios did not work with IKEA on this analysis, and nothing here describes IKEA's own decisions or results.
A retailer such as IKEA weighing several candidate sites for new pickup locations faces a familiar problem. Traditional analysis takes months and struggles to compare the network-wide implications of each option. This example shows how a digital twin would model the trade-offs so a team can bring a confident recommendation to leadership.
The Challenge
The decision balances competing priorities across multiple dimensions:
- Cost: real estate, operations, transportation
- Service: customer access, delivery times
- Network impact: existing distribution center utilization, routing changes
Spreadsheet analysis struggles to capture the interconnected effects of adding a new node to an existing network. Every location choice creates ripple effects that are hard to quantify by hand.
The Approach
With Hylios, the analysis starts from a digital twin of the distribution network that models:
- Current distribution centers and pickup points
- Customer demand patterns by region
- Transportation costs and lead times
- Candidate site locations and capacities
Testing Scenarios
The analysis runs several scenarios for each candidate location:
- Baseline network performance
- New location addition with demand redistribution
- Impact on existing DC utilization
- Transportation cost changes
- Service level changes by region
What the Model Shows
A digital twin used this way lets a team:
- Compare all options side by side with transparent assumptions
- Quantify trade-offs between cost and service for each location
- Understand network effects that are not obvious in static analysis
- See where every number came from, public data, the team's own files, or a stated assumption
The model also stays useful after the first decision: the same twin can answer the next location question without starting over.
Key Insights
- Location decisions are not just about local factors; network effects matter
- Testing scenarios quickly makes for better stakeholder discussions
- Transparent assumptions build confidence in the recommendation
Try It on Your Network
This example uses a well-known retailer to show the method. To see it on your own network, book a working session or start with the network diagnostic.