Case Study: IKEA U.S. Store Expansion
Selecting the Next Store Location Using System-Level Trade-offs
This case study is an illustrative example based on publicly available information and system-level modeling. Hylios did not work directly with IKEA on this analysis.
Overview
This case explores how a system-level digital twin can be used to evaluate store and pickup location decisions for a large national retailer like IKEA.
Rather than optimizing a single site in isolation, the analysis focuses on understanding how a new location affects the broader supply chain network.
The Challenge
- Select the best location for a new store or pickup point in the United States
- Evaluate multiple candidate locations with competing trade-offs
- Avoid local optimization that could increase total cost to serve elsewhere in the network
In large retail networks, location decisions often have second- and third-order effects across transportation, inventory, and service levels that are difficult to quantify upfront.
The Approach
A system-level digital twin was used to model a representative U.S. distribution network and simulate the impact of adding new locations.
The model evaluated:
- Cost to serve across the network
- Transportation and fulfillment impacts
- Annualized financial outcomes
Multiple candidate locations were tested using consistent assumptions to enable direct comparison.
The Result
The model identified Boise, ID as the recommended location over an alternative in Alabama.
Key outcomes included:
- 0.73% reduction in total cost to serve
- $3.1M in annualized savings
- Clear visibility into why the recommended option outperformed the alternative
Insight
The analysis demonstrated how decisions that appear similar at a local level can produce materially different outcomes when evaluated across the full network.
Takeaway
This example highlights the value of decision-first digital twins in location strategy, where small percentage improvements at the system level translate into meaningful financial impact.