A distribution network design case asks where a company should locate warehouses, distribution centers, or hubs, and how goods should flow between them, to hit a target service level at the lowest total cost. The core method is mapping demand and cost-to-serve by region, testing a small set of network configurations (centralized vs. regional, hub-and-spoke vs. point-to-point), and comparing each on total landed cost against delivery speed — not on facility count alone.
Distribution network design cases sit at the intersection of two questions that pull in opposite directions: how do we serve customers faster, and how do we keep facility, inventory, and transportation costs down. A client rarely asks “should we build a warehouse” — they ask “our costs are rising” or “delivery times are hurting us competitively,” and the network design is the lever underneath both. Based on our analysis of operations case prompts, this case type shows up whenever a company is scaling into new regions, absorbing an acquisition’s footprint, or facing pressure to compress delivery windows against e-commerce competitors.
Why Network Design Cases Are Different from Generic Operations Cases
A standard operations case asks you to find and fix a bottleneck in an existing process. A network design case asks you to choose between fundamentally different structures — and the right answer depends on trade-offs that don’t resolve with more analysis, only with a clear view of what the client is actually optimizing for.
The central tension: fewer, larger facilities reduce fixed costs and inventory duplication but increase average shipping distance and delivery time. More, smaller facilities cut delivery time but multiply facility overhead and split inventory into smaller, less efficient pools. There’s no configuration that wins on both dimensions simultaneously — the case rewards candidates who quantify the trade-off rather than defaulting to “more warehouses closer to customers,” which sounds intuitively better but is rarely the cheapest answer.
flowchart LR
A[Map Demand by Region] --> B[Define Service Level Target]
B --> C[Test Network Configurations]
C --> D[Calculate Total Landed Cost per Configuration]
D --> E[Recommend Configuration + Transition Plan]
Step 1: Map Demand and Current Cost-to-Serve by Region
Before proposing any network change, establish where demand actually sits and what it currently costs to serve each region. Ask for order volume by geography, current facility locations, and a rough breakdown of transportation, warehousing, and inventory carrying cost per region.
In our experience coaching candidates through this case type, the most common early mistake is jumping straight to “we need a warehouse on the West Coast” before establishing whether West Coast demand is large enough and concentrated enough to justify a dedicated facility, or whether it’s better served by faster transportation from an existing hub.
Step 2: Define the Service Level Target
The service level target — typically expressed as delivery time (same-day, next-day, 2-day) or fill rate — determines which network configurations are even feasible before cost enters the conversation. A 2-day delivery promise nationwide implies a fundamentally different facility count than a 5-day promise, independent of cost.
| Service Level Target | Typical Network Implication | Facility Count Driver |
|---|---|---|
| 5+ day delivery | Centralized network feasible | Minimized — 1-3 national facilities |
| 2-day delivery | Regional distribution centers required | Moderate — facilities within ~500 miles of major population centers |
| Next-day / same-day | Dense local network or micro-fulfillment | High — facilities within delivery radius of dense demand pockets |
Clarify this early. A candidate who optimizes purely for cost without confirming the service level constraint will propose a network that’s cheaper but fails the client’s actual competitive requirement.
Step 3: Compare Hub-and-Spoke vs. Point-to-Point vs. Decentralized Regional Models
Three structures cover most distribution network cases. Each has a distinct cost and speed profile.
| Model | Structure | Best For | Trade-off |
|---|---|---|---|
| Centralized hub | One or two large national facilities | Low-velocity, high-margin goods with flexible delivery windows | Lowest facility and inventory cost; longest average delivery time |
| Hub-and-spoke | Central hub(s) feeding regional spokes for last-mile | High-volume goods needing 2-day service nationwide | Balances cost and speed; adds a transfer step and coordination complexity |
| Decentralized regional | Multiple full-service regional DCs, each stocking full assortment | Next-day/same-day promises, bulky or perishable goods | Fastest delivery; duplicates inventory and overhead across every facility |
A hub-and-spoke model is the default recommendation in most cases because it captures most of the cost efficiency of centralization while meeting a 2-day service target — but the right answer still depends on product characteristics. Perishable goods or bulky items with high shipping cost per unit push toward decentralized regional facilities, since the transportation cost saved by shipping locally outweighs the extra facility overhead.
Step 4: Calculate Total Landed Cost, Not Facility Cost Alone
The single biggest scoring differentiator in this case type is whether the candidate compares configurations on total landed cost — facility fixed cost, inventory carrying cost, and transportation cost combined — rather than any one component alone.
Total landed cost = Facility fixed cost + Inventory carrying cost + Inbound transportation + Outbound transportation
For example, consolidating five regional warehouses into two hub facilities might cut facility fixed costs by $8M annually and reduce total inventory carrying cost by $3M (less duplicated safety stock), but increase average outbound shipping distance enough to add $6M in transportation cost. The net saving is $5M — real, but far smaller than the headline facility-cost reduction alone would suggest, and a candidate who stops at “we cut facility costs by $8M” has materially overstated the case.
Common Pitfalls
- Optimizing facility count in isolation. Fewer facilities almost always looks cheaper on a slide showing only fixed costs — the case is testing whether you catch the transportation cost that rises to offset it.
- Ignoring the service level constraint. A network that’s 20% cheaper but pushes delivery from 2 days to 4 days may violate a competitive requirement the client can’t actually trade away.
- Treating all demand as uniform. A client with 60% of volume concentrated in three metro areas has very different optimal network math than one with demand spread evenly across fifty markets — always ask for the demand distribution before proposing a structure.
- Skipping the transition cost and timeline. Closing or opening facilities involves lease exits, severance, inventory transfer, and requalifying carriers — a strong answer flags that the payback period on network changes is often 12-24 months, not immediate.
- Forgetting inventory pooling effects. Consolidating facilities reduces total safety stock needed for a given service level (statistical pooling), which is a real saving many candidates miss entirely.
Sample Case Walkthrough
Prompt: “Our client operates five regional warehouses across the US and is under pressure to cut logistics costs by 15% without hurting its 2-day delivery promise. What network structure would you recommend?”
Strong approach:
Clarify: What’s the current demand distribution across regions? What’s driving the cost pressure — facility lease renewals, rising freight rates, or inventory carrying cost? Is the 2-day promise firm across all regions, or only in core markets?
Map current cost-to-serve: Suppose the five warehouses collectively cost $40M annually — $18M facility fixed cost, $12M inventory carrying cost, $10M transportation. Demand is concentrated: three regions account for 70% of volume.
Test a hub-and-spoke consolidation: Propose consolidating the five warehouses into two larger regional hubs positioned to cover the three high-volume regions within a 2-day ground-shipping radius, using third-party carriers for the lower-volume remaining regions.
Quantify the trade-off: Two hubs might cut facility fixed cost to $11M (-$7M) and inventory carrying cost to $8M (-$4M) through pooling, but raise transportation cost to $14M (+$4M) as some shipments travel farther. Net saving: roughly $7M, or about 17.5% — clearing the 15% target, but only after accounting for the transportation offset most candidates would miss.
Flag the service level risk: Confirm the two hub locations still hit 2-day ground delivery for the three core regions; if not, recommend keeping a smaller regional facility in the highest-volume outlying market rather than consolidating fully.
Address transition: Note the lease exit timing on the three closing facilities, inventory transfer logistics, and a realistic 12-18 month implementation window before the full saving materializes.
Key Takeaways
- Distribution network design cases reward comparing total landed cost across configurations, not optimizing facility count or any single cost line in isolation
- Establish the service level target before proposing a structure — it constrains which configurations are even feasible
- Hub-and-spoke is the common default because it balances cost and speed, but decentralized regional facilities can win for perishable or bulky goods with high per-unit shipping cost
- Always net facility and inventory savings against the transportation cost increase that consolidation typically causes
- Flag transition cost and timeline — network changes rarely pay back immediately
Practice Distribution Network Design Cases
This framework builds on the same supply chain fundamentals as the operations case framework and pairs naturally with supplier risk and resilience cases — the difference is that network design optimizes the cost-speed trade-off in steady state, while resilience cases optimize for disruption. You’ll find both cost-driven and network-design prompts across operations cases in our library, with the clearest examples in manufacturing and retail prompts where facility count and delivery speed are directly in tension.
Reading the framework is straightforward — defending a specific facility count and location to a skeptical CFO who wants the cheapest possible network is where most candidates lose the thread. Our AI Mock Interview offers two ways to build that instinct: Guided Mode walks you through a network design case with hints when you get stuck on the total landed cost calculation, while Expert Mode simulates the pressure of justifying a consolidation plan to a partner who keeps pushing on the transportation cost you might be underestimating. Every session scores you across five dimensions — Structure, Analysis, Communication, Business judgment, and Math — so you know exactly where your cost trade-off reasoning needed sharper numbers.
If you’d rather work through an actual network design case end-to-end at your own pace, Chat with Case walks you through a real client scenario in five stages — background, framework selection, data interpretation, analysis, and recommendation — the same structure this guide covers, but applied to a specific data set instead of a walkthrough.
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