Illustrative scenario

From Months to Weeks: AI Agents for DC Footprint Rationalization and Network Scenario Modeling

Supply chain network decisions — which DCs to open, consolidate, or close — are among the highest-stakes CapEx choices a national omnichannel retailer makes. For a VP of Network Strategy facing a $3M–$20M modeling engagement, the constraint isn't access to data or analytical frameworks. It's the time it takes to run, validate, and present enough scenarios to actually inform a $50M+ CapEx decision with confidence.

Up and running in ~18 wkFor: VP Network Strategy, national omnichannel retailer
Estimate your payback
~6 mo
Payback period
$8M
Est. savings / year
+$4M
Year-1 net

Rough estimate — change the numbers to match your business. We scope the real figures with you on a call.

Why Network Studies Take So Long

Llamasoft and Coupa Supply Chain Guru are powerful platforms, but generating meaningful scenario comparisons requires iterative model runs, parameter adjustments, and sensitivity analysis that accumulates into weeks of analyst time per iteration cycle. Strategy teams typically run far fewer scenarios than the business needs because each one is expensive to produce. The result is a board presentation built on three or four scenarios when the decision really warrants fifteen — and executive questions that arise during the presentation often can't be answered until the next model cycle.

Agents That Run Scenarios at the Speed of the Question

An AI Labor Company agent mines network strategy conversations from Llamasoft and Coupa SCG model-run results and strategy team email threads, then deploys agents to evaluate DC open/close scenarios against 2–5-year demand forecasts using live transportation and facility cost parameters. Sensitivity tables are generated across demand, cost, and service-level assumptions. Executive scenario comparison decks are produced automatically as model runs complete. The VP Network Strategy approves each scenario package before it goes to board presentation — the agent compresses the production cycle, not the decision accountability.

The Value: A Faster, Better-Supported Decision

The primary payoff here is strategic, not operational. A 3-month faster decision cycle on a $50M+ CapEx question has real financial value — earlier DC openings that support revenue, avoided carrying costs on deferred decisions, and the ability to respond to demand-signal changes before the network plan is locked. Agents in this configuration typically reduce modeling cycle time by 30–50% and can be live in roughly 18 weeks. The board walks into the presentation with more scenarios, better sensitivity coverage, and answers to questions that haven't been asked yet.

Questions

Does the agent replace the supply chain consulting firm doing the study?

It depends on the engagement structure. The agent handles scenario modeling, sensitivity analysis, and deck production — work that typically represents a significant share of consulting hours. For some programs it supplements an existing consulting relationship; for others it can support an in-house team running the study directly.

What if our Llamasoft models have non-standard parameters or custom cost structures?

The agent is configured against your actual model parameters and cost structures. The output scenarios reflect your real network cost drivers, not generic industry benchmarks.

Related use cases

Illustrative scenario for logistics, transportation & field ops. Figures are example ranges, not guarantees — we scope real numbers with you on a call.

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