Agentic supply chain planning · live
Supply chain optimization. Without the OR team.
LogiModel's agents formulate, solve, and explain the decisions ecommerce, hyperlocal, and 3PL operators run every week: driver staffing, fulfillment placement, SKU replenishment, carrier allocation, channel spend, markdown timing. You make the call. The agents and the solver do the math.
What changes
Outcomes worth a Monday meeting.
What ecommerce, hyperlocal, and 3PL operators see when an agent formulates the network problem, the solver runs it, and a baseline-vs-optimized comparison lands on Monday. Every week.
What the agents run for you
Eight supply chain decisions, one workbench.
Every one is a real supply chain optimization problem: SLA-bound, capacity-capped, and quantifiable in dollars. Most companies staff an OR team to model them. We hand them to an agent and a solver, every week.
01
Driver staffing
How many drivers tonight to hit the promise?
02
Workforce shift planning
What FT/PT mix covers tomorrow's peaks?
03
Fulfillment placement
Which nodes serve which zones, at what promise?
04
Carrier & zone allocation
Which carrier on each lane, under capacity caps?
05
SKU replenishment
When, and how much, to reorder?
06
Channel spend
Where does the next marketing dollar go?
07
Markdown timing
When to discount aging inventory before value collapses?
08
Peak demand & expansion
How much capacity for the surge? What does the next zone cost?
Your decision isn't here yet? Tell us about it. Most of these started as a single planner's problem.
Baseline vs optimized
See what the solver moves on your network.
Every plan lines your live baseline up against the solver's optimized output, in the network metrics that matter: unit fulfillment cost, inventory days of cover, on-time rate. Toggle to see what the optimization gives back.
Same data. Same constraints. Solver-backed plan.
How it works
Capture. Solve. Decide.
You don't need to hire operations researchers. You don't need another ERP. You need a planning layer above your supply chain stack that turns a weekly decision into a solver problem, runs it, and explains the answer in dollars and SLA points.
01 · Capture
Describe the decision. The agent does the formulation.
Tell the agent what you're staffing, fulfilling, replenishing, or allocating, in plain English. It pulls live data from your ERP, WMS, TMS, and sheets (lead times, capacity caps, SLAs, fairness rules) and turns the request into a formal optimization problem the solver can run.
ERP
WMS
Sheets
LogiModel
Plan
Twin
02 · Solve
Multi-solver optimization, end-to-end on your network.
The agent picks the right solver for your network problem and runs the optimization end-to-end. Async execution handles the SKU × node × week problems that take minutes, not seconds. The optimized plan lines up against your live baseline, in the metrics your fulfillment, finance, and growth teams already use.
Plan vs actual vs twin
Plan Actual Twin
M T W T F
03 · Decide
Interrogate the solver. Re-solve under your overrides. Save the model.
Ask the agent the questions a planner would ask. What if carrier A drops 10% capacity? What if the Shanghai lane slips five days? The agent surfaces shadow prices, binding constraints, and infeasibility in plain language, then re-solves under bounded overrides. Save the scenario; rerun it next week with this week's data.
You
What if lead time on the Shanghai lane increases by 20%?
Twin
Coverage at ATL-3 drops below 5 days for 12 SKUs by Thu. Reroute through Long Beach + lift safety stock on top movers. Service holds, freight up 4.2%.
Generate plots Compare baseline Commit
Ask the twin…
Inside the workbench
Not a mockup. The actual product.
Every plan lives in one workspace: the agent formulates and explains the model, the solver returns the optimal call in hard numbers, and a live digital twin plays it back on your network.
Working sessions
Stop staffing the OR team. Start shipping the plan.
Book a 30-minute working session. Bring one supply chain decision: driver staffing, fulfillment placement, carrier allocation, replenishment, spend, anything. Watch the agent capture it, the solver run it, and leave with a baseline-vs-optimized comparison you can ship Monday.