Inventory Intelligence
Demand-to-supply ratios by model and state — replace quarterly allocation reports with real-time signals
Purpose
OEM allocation decisions are chronically made on lagging supply reports and gut feel from regional managers. When a model is under-supplied in Florida and over-supplied in Ohio, every day of misallocation costs the brand in lost sales, incentive spend, and dealer satisfaction — yet quarterly allocation cycles mean the problem persists for weeks before correction.
Inventory Intelligence combines sold velocity data with live active supply counts to calculate demand-to-supply ratios at the model and segment level for every state. The output is a ranked table of under-supplied models (increase allocation), balanced models (maintain), and over-supplied models (reduce allocation or increase incentives) — updated every time you run it.
How It Works
Execution flow. MCP tool calls are shown inline on each step.
get_sold_summaryCalls get_sold_summary ranked by make,model for your brand in target states to get monthly sold_count, average_sale_price, and average_days_on_market per model.
search_active_carsCalls search_active_cars with facets on model to get live active inventory count per model in same states. Uses car_type=new for new vehicle allocation focus.
Calculates Demand-to-Supply Ratio = sold_count / active_supply_count per model. Flags UNDER-SUPPLIED (> 1.5), BALANCED (0.8–1.5), and OVER-SUPPLIED (< 0.8).
get_sold_summaryParallel get_sold_summary calls for competitor brands in same states and segments to compare D/S ratios and identify where competitors are winning in under-supplied segments.
get_sold_summaryCalls get_sold_summary with summary_by=state for your brand to build a state-level demand table showing over/under-indexed markets vs national average share.
MCP Tool Calls
| Tool | Calls | Purpose |
|---|---|---|
get_sold_summary | 2–4 | Sold velocity by model, segment turn rates, state heatmap |
search_active_cars | 1–2 | Active supply by model and by state |
Example Output
INVENTORY INTELLIGENCE — Toyota | Texas | February 2026 ════════════════════════════════════════════════════════ DEMAND-TO-SUPPLY RATIOS (sorted by urgency) Model Monthly Sold Active Supply D/S Ratio Signal ────────────────── ──────────── ───────────── ───────── ───────────────── RAV4 XLE 2,847 1,243 2.29 UNDER-SUPPLIED ↑ Camry SE 1,932 912 2.12 UNDER-SUPPLIED ↑ Tacoma TRD Off-Road 1,614 891 1.81 UNDER-SUPPLIED ↑ Highlander XLE 1,287 842 1.53 BALANCED ✓ 4Runner TRD Pro 743 531 1.40 BALANCED ✓ Corolla LE 1,104 1,287 0.86 BALANCED ✓ Sienna XLE 312 618 0.51 OVER-SUPPLIED ↓ Sequoia Platinum 198 487 0.41 OVER-SUPPLIED ↓ ALLOCATION RECOMMENDATIONS 1. Increase RAV4 XLE allocation by ~600 units/month (D/S 2.29 — severe shortage) 2. Increase Camry SE allocation by ~400 units/month (D/S 2.12) 3. Reduce Sienna XLE allocation 30% — 90+ days supply building on Texas lots 4. Reduce Sequoia Platinum allocation — very low demand-to-supply ratio SEGMENT TURN RATES — Toyota vs Market (Texas) Segment Toyota Avg DOM Market Avg DOM Difference ───────── ────────────── ────────────── ────────── Pickup 22 days 28 days -6 days (FASTER) SUV 18 days 24 days -6 days (FASTER) Sedan 31 days 29 days +2 days (SLOWER)
Cost Estimate
40 analyses/month ≈ $2–4
Limitations
- US market only — requires get_sold_summary and search_active_cars, both US-only.
- D/S ratios work best for models with 30+ monthly sold units; low-volume models may show artificially high ratios.
- Active supply counts reflect dealer retail listings, not units in transit or at port — actual days supply may be slightly different.
- State-level analysis requires state code from profile or user input; national roll-up is available as fallback.
More in the Manufacturer Plugin
Same Capability, Different Plugin
These skills share the same underlying methodology but are tuned for a different audience.