A decision-support model for automotive inventory strategy — from OEM portfolio down to the dealer lot. Answer-first brief, a live net-benefit engine, a governed rollout plan, and a DMS-sourced retail view.
🔗 Live: https://privatejoel.github.io/OEM-Inventory-Health/ 📄 Methodology & assumptions: METHODOLOGY.md
A multi-billion-dollar national vehicle portfolio is, at any moment, simultaneously over-stocked in slow-turning segments and under-stocked in fast ones. Judging every segment against one inventory benchmark hides this — luxury and high-volume models have fundamentally different velocity economics. The result is capital bleeding carrying cost in one place while demand goes unserved in another.
Measure each model's Days Sales of Inventory (DSI) against a segment-specific target, separate stock that will bleed off on its own (just pause replenishment) from a genuine structural overhang, then clear only that overhang when the annual carrying cost avoided exceeds the one-time clearance loss — redeploying the recaptured capital to relieve stockout-risk segments first. The point of the bleed-off step: never discount demand you already have.
| Metric | Value |
|---|---|
| Capital deployed | $2.80B |
| Capital above segment target (gross) | $603M (22%) |
| Structural overhang (won't bleed off) | $320M (11%) |
| Annualized carrying cost on overhang | $58M/yr |
| Capital recaptured | $301M |
| Net benefit | $38M |
| Demand-constrained gap funded | $216M, fully covered |
Most above-target stock isn't cleared — it bleeds off once replenishment pauses; only the $320M structural overhang (EV-glut + slow-luxury) is liquidated. The recaptured capital more than covers the volume shortfall, with ~$85M left for strategic priorities. Full derivation in METHODOLOGY.md.
The app is organized as four role-targeted views:
- Strategy Brief — answer-first recommendation with quantified impact, then a Situation / Complication / Resolution case. (Consulting framing.)
- Decision Dashboard — segment-targeted DSI table that separates a structural overhang from above-target stock that simply bleeds off, accurately-labeled KPIs, a ranked net-benefit recommendation engine, and live scenario sliders (carrying rate, clearance discount, bleed-off window, demand shift) so every figure can be stress-tested. (Planning / analytics.)
- Execution Plan — phased rollout, RACI decision rights, governance KPIs, and a risk/mitigation register. (Program management.)
- Retail & DMS — drills the OEM thesis down to the dealer P&L: DMS-sourced aging buckets, floorplan cost, inventory turn, and dealer-level retail levers (incentive / dealer trade / wholesale). Shows the OEM → dealer → DMS data flow. (Retail execution.)
This was deliberately built to survive a probing interview:
- Scale matches the narrative — national-aggregate units and OEM wholesale costs put the portfolio in the billions, not the low millions.
- Metrics are labeled for what they are — "Annualized Carrying Cost," "Capital Above Target," and a volume-weighted "Portfolio DSI," not a count ratio mislabeled as a financial return.
- The headline tradeoff is actually computed — carrying cost avoided vs. clearance loss, per pool, with an explicit act/hold rule — not asserted in prose.
- Above-target ≠ liquidate — the model strips out inventory that sells through on its own and only clears the structural overhang, so it never recommends discounting demand you already have.
- Targets are segment-specific, consistent with the core thesis.
- Assumptions are owned and sourced, with sensitivity and limitations documented openly.
React 18 · TypeScript (strict) · Tailwind CSS 3 · Vite 5 · lucide-react
npm install
npm run dev # http://localhost:5173
npm run build # production build to /dist
npm run deploy # publish /dist to GitHub PagesPortfolio-level (no per-region matrix), steady-state demand, single-period net benefit, and contribution margin not yet modeled. Each is called out with its next step in METHODOLOGY.md §9.
For portfolio and educational purposes only. Not affiliated with, endorsed by, or representative of Hyundai Motor Group or its subsidiaries. All data and scenarios are simulated to demonstrate the analytical framework.
Joel Johnson — Automotive Strategy & Planning Portfolio