Build a make-vs-buy model

You're the ops lead at a fast-growing DTC coffee brand. You currently outsource fulfillment to a 3PL, but order volume is climbing fast and you need to decide whether to bring fulfillment in-house. Build an interactive make-vs-buy model in a single self-contained HTML file.

Your raw data is the attached file fulfillment-daily.csv — 731 days (~24 months) of actual fulfillment history: date, orders, units_shipped, avg_order_weight_lb, returns, and the 3PL's daily charges (pickpack, shipping, storage_pallets, storage cost). Reconstruct the real cost base and the demand trend + seasonality from this data yourself.

The in-house option (your assumptions):
- 12,000 sq ft warehouse @ $1.15/sq ft/mo (3-yr lease) + $14,000 one-time fit-out.
- $85,000 equipment capex (racking, conveyor, packing stations), depreciated over 7 years.
- 6 full-time staff at $48,000/yr fully loaded; add 1 FTE per additional 8,000 orders/month above 25,000.
- $1,400/mo warehouse-management software.
- Negotiated outbound shipping of $0.78/lb (vs the 3PL rate in the data).
- 3-month ramp: assume 60% efficiency (a 40% cost penalty) in months 1-3.
- Cost of capital: 12%.

Build a single self-contained HTML page that:
1. Projects total monthly fulfillment cost for BOTH options 36 months forward (extrapolate demand from the historical trend + seasonality in the data).
2. Shows the break-even month, cumulative-cost curves (chart), and the 3-year NPV of each option.
3. Has sliders for: demand growth rate, cost of capital, in-house fully-loaded staff cost, and the 3PL per-order rate.
4. Includes a sensitivity view: how the recommendation flips if volume runs +/-20%.
5. States a clear recommendation and the 2-3 key drivers behind it.

Vanilla JS + inline SVG or <canvas> for charts, no external libraries. Return only the HTML.