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# Demo script — 6 minutes
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One story, told once, with a clear beginning and end: *the twin sees a bearing
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failing before it fails, explains why in plain language, and lets you test a fix
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before touching the plant.*
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Resist the urge to show every feature. The four faults exist so you can answer
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"what else can it do?" — not so you can fire all of them.
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**Before you walk in:** `npm run dev`, open `http://localhost:5173`, confirm the
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header shows *Telemetry live* and the copilot badge shows a provider. Leave the
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clock at **1×**. Press **Reset** if anyone has been clicking around — the line
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comes back pre-warmed to a realistic ~88% OEE.
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---
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## 0:00 — What they are looking at (45s)
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> "This is a live digital twin of a five-station production line — infeed,
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> machining, curing oven, vision inspection, packing. Everything on this screen is
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> being computed from a running model of the line, not replayed from a video."
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Orbit the 3D view once, slowly. Click **CNC-02** in the 3D scene — the detail
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charts below change with it.
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> "OEE is 88%. Availability is 100% — nothing has broken down this shift.
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> Performance is 91%, and that's micro-stops. Quality is 97.6%. That's a well-run
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> line having a normal day."
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**Why this beat matters:** the baseline has to be believable before the failure
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means anything. If they accept 88%, they will accept everything that follows.
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## 0:45 — Show that it is a model, not a dashboard (60s)
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In **What-if controls**, drag **Oven setpoint** from 305 to 330 °C. Point at the
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Zone temperatures chart, then at Burner duty.
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> "Watch the response. It doesn't jump — it takes about half a minute of run time
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> to settle, and ten seconds in it's only 44% of the way there. And look at burner
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> duty: it spikes from 68 to 85% and then settles back to 74% as the controller
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> finds the new equilibrium. That's a thermal model with a real time constant and
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> a controller on top. A dashboard would just redraw a number."
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Measured: 305 °C → 316 at 10 s → 328 at 25 s → within 1 °C of setpoint by 30 s.
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Drag it back to 305.
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> "That's the point of a twin: you can ask 'what happens if' without doing it to
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> the actual plant."
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## 1:45 — Inject the fault and compress time (45s)
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Click **Inject** on *CNC-02 bearing degradation*. Then set the clock to **60×**.
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> "I've just started a spindle bearing degrading. In a real plant this plays out
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> over days. I'm running the clock at 60×, so we'll watch it in about thirty
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> seconds. Everything else stays physical — the clock is the only thing I sped up."
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Say the 60× out loud. Point at the sim clock. Never let them think this is
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real-time.
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Watch the **Bearing Vibration** chart climb toward the dashed **Warn 3.5** line.
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## 2:30 — The prediction (60s)
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A purple **Trend** card appears above the charts, and an alarm appears in the feed.
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> "There it is. Vibration is at 2.9 and rising 0.18 per minute. Nothing is over
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> limit yet — but it's telling me I hit the 4.5 alarm limit in about 27 minutes of
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> run time."
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If asked how — and someone always asks:
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> "It's a least-squares fit over the last twenty minutes, extrapolated to the
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> limit, and it only reports when the fit is good enough to mean anything — that
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> r² of 0.65 is the fit quality. It's a regression, not a black box. I can show
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> you the arithmetic."
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**Do not call this AI.** Calling a straight-line fit "AI prediction" is the fastest
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way to lose the engineer in the room.
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## 3:30 — The copilot (90s)
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Let it cross 4.5. OEE is now falling visibly. Click **Explain this** on the
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bearing alarm.
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While it thinks:
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> "This is reading the live telemetry — states, readings against limits, the OEE
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> breakdown, the measured trends. What it is *not* told is which fault I injected.
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> It has to work that out from the data, same as your engineer would."
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Read the answer out. It will trace vibration → tool wear → rejects → OEE.
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> "Notice it separates what's measured from what's inferred, and it traced the
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> whole chain: the vibration is raising tool wear, which is pushing parts out of
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> tolerance, which is why the reject rate went from 2% to 5% — and that's what's
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> dragging OEE down, not the machine stopping."
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Then click **Draft a maintenance work order**.
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> "And it writes the work order. That goes to your CMMS."
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**If the cloud model is slow:** switch the selector to **Local** before the
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meeting. See *Provider choice* below — a 20-second silence kills this beat.
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## 5:00 — The intervention (45s)
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> "So: I know the bearing is going, I know roughly when, and I know what it's
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> costing me. Let me act on it."
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Click **Clear** on the bearing fault, then **Tool change**. Set the clock to
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**20×**.
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> "Bearing replaced, fresh tooling. Watch the reject rate come back down and OEE
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> recover. The twin closed the loop — it found it, explained it, and confirmed the
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> fix worked."
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## 5:45 — Land it (30s)
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> "Two things to take away. This is running on a simulated line today, but the
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> ingest layer is built to take your data — MQTT, OPC-UA, or a historian export —
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> and nothing above that layer changes when we swap it. Second, the reasoning is
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> reproducible: I can turn the language model off entirely and the same diagnosis
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> comes out of a rule engine."
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Switch the copilot to **Offline rules** and ask *"Why is OEE down?"* — it answers
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instantly.
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> "Same conclusion, no model, no network. The AI makes it conversational. It isn't
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> where the analysis comes from."
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That last move is worth more than it looks: it is the answer to "is the AI just
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making this up?", and it lands better as a demonstration than as a claim.
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---
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## Provider choice — decide before you present
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Open **AI ⚙** in the header, pick a model, and hit **Test**. It reports
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time-to-first-token and tells you whether that model is presentable live. Do this
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on the demo machine, on the demo network, before the meeting — cloud latency
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measured anywhere between 2 s and 36 s on consecutive identical calls.
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| Setting | First token | Use it when |
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|---|---|---|
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| **Cloud** (`stealth/ox-alpha`) | 2–35 s, variable | Best analysis. Good for the follow-up conversation, risky for the live beat. |
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| **Local** (Ollama) | ~1–2 s | The live walkthrough. Needs `ollama serve` running. |
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| **Offline rules** | instant | No network at all. Your safety net, and the credibility proof at the end. |
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The copilot panel shows which one answered every message, so you always know. The
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selector there overrides the default per question, so you can run the walkthrough
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on Local and switch to Cloud for a deeper answer when someone pushes.
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Your choice is saved, so set it once while preparing and it will still be there.
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## Questions you will get
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**"Is this our real data?"** No — it is a simulated line. The adapters for your
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data are in `server/ingest/`, and `docs/architecture.md` shows exactly where they
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plug in. Nothing above that layer changes.
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**"How does it predict the failure?"** Least-squares fit over recent run time,
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extrapolated to the alarm threshold, suppressed when r² is too low to trust. Open
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`server/analytics/trend.js` if they want to see it.
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**"Could it control the line?"** Technically yes over OPC-UA; deliberately
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read-only here. Writing setpoints to a PLC needs interlocks, an audit trail and
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their sign-off. Say that — it builds more trust than saying yes.
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**"Why is OEE only 88% at the start?"** Because a line that reads 100% is a line
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nobody believes. Micro-stops and unplanned stops are modelled, so the number moves
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for reasons you can name.
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**"Can it run on the plant floor / offline?"** Yes. It is one Node process and a
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static front end, and with the local model or the rule engine it needs no internet
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at all.
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## If something goes wrong
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- **Charts empty / "Disconnected"** — the server died. `npm run dev` again; the
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dashboard reconnects on its own and the server comes back pre-warmed.
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- **Copilot returns nothing** — switch to **Offline rules** and carry on. This is
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why that mode exists.
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- **The line is in a strange state** — press **Reset**. It clears faults, learned
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baselines and history, and re-warms to a clean ~88% OEE in under a second.
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- **The 3D is choppy** — drop the clock to 20×. The model is fine; it is the
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render loop competing with the projector.
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