Executive Overview
Objective: Reduce unplanned downtime and maintenance cost within 90 days using AI‑driven predictive maintenance and guardrailed deployment.
BaselineClient input
Unplanned downtime4.0%
MTBF220 hrs
Target UpliftPoV goal
Downtime reduction25–40%
MTBF improvement+15–30%
Scope90 days
- Week 1–2: Data ingest (CMMS, sensors, logs) & quality checks
- Week 3–6: Model training + shadow alerts
- Week 7–10: Controlled rollout & operator buy‑in
Deliverables: asset health dashboard, evaluator, governance pack, 1‑page ROI memo.
Proposal Materials
PoV One‑Pager
Summary of scope, KPIs, governance & success criteria.
FAQ
Data needs, model types, explainability, and human‑in‑the‑loop.
How do predictions work?
We learn patterns from vibration, temperature, current draw, PLC/SCADA events, and CMMS history to estimate remaining useful life (RUL) and failure probability by asset.
What data is needed?
Sensor streams (where available), historian/SCADA tags, maintenance logs (CMMS), spare‑parts/lead times, production calendar, and failure codes taxonomy.
Commercials
| Item | Price | Notes |
|---|---|---|
| Fixed PoV fee | $XX,000 | Setup, modeling, dashboard |
| Success component | $Y per % downtime cut | Capped at Z% |
Demo & Metrics (Mock Data)
Unplanned Downtime — Baseline vs Predicted Reduction
Toggle optimization strength to see expected results.
12%
Maintenance Cost Impact
Cost curve accounts for reduced breakdowns and fewer expedited parts.
Value Calculator
Projected annual savings$0.00
Assumes baseline unplanned downtime = 4% and reduction = sensitivity×1.5% (capped), bounded by conservative factors.
Risk & Governance ("Governance in a Box")
Risk Matrix
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| False positives overwhelm maintenance | Medium | Medium | Calibrate thresholds, cost‑aware evaluator, phased rollout |
| Data quality / sensor drift | Medium | Medium | Signal QC, drift checks, tag health monitoring |
| Operational disruption | Low | Medium | Shadow alerts, supervisor approval gates |
Controls & Guardrails
- Evaluator calculates expected value (repair vs. run‑to‑fail) before recommending action
- Human‑in‑the‑loop: planner approval for work orders
- Audit & backtesting against actual failures; weekly model review
Compliance Pack
Templates: reliability KPIs, decision logs, rollback guide, RCA template, and CMMS field mapping.
Case Comp Cards (Analogous Wins)
Automotive
CNC lineBreakdowns down 35%; spare‑parts expedite costs down 22%.
Process
Pumps & motorsVibration model cut unscheduled outages 28% YoY.
Food & Bev
Packaging lineMTBF +24%; changeover planning improved maintenance windows.
Collaboration & Q&A
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Next Steps & Action Tracker
| Action | Owner | Due | Status |
|---|---|---|---|
| Share 6–12 months CMMS history + failure codes | Client Maint. | MM/DD | |
| Provide tag list (SCADA/historian) for critical assets | Client OT/IT | MM/DD | |
| Book Reliability Workshop (2 hrs) | Both | MM/DD |