— 04 / Outcomes
Real numbers from real operating environments.
Typical improvement ranges measured across recent pilots and enterprise rollouts in heavy industry. Each range reflects the low and high end of results from comparable asset classes — your starting point and data maturity determine where you land.
Downtime reduction
20–40%
Predictive maintenance and failure prediction across critical asset classes.
Maintenance cost
10–25%
PM optimization, work management redesign, and spare parts forecasting.
Throughput uplift
5–15%
Bottleneck analytics, scheduling AI, and yield optimization.
Energy intensity
10–20%
Energy-per-unit reduction and ESG performance analytics.
Higher PM compliance
Better schedule adherence
Lower spare parts risk
Improved safety compliance
Higher asset availability
Better capital decisions
Faster management response
Measurable ESG outcomes
20–40%
/O.01 — Downtime reduction
Predict the failure before the work order is written.
How we get there
- Failure-mode mapping on the top 20 assets driving 80% of unplanned hours.
- Hybrid models combining physics, condition data, and operational context.
- Predictions routed into EAM work-management so the floor actually acts on them.
Leading indicators
- Predictive work-order rate vs. reactive — flips inside 6 months.
- Mean time between failures on instrumented critical assets.
- Schedule attainment on planned predictive work.
10–25%
/O.02 — Maintenance cost
PM that is earned, not scheduled by calendar.
How we get there
- PM library rebuild on real failure modes, not vendor defaults.
- Work-management redesign in SAP PM or Maximo to remove duplicated effort.
- Spare-parts forecasting tied to predicted, not historical, demand.
Leading indicators
- PM hours per asset, by criticality band.
- Stockout and emergency-purchase rate.
- Wrench time on planned vs. unplanned work.
5–15%
/O.03 — Throughput uplift
Find the constraint. Move it. Then find the next one.
How we get there
- Bottleneck analytics anchored on actual run-rate, not nameplate capacity.
- Scheduling AI that respects real changeover, crew, and material constraints.
- Yield optimization driven by operator-controllable variables, not theory.
Leading indicators
- Constraint utilization at the actual bottleneck.
- Changeover loss per shift.
- First-pass yield by product family.
10–20%
/O.04 — Energy intensity
Energy per unit produced, not energy in absolute.
How we get there
- Energy-per-unit baselines by line, product, and shift — not site-wide averages.
- Operating-setpoint optimization on the largest energy consumers.
- ESG analytics that align ops data with Scope 1, 2, and 3 reporting.
Leading indicators
- Energy intensity index, weekly, by line.
- Off-spec rework as an energy cost.
- Verified emissions per unit shipped.
— 06 / 90-Day Launchpad
Start with one asset class.
Prove the model.
Scale the framework.
Our Reliability Launchpad delivers a measurable AI use case in 90 days — fixed-fee, milestone-based, with an executive roadmap for enterprise scale. No abstract proofs of concept.
12-week launchpad roadmap
Wk 1–2 Discovery & data readiness
Asset selection, data audit, success metrics, stakeholder alignment, and a clear definition of done.
Wk 3–6 Model build & validation
Feature engineering, model training, holdout testing, and reviewer sign-off against agreed accuracy targets.
Wk 7–10 Production pilot
Live operation in a single plant, KPI tracking, user workflow integration, and on-the-ground change management.
Wk 11–12 Executive roadmap
Business case, scale plan, capital sequencing, and an executive-ready roadmap to roll out across sites and asset classes.
What you walk away with
- 1 A working AI use case in production on one asset class
- 2 Verified ROI signed off by your Finance team
- 3 A costed roadmap to scale across the enterprise
- 4 Everything we built — yours to keep, scale or not
Fixed fee
90 days · milestone-based