AI predictive maintenance scores every critical asset daily from the vibration, temperature, current and PLC tags you already collect, then opens a planned work order in SAP PM or Maximo with the likely fault, parts and a repair window.
AI predictive maintenance is the use of machine learning on equipment condition data — vibration, temperature, motor current, pressure and PLC/SCADA tags — to estimate each asset's risk of failure before it happens. It outputs a ranked asset health score, the probable failure mode, a recommended repair window and a draft work order in your CMMS, so planners replace calendar-based tasks with interventions timed to actual condition.
Sensor data sits in historians and PLCs while maintenance runs on fixed calendars and pager calls. The gap shows up as lost hours, rushed parts orders and overtime.
of revenue lost to unplanned downtime at the world's 500 largest companies, about $1.4 trillion a year, per Siemens' 2024 True Cost of Downtime analysis.[1]
of unplanned downtime a month is the average for a manufacturing plant in the same 2024 analysis.[1]
of productive capacity lost to poor maintenance strategies, according to Deloitte's work on asset maintenance.[2]
less machine downtime is what McKinsey reports predictive maintenance typically delivers, with machine life up 20–40%.[3]
We read what you already have before suggesting new sensors, and add low-cost wireless sensors only where a critical asset is blind.
Models learn each asset's normal behaviour under load, then flag drift tied to named failure modes rather than raw alarms.
Each alert becomes a draft notification with evidence, so the planner decides in one screen and the technician knows what to bring.
We choose 20–60 critical assets with your reliability lead, pull maintenance history and agree the metric — usually unplanned downtime hours or confirmed early catches.
Read-only links to PLCs, historian and CMMS. Models learn normal operating patterns by asset class and back-test against past failures.
Daily health scores and draft work orders go to planners. Every alert is marked confirmed, false or unclear so thresholds tighten fast.
We report catches, misses and false alarms against the agreed metric, with the cost case for rolling out across remaining lines.
| Criterion | Calendar / reactive | Off-the-shelf monitoring kit | Stratgik build + run |
|---|---|---|---|
| Uses existing PLC and historian data | Rarely analysed | Often needs its own sensors | Yes, sensors added only for blind spots |
| Mixed OEMs and older assets | Handled by technician experience | Best on supported asset types | Models per asset class, any make |
| Output | Fixed PM tasks, breakdown calls | Dashboard alerts | Draft work orders in SAP PM or Maximo |
| False-alarm control | Not applicable | Vendor defaults | Threshold budget agreed with planners |
| Time to first value | Immediate but no prediction | Fast for simple rotating kit | 4–6 week pilot on your worst line |
| Best fit | Low-criticality, cheap assets | Single asset type, small fleet | Mixed fleets where downtime is expensive |
The evidence has been consistent for years. What changed is that sensor data is now cheap and models can run on it daily without a data science team on site.
Enter your downtime and what an hour costs you. The share of downtime a model can catch early is an assumption — the pilot measures it on your own assets.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
One line or 20–60 critical assets, 4–6 weeks
Per site, per month
Multi-site programmes
Sensors, gateways, cloud compute and any CMMS licence changes billed at cost with no markup; travel for on-site work quoted separately; taxes excluded. GBP and AED prices are indicative conversions from USD.
Accuracy depends on the failure mode and the data behind it, so we measure it rather than promise it. Rotating equipment with vibration data usually gives the clearest early signals; assets with only on/off tags give less. In the pilot we log every alert as confirmed, false or unclear, and report catch rate, false-alarm rate and average warning time on your own assets before any rollout decision.
A Stratgik pilot on one line or 20–60 critical assets is $18,000 over four to six weeks. Ongoing run is $4,000 per site per month, covering scoring, retraining and alert tuning. Sensors, gateways and cloud compute are billed at cost. Most of the cost case rests on the downtime hours you avoid, which the pilot measures directly.
Often not at first. Many plants already log motor current, temperatures, pressures and run states in PLCs or a historian such as PI or Ignition. We start there, then recommend wireless vibration sensors only on critical rotating assets that have no condition data. That keeps hardware spend small and ties every sensor to an asset whose failure actually costs you.
Predictive maintenance generally saves more on critical assets because work is done when condition requires it, not on a calendar. The US Department of Energy's O&M guide puts savings at 8–12% over a preventive-only programme. Preventive tasks still make sense for cheap, low-criticality equipment, so most plants run both.
Yes. Alerts become draft notifications or work orders in SAP PM, IBM Maximo, Fiix or eMaint through their standard APIs, carrying asset ID, suspected failure mode, evidence charts and suggested spares. A planner approves before anything is scheduled. Closed work orders and technician findings flow back to retrain the models, so accuracy improves with use.
A first line is live in four to six weeks. Week one covers asset selection and data access, weeks two and three connect and baseline, and live scoring with planners runs through week five. Full-site rollout typically follows in phases by asset class, driven by which failures cost most.
Book a 30-minute call with your reliability or maintenance lead. We'll review your assets, data sources and CMMS, and scope a pilot around one downtime number.