Which machines will fail in the next two weeks?
Adaptrix answers that question from your own shop-floor data. IoT sensor streams, MES and SAP history feed anomaly detection and forecasting models that flag failure risk days to weeks ahead — then explainable root-cause analysis explains why. Self-hosted and GDPR-first: production data never goes to a third-party AI provider, at fixed annual pricing with €0 per-token.
See it work
Ask in plain language. Get an explained, cited decision.
One question routes through specialised agents — schema, SQL, forecast, root cause and governance — and lands as an auditable answer with the evidence attached.
Director· Manufacturing workspace
DemoWill we hit our Q1 gross-margin target?
Director Highanswer in 14.2s
- Analysing gross margin
- Scanning orders + invoices
- Ranking the drivers of the change
- Formatting the explained answer
scan Scan shipments · 1.2M rowsfilter Filter the premium product lineaggregate Aggregate gross margin by segmentrank Rank driver contributions
Premium-line returns drive most of the margin slip.
Why gross margin movednet -1.4 pts
- 62% of the movePremium-line returns-1.1 pts
- Freight surcharge-0.5 pts
- offsetsMix shift to services+0.2 pts
Adaptrix recommends
- Re-certify 6 QC inspectors
- Rebalance 2 supplier contracts
Projected · +10-pt margin · €25M / yr
GDPR · checkpoints cleanSQL + provenance attachedAudit logged
Ask a follow-up…
Illustrative scenario shown in a demo workspace — figures for demonstration, not a customer result.
Ask it about your operation
Why did on-time delivery drop — and will we hold 95%?
A dashboard shows you the OTD line bending. Adaptrix explains why it moved — tracing the drop across your linked order, shipment and supplier data, attributing it to signed drivers, and forecasting where the quarter lands with a 95% confidence interval. Every answer ships with the SQL and provenance attached, so the plant manager and the auditor read the same story.
Q
11.8s·auditedWill we hold our 95% on-time delivery target this quarter?
- Parsing the question — metric, segments, horizon
- Linking 4 connected sources
- Forecasting with a 95% confidence interval
On-Time Delivery · 12 wk actuals + forecast
forecast 85.0%
Why it moves
Late inbound from one supplier cascades to 3 assembly lines.
Forecast85.0%95% CI · 82.9%–87.1% by Q1
- −1.8 ptsSupplier B late inbound · 60% of the move
- −0.8 ptsLine 3 changeover time
- −0.4 ptsTransit / weather
Net change= −3.0 pts
Adaptrix recommends
- Dual-source Supplier B critical parts
- Add a 2-day inbound buffer on Line 3
Projected · recover to 94% on-time by Q1
TraceGDPR · checkpoints cleanSQL + provenance attachedAudit logged
Scripted replay with illustrative data — no customer data shown.
11%
of Fortune Global 500 revenue is lost to unplanned downtime — about $1.4 trillion a year (Siemens/Senseye, 2024)
$2.3M
cost of a single hour of unplanned downtime in a large automotive plant (Siemens/Senseye, 2024)
70%
fewer breakdowns on average with predictive maintenance (Deloitte Analytics Institute)
Sources: Siemens/Senseye, The True Cost of Downtime 2024; Deloitte Analytics Institute, Predictive Maintenance position paper. Figures are industry-level benchmarks, not Adaptrix results.
Why does unplanned downtime still surprise your plant?
Because the warning signs sit in disconnected systems: vibration and temperature in IoT sensors, cycle times in the MES, order pressure in the ERP. Until those signals are correlated, the first alert is the line stopping.
Quality Control Gaps
Manual spot checks find defects only after value has been added — and the underlying cause stays invisible.
Scrap discovered too late
Unplanned Downtime
Equipment failures cause unexpected production stoppages
$2.3M per lost hour in automotive
Supply Chain Blindness
Limited visibility into supplier performance and inventory levels
Capital tied up in buffer stock
How does AI predictive maintenance actually work?
Sensor and MES data stream into anomaly-detection and cross-validated time-series models that learn each machine's normal behaviour. Deviations are scored, explained with SHAP and translated into concrete maintenance windows your planners can act on.
Before Adaptrix
- Equipment fails without warning
- Maintenance based on schedules, not condition
- Spare parts stockouts during critical repairs
- No visibility into equipment health trends
After Adaptrix
- Predict failures 2-4 weeks in advance
- Condition-based maintenance optimization
- Automated spare parts ordering
- Real-time equipment health scoring
70% fewer breakdowns
Average reported for predictive maintenance by the Deloitte Analytics Institute. Actual outcomes vary with sensor coverage, data quality and failure modes.
What caused the drop in OEE last Tuesday?
Adaptrix answers with explainable root-cause analysis instead of another dashboard: SHAP-based attribution separates true drivers from mere correlations and shows which signals moved a prediction — so teams fix the cause, not the symptom.
Explainable drivers, not correlations
Explainable driver attribution tests whether a suspected driver — tool wear, a material batch, ambient temperature — is actually behind the anomaly, or merely co-occurs with it.
Explainable predictions
SHAP values show which sensor signals pushed a machine's failure-risk score up, so maintenance teams can verify every prediction on the shop floor before acting.
From anomaly to OEE action
Anomalies in scrap rate, cycle time or energy draw are mapped to OEE losses — availability, performance, quality — and routed to the team that owns the fix.
Traditional Approach vs. Adaptrix Solution
Quality Management
Traditional
Manual inspections, paper checklists, reactive defect detection
With Adaptrix
AI-powered defect prediction, real-time quality monitoring, automated alerts
Catch defects in-line
Predictive Maintenance
Traditional
Scheduled maintenance, reactive repairs, production interruptions
With Adaptrix
IoT sensor analytics, failure prediction, optimized maintenance windows
Weeks of warning
Supply Chain Optimization
Traditional
Excel forecasting, manual supplier tracking, siloed inventory data
With Adaptrix
End-to-end visibility, demand forecasting AI, automated reordering
Right-sized inventory
Where do plants deploy Adaptrix first?
Deploy pre-built manufacturing analytics that deliver value from day one
Predictive Maintenance
Prevent equipment failures, optimize maintenance schedules, reduce downtime
Time to value: 4 weeks
Quality Prediction
Detect defects early, reduce waste, improve first-pass yield
Time to value: 3 weeks
Production Optimization
Maximize throughput, balance lines, reduce cycle times
Time to value: 5 weeks
Supply Chain Analytics
Optimize inventory, improve supplier performance, reduce lead times
Time to value: 4 weeks
Energy Management
Monitor consumption, identify savings, optimize energy usage
Time to value: 3 weeks
OEE Improvement
Track performance, identify bottlenecks, improve availability
Time to value: 4 weeks
Illustrative scenario
What does this look like in a mid-market plant?
Consider a German industrial pump manufacturer: three machining lines, a lean maintenance crew, and a chronic problem — bearing failures on its machining centres stop production without warning, and monthly OEE reviews cannot explain recurring micro-stoppages.
Vibration and spindle-current sensors, MES cycle data and SAP maintenance history flow into Adaptrix over secure connectors and are analysed on Adaptrix's own sovereign EU infrastructure — production data never goes to a third-party AI. Time-series models learn each spindle's baseline, anomaly detection flags drift weeks before failure, and explainable driver attribution traces the micro-stoppages to a change in a coolant supplier's batch.
Maintenance now plans bearing swaps into scheduled windows instead of firefighting, and purchasing renegotiates the coolant specification. Availability and OEE improve over the following quarters — with no shop-floor data ever leaving the plant.
Illustrative scenario based on typical mid-market deployments, not a named customer reference. Outcomes depend on data quality, sensor coverage and failure modes.
“Unplanned downtime now costs Fortune Global 500 companies 11% of their revenues — around $1.4 trillion a year.”
Does shop-floor data have to leave your network for AI?
No. Adaptrix runs analysis on self-hosted open models on its own sovereign EU infrastructure — so process parameters, recipes and machine data never go to a third-party AI provider. That keeps GDPR and EU AI Act obligations manageable and your process know-how out of cloud AI providers' hands.
- Self-hosted open models on EU infrastructure — no external AI API calls — on-premise and air-gapped deployment planned for Enterprise Plus
- GDPR-first architecture and documentation that supports your EU AI Act obligations
- Fixed annual pricing with €0 per-token — usage never drives the bill
Manufacturing-Specific Features & Integrations
MES integration (Siemens, Rockwell, SAP)
SCADA system connectivity
OPC UA protocol support
IoT sensor data ingestion
PLM system integration
ERP connectivity (SAP, Oracle)
Real-time production dashboards
Mobile alerts for floor managers
Your 8-Week Smart Factory Transformation
Phased implementation with minimal disruption to production
Weeks 1-2
System Integration
- Connect MES/SCADA
- IoT sensor setup
- Data validation
Weeks 3-4
Baseline & Models
- Historical analysis
- ML model training
- KPI definition
Weeks 5-6
Pilot Line
- Single line deployment
- Operator training
- Fine-tuning
Weeks 7-8
Full Rollout
- All lines active
- Dashboard launch
- Alert configuration
Keep exploring
Related solutions and guides for GDPR-first, self-hosted AI analytics.
Frequently asked questions
What data does AI predictive maintenance need?
Vibration, temperature, current and cycle data from IoT sensors, plus operational context from MES/SCADA and maintenance history from ERP systems such as SAP. Adaptrix connects via OPC UA and standard connectors; a single well-instrumented line is enough to start.
How far in advance can machine failures be predicted?
Typically days to weeks, depending on failure mode and sensor coverage. Slow-developing degradation such as bearing wear gives the longest warning; cross-validated time-series models and anomaly detection surface the drift early. Sudden failures without measurable precursors remain unpredictable — no honest vendor claims otherwise.
How is this different from classic condition monitoring?
Condition monitoring alarms when a threshold is crossed. Adaptrix learns each machine's normal behaviour, forecasts failure risk ahead of thresholds, and adds explainable root-cause analysis with SHAP — so you know why something drifted, not just that it drifted.
Does our production data leave the plant?
No. Adaptrix runs self-hosted open models on its own sovereign EU infrastructure. There are no external AI API calls — on-premise and air-gapped deployment are planned for Enterprise Plus — and GDPR and EU AI Act obligations stay manageable.
How does AI root cause analysis work in manufacturing?
Anomalies in OEE, scrap or cycle time are analysed with explainable models that rank candidate drivers — tool wear, material batches, ambient conditions — against your data, while SHAP shows which signals moved a prediction. The result is a ranked, verifiable cause list instead of a correlation heatmap.
What does Adaptrix cost for a mid-market plant?
Adaptrix uses fixed annual pricing with €0 per-token — cost does not scale with queries or data volume, so plant-wide rollouts stay predictable. See the pricing page for tiers and what is included.
See which machines are at risk
Book a demo — we connect sample sensor or MES data and show a first failure-risk and OEE view in the session.
Ask it why.
Bring a real question from your business and watch a live answer — reasoning, sources and math included — or model what it saves you first.