Quantified grounds for scheduling and maintenance
Condition replay, throughput and energy simulation, equipment life and failure prediction
We replay operating conditions from historical data, build throughput and energy simulation models, and predict remaining life and failures. Scheduling, maintenance and energy decisions rest on quantified analysis rather than the present state alone.
Our approach
One team scopes, executes and retests; conclusions are delivered as evidence, not checklists.
Replay first, then simulate
Condition replay verifies that data and model agree; simulation models are calibrated on that basis and match actual conditions. Data issues found during replay are corrected before simulation.
Results carry confidence intervals
Throughput and energy forecasts are given as ranges with stated assumptions to reflect forecast uncertainty, allowing scheduling and investment decisions to include appropriate margin.
Prediction layered to ISO 13374, ending in maintenance recommendations
Models follow the ISO 13374 layers of state detection, health assessment, prognostic assessment and advisory generation; remaining-life predictions follow ISO 13381-1 with stated confidence and preconditions. The output is maintenance recommendations and priorities linked to the EAM work-order flow.
Deliverables
Condition replay
Replay of equipment state and metrics by period, with comparison analysis. Replay is checked period by period against historical records; that agreement is the precondition for calibrating simulation.
Simulation models and scenarios
Throughput, energy and logistics simulation with calibration report and assumptions. The report lists backtest periods, deviations and valid range; no conclusions are given outside that range, and models are recalibrated after line or process changes.
Prediction models and maintenance recommendations
Equipment health score, remaining life prediction, maintenance priority and interfaces. Acceptance is by historical backtest: warning lead time, missed and false alarms are measured against targets agreed during data assessment.
How we deliver
Four stages, each with defined inputs, outputs and a client sign-off.
Data assessment
Historical data completeness, equipment register, maintenance records.
Replay and calibration
Replay function, first simulation model and calibration.
Simulation and prediction modelling
Scenario simulation, prediction model training and validation.
Integration and validation
Integration with scheduling / EAM, business validation.
Frequently asked questions
Notes on scope, execution and delivery standards. Contact us for anything not covered here.
Throughput and energy simulation usually needs 3–6 months of continuous operating data; failure prediction needs records covering several failure events. Feasibility and gap-filling are confirmed during data assessment.
Start from where you stand
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.