Quantified grounds for scheduling and maintenance
Condition replay, throughput and energy simulation, equipment life and failure prediction
Beyond showing the present, a twin can answer what-if. We replay operating conditions from historical data, build throughput and energy simulation models, and predict remaining life and failures, so scheduling, maintenance and energy decisions rest on quantified analysis.
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.
Results carry confidence intervals
Throughput and energy forecasts are given as ranges with stated assumptions; a single figure hides uncertainty.
Prediction aimed at maintenance decisions
Equipment predictions output maintenance recommendations and priorities that connect to the EAM work order flow.
Deliverables
Condition replay
Replay of equipment state and metrics by period, with comparison analysis.
Simulation models and scenarios
Throughput, energy and logistics simulation with calibration report and assumptions.
Prediction models and maintenance recommendations
Equipment health score, remaining life prediction, maintenance priority and interfaces.
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.