Most plants still maintain assets on a calendar. Parts are replaced too early, failures still happen too late, and the data the machines already produce is never used. Predictive maintenance changes that by learning failure patterns from real machine and tool data.
SIHub builds both levels: machine learning models that predict when an asset or tool will fail, and an AI prescriptive layer that recommends what to do about it - which action, which part, which shift, at what cost. Both come with visualization dashboards designed for maintenance teams, not for data scientists.
Preventive plans are safe by design, which is exactly why they are expensive: they assume every asset ages at the same rate.
These three are often used interchangeably. They are not the same, and the operational gap between them is where most of the value sits.
| Preventive | Predictive | Prescriptive | |
|---|---|---|---|
| Core question | Is the interval due? | When will this asset fail? | What is the best action to take, and when? |
| Trigger | Calendar time, run hours, or produced units | Model-detected anomaly or predicted remaining useful life | Model prediction plus operational, cost, and scheduling context |
| Data used | OEM recommendation and maintenance history | Live sensor and machine data, historical failures, work orders | All predictive data plus spare-part stock, production plan, cost, and staffing |
| Technology | CMMS plan and checklists | Machine learning: anomaly detection, classification, remaining useful life | ML predictions combined with AI reasoning and optimization |
| Output to the technician | Service task 4B is due this week. | Pump P-102 shows a bearing signature; failure risk is high within 12 days. | Replace the bearing on P-102 during the Saturday changeover; the part is in stock at position A-12; deferring past day 9 risks an unplanned line stop. |
| Main benefit | Predictable, low-effort baseline safety | Fewer surprises and longer component life | Fewer surprises and a decision the team can execute directly |
| Main limitation | Over-maintenance and residual failures | Tells you the risk, but not the trade-off | Requires reliable predictive models and clean operational context first |
In short: preventive maintenance follows a plan, predictive maintenance follows the data, and prescriptive maintenance follows the data and recommends the decision. Prescriptive sits one level above predictive because it does not stop at a warning - it weighs cost, spare parts, production schedule, and risk, then proposes the action. SIHub delivers predictive as the foundation and prescriptive as the layer on top, so each organization can start where its data maturity allows.
No model is better than the data behind it. The first step is a reliable, contextualized data foundation from the assets themselves.
Models are selected for the asset and the data that actually exists, not for the sake of using a specific algorithm.
Learn the normal operating signature of an asset and flag deviations early, including on assets with few or no recorded historical failures.
Estimate how much life is left in a component or tool under current operating conditions, with a confidence range instead of a single number.
Distinguish between failure types - imbalance, misalignment, bearing wear, lubrication loss, tool chipping - so the response can be specific.
Predict tool degradation from spindle load, vibration, and quality signals to change tools on condition rather than on a fixed count.
Connect drifting process signals to emerging quality deviations, catching a developing fault before it shows up as scrap.
Track model drift, false-alarm rates, and hit rates over time, with a retraining loop fed by confirmed technician feedback.
A prediction is only useful if someone knows what to do with it. The prescriptive layer turns a risk score into a concrete, justified recommendation.
The people who fix machines should not need a data platform login and a query language to see what is happening on their line.
Predictive and prescriptive maintenance is a data and process programme, not a single software purchase. We build it in phases with value at each step.
Select critical assets, review failure history, assess existing instrumentation and connectivity, and define the target failure modes.
Connect machines, add retrofit sensors where needed, build the data pipeline, and train and validate the first predictive models.
Add the AI recommendation logic, connect it to spare parts and the production plan, and roll out the dashboards to maintenance teams.
Extend to more assets and sites, track avoided downtime and cost, and retrain models with confirmed field feedback.
The goal is not a model. The goal is fewer stops, lower cost, and maintenance decisions that are easier to defend.
Most relevant for asset-intensive operations where downtime is expensive and machine data already exists but is unused.