Predictive maintenance flags symptoms or forecasts failure. Kausalyze builds a causal model of your process to trace the problem to its upstream driver, show the evidence and identify what to change.
Process manufacturers have invested heavily in data infrastructure and predictive tools. Those tools have become better at flagging problems, but an alert still does not tell you what caused the problem or what to change. Engineers must make that connection while experienced colleagues retire and recurring failures keep costing the plant money.
Sources: Siemens, The True Cost of Downtime 2024. Rockwell, State of Smart Manufacturing 2026. Per-facility figure is a benchmark-derived estimate for modelling and varies by site.
A process upset does not pause while the team investigates. Finding the true root cause of a recurring problem takes days or weeks. Lost yield, off-spec product, excess energy and avoidable maintenance add up the whole time.
A warning helps you plan a response. Resolving a recurring problem also requires the upstream cause, evidence your engineers can inspect and a specific change with an expected impact.
Assess process health, find root causes, forecast time to a limit, test interventions and prescribe changes, all using the same causal model.
Assess equipment condition and process stability across the connected unit. See a change in the context of the upstream variables driving it.
Trace a recurring failure or quality problem to its upstream drivers. Inspect the causal path and supporting evidence with your engineers.
Estimate when a condition is likely to cross a limit, with confidence levels and supporting evidence, so your team can plan an intervention.
Test a set-point change in the model before touching the plant. Compare interventions and quantify the expected effect on production, energy or maintenance.
Indicated values are published industry figures, not Kausalyze performance claims. Site-level value is established in the pilot, against the plant's own records.
Assess each variable alongside its upstream drivers to identify changes in process health across connected equipment.
Trace a fault back through the causal relationships to the upstream driver, with evidence your engineers can review.
Root cause: A_LEAN_FLOWEstimate the time to a process limit and review the forecast evidence to plan when to intervene.
Test the change before you touch the plant. Compare a proposed set-point change with leaving conditions unchanged, and inspect the predicted downstream effects.
try A_LEAN_FLOW −2.1%Review the prescribed change and its expected impact on the unit. Quantify the value before deciding whether to implement it through your existing operating procedures.
set A_LEAN_FLOW −2.1%Predictive tools flag anomalies. AI co-pilots search historical events. Kausalyze identifies the root cause, shows the evidence, and helps optimise the process, transforming data into decisions and saving real money.
Constrained by physics, validated by engineers, and supported by evidence, Kausalyze provides the causal foundation for autonomous manufacturing.
| Capability | Predictive maintenance | AI co-pilots | Kausalyze |
|---|---|---|---|
| Finds the upstream cause Not just the symptom | ◑ Usually asset-level | ✕ | ✓ |
| Models cause across the process Equipment, units and shared utilities | ✕ Typically asset-by-asset | ✕ | ✓ |
| Shows the evidence behind the answer | ◑ | ◑ Depends on source retrieval | ✓ Causal path and evidence per link |
| Constrained by process physics | ◑ Varies by tool | ✕ No process model by itself | ✓ Physics and process connectivity |
| Engineers validate causal links | ◑ Review alerts and forecasts | ◑ Review generated answers | ✓ Confirm or reject important links |
| Tests what happens if you make a change | ✕ | ✕ | ✓ |
| Prescribes what to change | ◑ Usually maintenance action | ◑ Can suggest options | ✓ With expected downstream impact |
| Starts with existing historical data First validated root causes in 12 to 16 weeks | ◑ Requirements vary | ◑ Requirements vary | ✓ |
Capabilities vary by product and implementation. The comparison describes typical uses.
Kausalyze is working with Huntsman Polyurethanes' Global Excellence Team on heat-exchanger fouling, building causal models of how operating conditions actually drive fouling and degradation, and validating them jointly with Huntsman's engineers.
Kausalyze gives us deeper insight into asset behaviour than conventional predictive maintenance tools.Process Improvement Global Excellence Team, Huntsman Polyurethanes on the collaboration.
Scope the problem, validate the cause with engineers and quantify the value of a prescribed change. For the pilot: no new sensors, no new control software and no live-system integration required.
Choose one unit and a recurring, costly failure, quality problem or process instability. Agree the scope and data handoff.
Build a model of the unit from historical plant data and process information.
Review the important relationships and root-cause findings with your process engineers.
Identify interventions, estimate their impact and build the case for whether and where to expand next.
Enter your annual downtime exposure per site, the share you believe is avoidable once the causes are known, and the number of sites in scope.
Bring one recurring plant problem. Review the unit, available data and potential value with our engineers, and define the scope for a pilot.
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