Before a manufacturer pays for a better prediction of equipment failure, it should ask what happened to the last useful warning. Did maintenance act in time, or did the alert wait for approval, spare parts, or a production window?
If the warning arrived early enough but the repair still happened too late, a better model may leave the costly delay untouched.
Coromandel International’s predictive-maintenance program in India shows why execution belongs in that assessment. According to supplier Infinite Uptime’s case study, the deployment combined equipment monitoring with automated maintenance recommendations and digital work orders. As of January 2026, the supplier reported 801 recommendations executed and 3,210 hours of unplanned downtime avoided across 27 plants.
These are vendor-reported, network-wide figures. They don’t trace an alert to a completed repair, and they don’t separate gains from better detection and faster execution. What they do show is a supplier counting how many warnings became completed work, a figure most buyers never ask for.
The investment question is therefore more demanding than whether a model can detect faults sooner: would a better prediction change when or how the plant intervenes?
The Overlooked ROI Constraint: Prediction-To-Execution Time
The savings depend on whether a warning enables maintenance before a failure disrupts production. That can require confirming the fault, securing parts, creating a work order, and arranging a production window.
Two plants using equally capable models could therefore get different results. One might send an alert directly into its maintenance planning system, along with the equipment’s history and details of the problem. Another might need someone to copy that information between applications before planning can start.
McKinsey’s 2025 survey of 101 senior operating executives highlights the implementation challenge: 46% reported limitations in their data or IT/OT systems, and only 2% said AI was fully embedded across operations. These figures do not measure maintenance delays directly, but they strengthen the case for checking operational readiness before funding model upgrades.
Identify The Constraint Before Spending
For a plant deciding where to invest, the first step is to distinguish prediction problems from response problems and then identify what causes the response to stall.
When prediction is the bottleneck, alerts arrive too late, miss failures, or raise false alarms that weaken trust. Better sensors, training data, diagnostics, or models help maintenance teams identify problems early enough to act.
When technology slows the response, equipment details may be missing, information may need to be entered repeatedly, or monitoring and maintenance systems may not exchange data. Targeted integration or better access to existing information is the fix.
Operational delays require a different response. Approval rules, spare-parts policies, unclear responsibilities, or production scheduling can hold up maintenance even when the technology works. These problems may need changes in how the plant operates rather than another software purchase.
Spare-parts sourcing makes this distinction concrete in India. In its 2024–25 annual report, Tata Steel identifies supply-chain disruption and geographically concentrated suppliers as sources of uncertainty in spare-parts availability. Its response includes developing domestic sources and improving inventory management.
The objective is a timely, well-planned intervention that avoids production losses. Repairing equipment immediately can be wasteful if the plant can safely wait for a scheduled shutdown.
Where Should Investment Go?
1. Is the prediction actionable?
Does the alert identify the equipment, explain how urgent the problem is, and arrive early enough for maintenance to respond? Check false alarms and missed failures. If technicians cannot understand or trust the warning, establish whether the problem lies in the prediction itself or the information provided with it.
2. Where does the warning time go?
Track when the alert arrives, when someone reviews it, and when maintenance is approved, scheduled, and completed. Separate necessary diagnosis and deliberate waiting for a suitable production window from avoidable delays. Examine individual cases as well as averages, which can hide urgent work held up for too long.
3. What causes the avoidable delay?
Follow the alert through maintenance, inventory, and production planning. Is work waiting because systems cannot share equipment details, because approval is pending, because parts are unavailable, or because no suitable production window has been agreed? Identify whether the constraint is technical, operational, or a combination of both.
4. Would a better prediction change the intervention?
Compare the warning time with the time needed to confirm the fault, secure parts, arrange qualified staff, and reach a suitable production window.
Consider a hypothetical upgrade that extends the warning from eight hours to twelve. If a replacement component requires 24 hours to source, extending the warning from eight to twelve hours may still leave the plant unable to intervene before failure. The immediate priority may be critical-spares inventory or alternative sourcing. But if earlier detection allows procurement to finish before failure, the model upgrade could be worthwhile.
The same test applies to improved accuracy: would fewer false alarms or missed faults materially change maintenance decisions?
Turning The Answers Into A Spending Decision
Before approving a model upgrade, establish whether its additional warning time would change when maintenance can be completed. If existing delays would consume that advantage, examine the response process first.
Fund better prediction when it enables an intervention the plant currently cannot make in time. Fund a technology fix when missing information or disconnected systems prevent action. Change approval rules, parts policies, responsibilities, or scheduling practices when those are the constraint—without assuming a new platform is needed.
Where more than one option could work, compare the production losses each could realistically prevent against its implementation and running costs. Measure success through avoided disruption and effective maintenance, rather than repair speed alone.
Connecting Prediction to Maintenance
The Coromandel case study reports a 93.47% implementation rate for maintenance recommendations. Its account links continuous monitoring and specialist analysis with digital work orders intended to accelerate execution and reduce manual intervention.
These are supplier-reported results across the deployment. The published account does not trace a specific alert through to a completed repair, report individual response times, or separate gains from better detection and maintenance execution.
Execution also needs verification. Confirming that equipment condition has improved after maintenance helps establish whether the intervention worked and whether similar future alerts warrant the same response.
Predictive maintenance delivers value when warnings change operational outcomes, not merely when models detect faults earlier. Before paying for better predictions, manufacturers need to establish whether the constraint is the warning or the plant’s ability to act on it.
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