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Industrial companies already have plenty of data: sensors, automation systems, historical records, energy indicators, quality, production. The problem is that this data is often still hard to use for maintenance: it\u2019s fragmented, lightly contextualized, or disconnected from what happens on the shop floor.<\/p>\n
Predictive maintenance aims for the opposite: using equipment data to anticipate failures and intervene at the right moment. For that to work, you need an industrial data foundation that is accessible, reliable, and usable across the organization.<\/p>\n
STI Maintenance<\/strong> is now a member of the AVEVA Partner Ecosystem<\/strong> program (Partner ID 966506). In practical terms, this strengthens our ability to connect maintenance, operations, and industrial data\u2014especially around AVEVA PI System.<\/p>\n In reality, the data exists, but maintenance decisions live elsewhere:<\/p>\n Result: many organizations want to \u201cdo predictive,\u201d but stay stuck at the \u201cwe have data\u201d stage without being able to turn that data into repeatable decisions.<\/p>\n Predictive maintenance relies on analyzing equipment data (sensors, monitoring, IoT) to detect anomalies and anticipate failures. The goal: reduce unplanned downtime and optimize interventions by acting when the data shows a drift, rather than on a fixed calendar.<\/p>\n Key point: this is not primarily an \u201cAI\u201d problem. It\u2019s a data quality + context problem, with a clear mechanism: \u201csignal \u2192 decision \u2192 action\u201d.<\/p>\n AVEVA PI System is commonly described as a solution that collects, cleans, stores, enriches, and visualizes real-time operational data, making it accessible and actionable for different profiles (operations, analytics, management).<\/p>\n The point isn\u2019t to archive tags. The point is to make data reliable and usable to power concrete use cases (maintenance, reliability, performance).<\/p>\n A CMMS\/ERP can manage execution: work orders, statuses, history, costs, parts, scheduling. Without a solid link to shop-floor data, what\u2019s often missing is the real condition of assets and early warning signals.<\/p>\n Value appears when you connect:<\/p>\n For high-criticality assets, a few well-chosen signals can be enough: temperature, vibration, current, pressure, flow. The goal is to spot a drift before it becomes a shutdown or an emergency.<\/p>\n The challenge isn\u2019t \u201cdo more,\u201d it\u2019s \u201cdo better.\u201d Data helps prioritize based on asset criticality, drift severity, operational context, and risks. You move from a \u201cpolitical\u201d backlog to a \u201cdata-driven\u201d backlog.<\/p>\n Fixed frequencies often create over-maintenance (too early) or under-maintenance (too late). Shop-floor data helps recalibrate: keep preventive where it makes sense and shift certain assets toward condition-based or predictive logic.<\/p>\n A good dashboard helps you decide: what to do, where to act, and with what priority. Dashboard design must be aligned with a clear decision\u2014otherwise it becomes simple reporting.<\/p>\n The classic trap is starting with technology. The right approach is to start with a use case that creates value, then build the data foundation around that use case.<\/p>\n The best start isn\u2019t \u201cdeploy everywhere.\u201d It\u2019s a short, realistic sequence:<\/p>\n If you have a digital maintenance initiative in mind (even at the \u201cidea\u201d stage), we can help make it concrete: choose a use case, validate available data, and define a first pilot around AVEVA PI System and your maintenance systems.<\/p>\nWhy industrial data remains underused (even when everything is instrumented)<\/h2>\n
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Predictive maintenance: a simple (and useful) definition<\/h2>\n
The role of AVEVA PI System: making industrial data usable<\/h2>\n
The real challenge: connecting data (real time) and execution (CMMS\/ERP)<\/h2>\n
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Concrete use cases (the ones that generate ROI)<\/h2>\n
1) Early detection of drifts on critical assets<\/h3>\n
2) Smarter prioritization of interventions<\/h3>\n
3) Optimizing preventive maintenance plans<\/h3>\n
4) Decision dashboards (not decorative)<\/h3>\n
What STI Maintenance can do (results-driven approach)<\/h2>\n
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Where to start (without a massive project)<\/h2>\n
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Discovery workshop (45 min)<\/h2>\n