Predictive maintenance AI requires three data inputs: operational sensor data (vibration, temperature, current, acoustic, or process parameter measurements from IoT sensors or SCADA historians), maintenance history (work order records from SAP PM/EAM documenting when failures occurred and what maintenance actions were taken), and asset metadata (asset type, installation date, and operating parameters from SAP PM). The minimum viable dataset for initial model training is typically 12 to 24 months of sensor data covering at least 20 to 50 failure events for the asset class in scope. A data readiness assessment is the first deliverable of every EMUG PULSE engagement.
When a model predicts failure probability above a defined threshold within a specified horizon, the integration automatically creates a planned maintenance order in SAP PM with the relevant equipment number, maintenance activity type, required spare parts, and target completion date. Work order priority is set based on failure probability and asset criticality in SAP. Maintenance planners receive alerts in SAP PM with supporting prediction evidence — sensor trend graphs and feature importance — so they can validate the AI recommendation before releasing the work order. Integration uses SAP PM BAPIs and OData service APIs compatible with SAP S/4HANA, SAP ECC, and SAP EAM.
Condition monitoring tracks equipment health indicators and alerts when a measurement exceeds a defined threshold — it tells you that a condition is abnormal now. Predictive maintenance uses machine learning to analyze patterns in condition monitoring data over time and predict when a failure is likely to occur — giving you a future forecast rather than a present alert. The practical difference is that condition monitoring alerts you when a bearing has already started to degrade, while predictive maintenance tells you that based on the current degradation trajectory, this bearing is likely to fail in 14 to 21 days — giving you time to plan a maintenance intervention during a scheduled production break.
A focused predictive maintenance deployment covering one asset class at a single production site runs 12 to 16 weeks using the EMUG PULSE Framework. A multi-asset program covering three to five equipment categories across multiple sites runs 20 to 30 weeks. EMUG provides a validated model with measured performance on production-representative data within six to eight weeks of engagement start. The SAP PM integration and operational dashboard deployment typically adds four to six weeks after model validation.
EMUG addresses rare failure modes through three approaches. First, anomaly detection: unsupervised models trained on normal operating data that flag deviations from established normal behavior patterns without requiring labelled failure events. Second, transfer learning: applying models trained on similar asset classes from EMUG’s cross-client model library to the target asset type. Third, physics-informed machine learning: incorporating domain knowledge about failure mechanisms into the model architecture to reduce the training data required. EMUG selects the appropriate approach based on the data assessment results from the PULSE Profile phase.
ISO 55001 requires organizations to demonstrate that their asset management activities are based on the best available information about asset condition, risk, and performance. Predictive analytics directly supports this by replacing calendar-based maintenance strategies with condition-based and predictive maintenance strategies that use actual asset health data. EMUG documents ISO 55001 alignment through three deliverables: a maintenance strategy update showing how AI-enhanced condition monitoring changes the maintenance approach, a risk register update reflecting reduced failure probability from predictive interventions, and a performance measurement framework showing how predictive maintenance KPIs connect to ISO 55001 objectives.
Yes. EMUG delivers combined programs covering predictive maintenance (failure prediction connected to SAP PM work order creation) and supply chain prediction (material demand forecasting and supplier risk prediction connected to SAP IBP and SAP MM). The combined program creates a virtuous cycle: predictive maintenance generates more accurate future maintenance demand signals that improve spare parts demand forecasting accuracy, while improved material availability predictions reduce the maintenance delays caused by spare parts stockouts. Combined programs typically show 15 to 20 percent greater total ROI than separate programs.
EMUG delivers predictive analytics to automotive manufacturers (IATF 16949 maintenance alignment), aerospace and defense organizations (AS9100 MRO demand prediction), industrial machinery producers (ISO 55001 integration), energy, oil, and gas companies (pipeline integrity and process plant maintenance), and engineering services firms (project cost and schedule prediction). Delivery countries include Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic, UAE, Saudi Arabia, Qatar, Kuwait, Bahrain, India, China, Japan, South Korea, Malaysia, Thailand, USA, Canada, Mexico, Brazil, South Africa, Nigeria, and Kenya.
