EMUG Tech delivers predictive analytics across six manufacturing and engineering application areas: predictive maintenance for rotating equipment (bearings, gearboxes, pumps, compressors, spindles) using vibration, temperature, and acoustic sensor data; remaining useful life (RUL) estimation for life-limited components; supply chain demand forecasting with SAP MM/PP integration; supply chain risk and disruption prediction; energy consumption and OEE optimisation; and fleet-level asset reliability analytics. All programmes follow the EMUG PULSE Framework — five phases covering Profile, Unify, Learn, Score, and Evolve — ensuring models reach production deployment and remain accurate through continuous monitoring and retraining governance.

EMUG PULSE is the five-phase predictive analytics delivery methodology: Profile — data landscape assessment, use-case scoping, and data readiness profiling; Unify — data pipeline build consolidating IoT, SAP PM/EAM, MES, and SCADA data into a unified feature store; Learn — model development, training, and benchmark validation against acceptance criteria; Score — production deployment with SAP/MES integration and operator dashboard delivery; Evolve — model governance, retraining pipeline, drift detection, and business value tracking. PULSE addresses the most common failure of predictive analytics programmes: deploying accurate models that have no integration with SAP or MES and therefore never influence actual maintenance or planning decisions.

Rotating equipment predictive maintenance requires: vibration sensors (accelerometers at bearing locations, 1–10 kHz sampling), temperature sensors at bearing housings and motor windings, and optionally acoustic emission sensors and current signature monitors. Minimum data requirement: 12 to 24 months of historical sensor data with documented failure events for at least 5 failure instances per failure mode. For organisations without existing sensor infrastructure, EMUG PULSE covers sensor selection, placement specification, edge hardware selection (National Instruments, ADLINK, Advantech), historian database configuration (OSIsoft PI, GE Proficy, Ignition), and data pipeline build in the Unify phase.

EMUG PULSE integrates predictive analytics with SAP PM/EAM through the Score phase: when a predictive model’s failure probability score exceeds the defined threshold for an asset, an automated integration creates a SAP PM planned maintenance notification or order with the asset tag, predicted failure mode, recommended maintenance action, and time-to-failure estimate as structured data. The integration uses SAP PM standard BAPIs or the SAP Asset Intelligence Network (AIN) API for cloud-connected assets. Bi-directional integration feeds SAP PM work order completion data back into the predictive model as labelled maintenance events to improve retraining accuracy.

Supply chain demand forecasting models require: minimum 24 to 36 months of historical demand data per SKU or material, with longer history improving seasonal pattern detection; promotional and pricing event history if applicable; external regressor data (economic indicators, commodity prices, customer order books) for models with external drivers; and hierarchical aggregation structure (product family, site, region) for hierarchical time-series models. EMUG PULSE data profiling in the Profile phase assesses demand history quality, identifies missing data periods, and recommends imputation and cleansing approaches before model training begins. SAP MM sales order and delivery history is the primary data source for manufacturing organisations.

Predictive maintenance model accuracy is measured on three dimensions: precision (percentage of maintenance alerts that correspond to actual failures — minimising unnecessary maintenance), recall (percentage of actual failures that were predicted before occurrence — minimising missed failures), and lead time (how far in advance the model predicts failure — the practical planning window). EMUG PULSE programmes define acceptance criteria for each metric at the Profile phase — typically targeting precision above 70 percent, recall above 80 percent, and lead time of 2 to 6 weeks for rotating equipment. These benchmarks vary by equipment criticality: higher-criticality assets justify higher recall at the cost of precision (accepting more false positives to avoid missed failures).

EMUG PULSE Evolve phase establishes a model governance and retraining pipeline: automated performance monitoring tracks precision, recall, and alert rate against production baselines; data drift detection identifies when incoming sensor data distributions shift from training data (caused by equipment age, operating condition changes, or sensor calibration drift); scheduled retraining pipelines update models on a defined cadence with new labelled failure data; and quarterly model performance reviews with maintenance engineering stakeholders validate that model outputs remain operationally relevant. This governance prevents the silent accuracy degradation that affects predictive maintenance programmes deployed without formal MLOps.

Predictive analytics programmes are delivered across automotive OEMs and Tier 1 suppliers, aerospace and defense manufacturers, industrial machinery and equipment producers, energy and process industries, and high-tech electronics firms in 20 countries: Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic in Europe; India, Japan, South Korea, China, Malaysia, Thailand in Asia-Pacific; UAE and Saudi Arabia in the Middle East; USA, Canada, Mexico, Brazil in the Americas. Predictive analytics delivery from Hyderabad, Germany, and Dubai with on-site sensor infrastructure and SAP PM/EAM integration capability at client manufacturing and energy facilities globally.