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City	Date	Duration	Price	Status
AmsterdamNetherlands	October 9, 2026	7 days	€4,900	Confirmed date
Kuala LumpurMalaysia	November 20, 2026	7 days	€4,800	Confirmed date
OnlineOnline	December 31, 2026	7 days	€2,400	Confirmed date
TunisTunis	February 11, 2027	7 days	€4,400	Confirmed date
GenevaSwitzerland	March 25, 2027	7 days	€4,900	Confirmed date
LisbonPortugal	May 6, 2027	7 days	€4,800	Confirmed date

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Course Information

Duration

1 Weeks

Introduction

Predictive Maintenance Using AI equips organizations to shift from reactive repairs and time-based servicing to intelligence-led reliability strategies that protect production capacity, safety performance, and asset value. By combining operational data, sensor signals, historical work orders, and contextual process information, leaders can forecast failure risk, optimize maintenance windows, and align resources to the highest business impact—reducing unplanned downtime while improving cost predictability. The modern challenge is not a lack of data but translating noisy, fragmented, and biased datasets into decisions that maintenance and operations teams trust and act on consistently; this requires disciplined governance, clear success metrics, and cross-functional ownership. The opportunity is significant: AI-driven detection of early degradation, faster root-cause insights, smarter spare-parts planning, and performance benchmarking across sites and fleets. This course addresses practical implementation realities—data readiness, model selection, change adoption, and value tracking—so participants can deliver measurable reliability improvements, achieve higher equipment availability, and build a sustainable maintenance decision system that supports executive accountability and operational excellence.

Course Objectives

Define measurable predictive maintenance success criteria aligned to asset criticality, safety, and production priorities
Assess data readiness and identify gaps in sensors, historian data, and maintenance records impacting model performance
Build a structured failure-mode approach that connects equipment physics, operating context, and degradation signals
Select appropriate analytics methods for anomaly detection, forecasting, and remaining useful life estimation by use case
Design model validation plans using performance metrics that reflect operational decision quality and risk tolerance
Implement governance for data quality, model lifecycle, drift monitoring, and controlled deployment to operations
Integrate AI insights into maintenance planning workflows, work management systems, and reliability reporting
Quantify business value using downtime avoidance, maintenance cost optimization, and spare-parts efficiency indicators
Lead cross-functional adoption using stakeholder mapping, operating procedures, and training for sustained execution

Target Audience

Maintenance and reliability managers, operations leaders, asset integrity engineers, digital transformation managers, data analysts supporting industrial operations, plant and facility managers, engineering supervisors, maintenance planners and schedulers, condition monitoring specialists, and technical leaders responsible for performance, uptime, and lifecycle cost outcomes.

Benefits for the Organization

Reduced unplanned downtime through earlier failure risk identification
Improved asset availability and throughput via optimized maintenance timing
Lower maintenance cost by targeting interventions where they deliver impact
Stronger safety and compliance performance through risk-informed maintenance decisions
Better inventory and spare-parts planning using demand signals and criticality logic
Standardized reliability governance with measurable performance reporting and accountability

Benefits for the Trainee

Practical ability to translate operational data into actionable maintenance decisions
Stronger competence in selecting and validating AI approaches for reliability use cases
Clear methods to align predictive maintenance initiatives with executive priorities
Improved skill in building cross-functional workflows between operations and maintenance
Enhanced capability to measure value and communicate results to stakeholders
Confidence to manage model lifecycle, data quality, and adoption challenges at scale

Course Outline

Day 1 – Strategy, Asset Criticality, and Use-Case Selection

Predictive maintenance value chain: from signals to decisions to outcomes
Asset criticality analysis and prioritization of equipment portfolios
Failure modes, symptoms, and decision thresholds for intervention
Data landscape: sensors, historians, CMMS records, and operational context
Defining measurable success metrics: downtime, cost, reliability, and risk
Building an implementation roadmap with roles, governance, and accountability

Day 2 – Data Readiness and Reliability Data Engineering

Data quality dimensions: completeness, accuracy, timeliness, and consistency
Feature design from vibration, temperature, pressure, current, and process data
Maintenance record structuring: work orders, failure codes, and intervention labels
Handling imbalance, rare failures, noise, and missing values in operational datasets
Building a repeatable data pipeline for training, scoring, and monitoring
Practical documentation standards for traceability and audit-ready decisions

Day 3 – AI Methods for Predictive Maintenance Applications

Anomaly detection patterns and selecting the right baseline behaviors
Classification and risk scoring for failure likelihood and maintenance prioritization
Forecasting and remaining useful life concepts for planning maintenance windows
Model explainability to improve trust and accelerate operational adoption
Validation and testing: avoiding leakage, ensuring realism, and stress-testing scenarios
Translating model outputs into decision rules for planners and supervisors

Day 4 – Deployment, Integration, and Operational Adoption

Designing end-to-end workflow integration with planning and work management systems
Alert management: thresholds, escalation paths, and reducing false positives
Model monitoring: drift detection, retraining triggers, and performance dashboards
Governance and controls: versioning, approvals, and operational change management
Cybersecurity and access controls for operational data and analytics outputs
Building competency: training plans, operating procedures, and adoption measurement

Day 5 – Value Realization, Scaling, and Executive Reporting

Value tracking: downtime avoidance, maintenance optimization, and inventory impact
Root-cause learning loops: converting insights into reliability improvements
Scaling across sites and fleets: standardization vs local adaptation
Building a reliability operating model with clear ownership and decision cadence
Executive reporting: KPI storytelling, risk visibility, and performance accountability
Capstone: a practical predictive maintenance business case and rollout plan

Course Duration

Duration: 1 Weeks

Duration: 5 days
Format: Classroom / Online / Blended

Instructor Information

“The training will be delivered by a team of experts specialized in negotiation and professional relationships. They have extensive practical experience in managing complex negotiations, as well as a strong record in delivering leadership and management development programs.”

Conclusion

This course enables leaders and technical professionals to turn AI-enabled predictive maintenance into a disciplined operating capability—one that improves uptime, reduces cost volatility, and strengthens accountability for reliability outcomes. By focusing on measurable priorities, data readiness, practical model deployment, and adoption mechanisms that translate insights into action, participants will be positioned to deliver sustained performance improvement and build executive confidence in data-driven maintenance decision-making.

Business & Management

Technical Courses

Netherlands

United Arab Emirates

Switzerland