Course Schedule & Pricing
| Date | Location | Mode | 1 Delegate | 2 - 4 Delegates | 5 or More | |
|---|---|---|---|---|---|---|
| 16–17 Jul 2026 | South Africa | Onsite | $2,795 | $2,595 | $2,395 |
Course Overview
Today’s process and manufacturing industries are complex and asset-intensive, requiring maintenance practices that ensure high reliability, maximize uptime, minimize costs, and maintain process safety and asset integrity. Effective maintenance requires understanding current practices, identifying development areas, execution capabilities, and realistic implementation plans.
Skills to leverage modern technologies, such as sensors with the Industrial Internet of Things (IIoT), are critical. Applying asset management in this manner supports sustainable operation, and this masterclass provides the skills to achieve this.
The course emphasizes interaction and cooperation between engineering, operations, and maintenance to achieve optimum plant reliability. Practical examples of significant failures illustrate the consequences of misunderstanding roles, responsibilities, and interfaces between these functions.
Learning Outcomes
- Understand operational asset efficiency, associated risks, and ways to increase availability, achieve production targets, and reduce costs.
- Learn fundamentals of Reactive Maintenance, Preventive Maintenance (PM), Predictive Maintenance (PdM), and Impact Driven Asset Management (IDAM).
- Understand the Digital Twin concept and its practical benefits in heavy industry.
- Identify key aspects of equipment-level reliability to optimize in-service time, cost, and asset utilization.
- Benefit from artificial intelligence, machine learning, and IIoT to transform daily maintenance operations.
- Apply reliability-based maintenance concepts to current strategies, avoid common pitfalls, and realize short and long-term gains.
Course Outline
Day 1 — Fundamentals of Maintenance Strategies & Digital Concepts
- Fundamentals of Reactive, Preventive, and Predictive Maintenance (PdM)
- Transitioning to Impact-Driven Asset Management (IDAM)
- Understanding operational asset efficiency and associated risks
- Introducing the Digital Twin concept and its practical benefits
- Case studies: Analyzing significant failures and cross-functional interfaces
Day 2 — Implementing AI, IIoT & Reliability-Based Maintenance
- Leveraging Artificial Intelligence, Machine Learning, and IIoT in maintenance
- Identifying key aspects of equipment-level reliability
- Optimizing in-service time, reducing costs, and maximizing asset utilization
- Applying reliability-based maintenance concepts to current strategies
- Avoiding common pitfalls and developing a realistic digital transformation roadmap
Frequently Asked Questions
What is digital transformation in maintenance?
Digital transformation in maintenance involves integrating advanced technologies like Artificial Intelligence (AI), Industrial IoT (IIoT), and machine learning into daily operations. It shifts asset management from a reactive, calendar-based approach to a highly efficient, data-driven strategy that maximizes asset lifespan and significantly reduces unplanned downtime.
What is the difference between preventive and predictive maintenance (PdM)?
Preventive maintenance is performed on a fixed schedule regardless of the actual condition of the equipment, which can sometimes lead to unnecessary labor and parts costs. Predictive maintenance (PdM), on the other hand, uses real-time sensor data to predict exactly when a machine is likely to fail, allowing maintenance to be scheduled precisely when it is needed.
What is a digital twin in asset management?
A digital twin is a dynamic virtual replica of a physical asset, process, or complete plant ecosystem. By utilizing real-time data from IIoT sensors, a digital twin simulates equipment behavior, monitors health, and tests potential maintenance strategies safely in a digital environment without risking actual operational disruptions.
How does Industrial IoT (IIoT) improve plant reliability?
Industrial IoT continuously connects physical equipment to digital monitoring networks via smart sensors. This continuous stream of real-time operational data allows engineering teams to monitor machine health 24/7, detect minute early-warning signs of performance degradation, and execute impact-driven maintenance interventions before a breakdown occurs.
How can Artificial Intelligence (AI) reduce maintenance costs?
AI algorithms analyze massive volumes of historical and real-time operational data to detect hidden failure patterns that humans might miss. By accurately forecasting equipment failures before they happen, AI prevents catastrophic secondary damage, optimizes spare parts inventory holding costs, and extends overall equipment in-service time, leading to major cost reductions.