COURSE OVERVIEW:

The transition to Industry 4.0 has fundamentally changed the landscape of reliability engineering, moving the discipline from manual analysis to automated, data-driven insights. This course explores the practical application of digital technologies—such as IIoT, Artificial Intelligence, and Digital Twins—to enhance the reliability and availability of industrial assets. It focuses on how to leverage these tools to create a "connected" maintenance ecosystem where data is collected in real-time and translated into prescriptive maintenance actions.

The scope of this training involves the detailed study of "Digital Reliability" use cases, ranging from automated vibration analysis to AI-driven spare parts forecasting. Participants will learn how to design a digital architecture that integrates operational technology (OT) with information technology (IT), ensuring a seamless flow of data across the enterprise. The course emphasizes the creation of a realistic "Digitalization Roadmap," helping organizations avoid the common pitfalls of "technology for technology's sake" and focusing instead on measurable business value.

Coverage includes the evaluation of wireless sensing technologies, the role of "Edge Computing" in data processing, and the application of machine learning for anomaly detection. Attendees will explore the human side of digitalization, focusing on the upskilling of the workforce and the management of organizational change. By the end of this course, participants will be able to lead their organizations through a digital transformation that significantly improves asset health, reduces maintenance costs, and enhances overall operational excellence.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Define the core components of "Reliability 4.0" and digital asset management.
• Identify high-value digital use cases tailored to their specific industry.
• Design a "Digital Twin" strategy for critical industrial equipment.
• Evaluate wireless and IIoT sensor technologies for different environments.
• Understand the role of Machine Learning and AI in predictive maintenance.
• Develop a multi-year Digitalization Roadmap with clear ROI milestones.
• Integrate digital monitoring data with existing CMMS and EAM systems.
• Address cybersecurity risks in connected maintenance and OT environments.
• Utilize Augmented Reality (AR) for enhanced field maintenance and training.
• Lead the cultural change required for a successful digital transition.
• Measure the success of digitalization through advanced data analytics.
• Select the right technology partners and platforms for the digital journey.

TARGET AUDIENCE:

This course is designed for Reliability Engineers, Digital Transformation Leads, Maintenance Managers, IT/OT Managers, Asset Managers, and Innovation Specialists.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.