COURSE OVERVIEW:

The core meaning of this course is the transformation of raw maintenance data into strategic intelligence using advanced statistical and analytical techniques. Most industrial facilities are "Data Rich but Information Poor," possessing massive amounts of CMMS records but lacking the analytical framework to use them. This course focuses on "Evidence-Based Maintenance," where decisions regarding task intervals, spare parts stocking, and asset replacement are driven by mathematical modeling rather than intuition.

The scope of this training involves the application of "Weibull Analysis" to determine failure patterns, "Life Data Analysis" to predict future outages, and "Monte Carlo Simulations" to model system risk. Participants will learn how to clean and "Munge" maintenance data to ensure accuracy before performing high-level analytics. The curriculum emphasizes the bridge between "Data Science" and "Reliability Engineering," teaching attendees how to utilize software and statistical tools to identify "Hidden Trends" that traditional reporting often misses.

Coverage includes the development of "Reliability Dashboards," the use of "Predictive Analytics" to forecast failure dates, and the calculation of "Asset Health Scores." Attendees will explore how to communicate complex statistical findings to "Non-Technical Stakeholders" in terms of cost and risk. By the end of this course, participants will be able to design a "Reliability Analytics Program" that provides a clear, quantitative justification for every maintenance dollar spent and every reliability initiative launched.

COURSE OBJECTIVES:

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

• Define the role of "Reliability Analytics" in modern asset management.
• Perform "Data Cleansing" on CMMS records to ensure analytical integrity.
• Master "Weibull Analysis" to identify "Infant Mortality" vs. "Wear-Out."
• Utilize "Life Data Analysis" to calculate B-Life and Mean Life.
• Conduct "Pareto and Jack-Knife" analysis to identify high-risk assets.
• Model system reliability using "Reliability Block Diagrams" (RBD).
• Apply "Monte Carlo Simulations" to predict future maintenance costs.
• Calculate "Asset Health Scores" using multi-variant data inputs.
• Design "Predictive Maintenance Dashboards" for real-time visibility.
• Use "Correlation Analysis" to link maintenance spend to asset output.
• Communicate "Analytical Findings" to senior management in financial terms.
• Implement a "Data-Driven Decision" workflow within the department.

TARGET AUDIENCE:

This course is designed for Reliability Engineers, Asset Analysts, Maintenance Managers, Data Scientists, and Performance Managers.

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.