Planned Maintenance Optimization (PMO)

Course Description

This advanced program combines the agility of the PMO (Planned Maintenance Optimization) methodology with the precision of statistical analysis for asset diagnostics. Based on the premise that up to 80% of failure modes do not follow an age-related wear-out pattern, the course teaches how to use historical data to scientifically distinguish between random failures and wear-out failures.

Through tools such as Weibull, Jack Knife, and ISO 14224, participants will learn to refine their current strategies, eliminating ineffective tasks and focusing resources on the failure modes that truly impact availability and risk.

Training Methodology

The focus is “Data-Driven Diagnosis & Action”:

Statistical Laboratory (Weibull Lab): Use of software (such as Minitab or Excel) to analyze real Time-to-Failure (TTF) data and determine the “Beta” parameter of the bathtub curve.
Reverse Engineering (Reverse RCM): We start from the current plan to validate or discard it—a key PMO strength versus traditional RCM.
Prioritization Workshops: Construction of Jack Knife diagrams to visually separate chronic issues from acute ones and assign the correct strategy.
Decision Simulation: Students must decide whether a task should be “Preventive” or “Run-to-Failure” based on statistical evidence from their own data.

Learning Objectives

Diagnose failure behavior (infant mortality, random, or wear-out) by applying Weibull analysis to select the correct maintenance tactic.
Evaluate the quality of historical maintenance data and structure it under ISO 14224 to feed reliability models.
Analyze failure dispersion and repair times using Jack Knife charts to identify “Bad Actors.”
Assess the technical validity of current tasks, determining whether they address the statistically diagnosed failure mode or are irrelevant tasks.
Design an optimized maintenance plan that integrates statistics-based decisions with the empirical experience of the technical team.

Target Audience

Reliability Engineers: Who need statistical tools to justify changes to maintenance frequencies.
Maintenance Planners: Responsible for optimizing resources based on real data and not only on OEM recommendations.
Maintenance Managers: Who seek to optimize the maintenance budget by eliminating unnecessary Preventive Maintenance (PM).

Diverse Learning Materials

Supportive Peer Network

Interactive Video Tutorials

Personalized Mentorship Sessions

20 Hours of Course

Access to Session Recording