ADVANCE ROOT CAUSE ANALYSIS
This Advance Root Cause Analysis course designed to equip the participants with the knowledge and skills necessary to facilitate effective problem analysis.
Curriculum
- 2 Sections
- 14 Lessons
- 14 Hours
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- Course Outline:Understand the Methodology and Implementation of Advance Root Cause Analysis12
- 2.1Introduction and Overview – Understanding the role of Advanced RCA in organizational success.
- 2.2Concept of RCA – Principles, categories of root causes, and real-world importance.
- 2.3RCA Process – From detection to sustainable solution implementation.
- 2.4Problem and Problem Solving – Defining and scoping the problem effectively.
- 2.5Problem Identification (Level / Type of Problem) – Products / Services -> Process -> System – >Business.
- 2.6Development of the data matrix for analysis • Breaking down total variance to variation of interest and variation due to error • Data analytic (statistics) to derive the root cause(s) and irreversible corrective actions. • Case studies
- 2.7Applying RCA in Systematic Problem Solving – Linking RCA to continuous improvement and operational excellence.
- 2.8Standard Operating Procedures for conducting RCA – Roles, workflow, and governance.
- 2.9RCA Reporting Format – What stakeholders need to see for action and buy-in.
- 2.10RCA Report (Examples / Case Studies) – Learning from real investigstions.
- 2.11RCA Team Exercises / Group Discussion / Case Studies – Hands-on practice in applying RCA tools.
- 2.12Workshop Summary – Recap, lessons learned, and workplace action plan.
- DAY 22
- 3.1Deploying Factorial Experimentation for Root Cause Analysis • Experimentation with two factors • How to establish the experimental error for analysis for a two-factors experiment?
- 3.2Experimentation with four or more factors • Identification of problem • Brainstorming • Multi-voting • How to use to create an experimental matrix • Establishing the experiment error for analysis • Data analytic to establish the root cause(s) result from interacting factors and main factors Irreversible corrective action • Case studies
Development of the data matrix for analysis • Breaking down total variance to variation of interest and variation due to error • Data analytic (statistics) to derive the root cause(s) and irreversible corrective actions. • Case studies
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Standard Operating Procedures for conducting RCA – Roles, workflow, and governance.
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