COURSE INTRODUCTION
This program is designed to develop strong problem-solving capability using proven methodologies such as Toyota 8 Steps, 8D Discipline, and 5 WHY Analysis, enhanced with Data-Driven and AI-Augmented techniques.
It focuses on practical application, real case analysis, and prevention of recurrence through strong corrective actions and Poka Yoke design. Practical examples and guided discussion help participants connect the subject to decisions and challenges in their own workplace.
COURSE OBJECTIVES
By the end of this programme, participants should be able to:
- Explain the core concepts, terminology and working principles related to Practical Root Cause Analysis Data-Driven Approach with AI Assistance.
- Apply structured methods and industry good practices for RCA Foundation, Why RCA Fails, and Data-Driven RCA.
- Evaluate relevant quality & root cause situations and identify risks, gaps and improvement opportunities.
- Translate the learning into practical workplace actions and sound technical decisions.
COURSE OUTCOME
Upon completion, participants will be better prepared to:
- Assess relevant workplace situations using the concepts and methods covered in Practical Root Cause Analysis Data-Driven Approach with AI Assistance.
- Select and apply appropriate tools, checks or controls for RCA Foundation, Why RCA Fails, and Data-Driven RCA.
- Recommend actions that support structured investigation, evidence-based decisions and recurrence prevention.
- Communicate findings, improvement opportunities and next steps clearly to relevant stakeholders.
WHO SHOULD ATTEND
Engineers, Supervisors, Quality Personnel, Manufacturing Personnel, Technical Managers.
COURSE CONTENT
- Part 1: RCA Foundation: Introduction to RCA and problem-solving approach; RCA vs Firefighting; RCA Mindset (Jump to Conclusion, Smoke Screen, Fact vs Opinion)
- Part 2: Why RCA Fails: Common RCA failures in industry; Weak problem statements; 5 WHY logic breakdown
- Part 3: Data-Driven RCA: Importance of data in RCA; Data types and sources; Data visualization (Pareto, trend, stratification)
- Part 4: AI-Augmented RCA: Introduction to AI in problem solving; AI for problem definition improvement; AI for data analysis and pattern detection
- Part 4.8: Poka Yoke & Zero Defect: Limitations of traditional corrective actions; Introduction to Poka Yoke; Types of Poka Yoke (Prevention, Detection, Warning)
- Part 4.9: Control Hierarchy: Strength of corrective actions; Control hierarchy (Elimination to Human); Evaluating solution effectiveness
- Part 5: Practical Workshop: Case study analysis (traditional RCA); Group work (problem definition, Fishbone, 5 WHY); AI-assisted RCA analysis
- Presentation and feedback
