Introduction
Root Cause Analysis is a systematic problem-solving technique which mainly focuses on finding the 'root causes' of a problem or event. This Root Cause Analysis training course approach is used for maintaining quality and solving problems makes it ideal to use with various other approaches such as Six Sigma, Lean and Kaizen. This Root Cause Analysis course training provides a detailed look at RCA methodology that needs to be effective.
It helps delegates to become knowledgeable practitioners who can deploy Root Cause Analysis along with a range of tools and methodologies to find and solve business problems.
Goals
TRAINING OBJECTIVES
- Enhance problem-solving effectiveness by providing a model for more deeply analyzing problem situations.
- Clarify the difference between analytical and creative thinking, and when each is most useful.
- Promote the ability to provide problem-solving support in situations where one is not an expert in the process or technology involved.
- Expand the range of tools available for analysis of problem situations.
Training Methodology
Delegates in this Root Cause Analysis training course will learn by active participation through inspiring presentation tools and interactive training course and role-playing activities, presented in a lively, enthusiastic, and interesting style. Delegates will take part in topic exercises, case studies and the practical program.
Program Content
Day 1 - PROBLEM Solving WITH RCA
- International requirements: ICAO-SMS framework, EASA, FAA, Transport Canada, IATA.
- Problem-solving process.
- Why most problem-solving models don’t get to the root cause, and a solution.
- How analytical and creative thinking must be both separated and integrated.
- Difference between content and process thinking.
- How to ensure that the right problem is being worked on.
- Tools and filters for priority setting.
- Developing a clear and sufficient problem statement.
- How a SIPOC diagram can set boundaries and define interrelationships.
- Flowcharts to drill down into the right part of the process.
Day 2 - Identifying root causes
- Identifying Possible Causes.
- Options for selecting or eliminating causes.
- Logic trees as a cause and effect diagram on steroids.
- Data Collection.
- Population versus sampling; options for sampling.
- Check sheets, graphs, and tables for discrete data collection.
- Surveys, interviews, and field observation for opinions or less precise data.
Day 3 - Data Analysis
- Tools for discrete data analysis (run charts, histograms, Pareto diagram, modified scatter diagram, pivot tables).
- Tools for softer type data (affinity diagram, relationship digraph).
- Integrative data analysis tools.
- Consulting Case Study Practice.
- Participants role play consulting with an instructor on a problem.
- Review of key learning points.
Day 4 - Incident management
- Tools for discrete data analysis (run charts, histograms, Pareto diagram, modified scatter diagram, pivot tables).
- Tools for softer type data (affinity diagram, relationship digraph).
- Integrative data analysis tools.
- Consulting Case Study Practice.
- Participants role play consulting with an instructor on a problem.
- Review of key learning points.
- Day 4 - Incident management.
- Incidents/events and Human Error.
- How incident/accident analysis differs.
- Causes of and solutions for human error.
- Practice on a project relevant to the participants’ organization.
- Facilitation Skills.
- Process facilitation versus content expert.
- Facilitation roles and intervention choices.
- The importance of organizational change management issues.
- Some models for understanding resistance and planning change.
- Implementation, follow-up, and standardization.
- Corrective action.
- Corrective action plan development.
- Corrective action plan follow-up.
- IOSA Corrective Action Record requirement.
Day 5 - RCA Management/organizational Issues
- Statistical Hypothesis Testing and Ms Excel.
- What t, F, and ANOVA tests can do.
- How to do them in MS Excel.
- Using chi-square for count data.
- Drilling Down With Data.
- Seeing variation as a 3+ dimensional space.
- How to slice major components of that variation.
- Which tools to use for data comparisons.
- Management/organizational Issues Affecting Rca Projects.
- Cognitive biases that affect RCA.
- Impact of organizational culture.
- Structures/roles that can support RCA.
- Common root cause analysis errors.
