Course : Leveraging AI to Manage Industrial Quality and Control NonconformitiesFrom identifying deviations to optimizing action plans through data analysis.
Practical course - 3d
- 21h00 - Ref. CQI
|
Download in PDF format
Share this course by email
 | Identify relevant use cases for AI in industrial quality processes |
| Using quality data to detect nonconformities and deviations |
| Analyze root causes using AI tools and quality methods |
| Develop and prioritize action plans based on reliable data |
| Assessing the Relevance and Limitations of AI Solutions in an Industrial Context |
Intended audience
Quality managers, quality engineers, quality technicians, production managers, continuous improvement managers, industrial project managers, and data analysts working in an industrial setting.
Prerequisites
None.
Practical details
A combination of theoretical instruction and hands-on practice. Case studies drawn from realistic industrial contexts. In-person or remote instruction.
Course schedule
1 Integrating AI into the Industrial Quality Management Process
- Define the key concepts of artificial intelligence (machine learning, deep learning, generative AI).
- Identify the contributions of AI to quality processes (8D analysis, FMEA, and PDCA).
- Map the available quality data (scrap, customer returns, inspections, sensors, metrics).
- Identify priority use cases based on industry challenges (cost of non-quality, risks).
- Understanding the limitations, biases, and conditions for success in AI projects.
Hands-on work
Case study in small groups: identifying AI use cases based on a hypothetical industrial process (mechanical manufacturing). Participants analyze the available data and prioritize two use cases based on impact and feasibility. Oral presentation and group discussion.
2 Collect and prepare quality data for AI
- Identify relevant data sources (ERP, MES, IoT sensors, quality reports).
- Structure and clean the data (handle missing values, normalize the data).
- Assess the reliability of the data and identify biases.
- Establish actionable quality metrics (KPIs, Cp/Cpk, defect rates).
- Use accessible tools (Advanced Excel, Python, Power BI) for data preparation.
Hands-on work
Guided exercises in pairs: cleaning and organizing a dataset of nonconformities in Excel or Power BI. Identifying data anomalies and creating simple metrics. Group analysis of the results.
3 Detecting Nonconformities and Deviations Using AI
- Understand anomaly detection methods (statistical and machine learning).
- Apply statistical process control (SPC) techniques enhanced by AI.
- Identify patterns of deviations based on historical data.
- Use analytical tools (Python, simple ML libraries, no-code AI tools).
- Interpret the results and ensure reliable detection.
Hands-on work
Hands-on group workshop: Implementing a simple anomaly detection model (using a no-code tool or a simplified notebook). Participants identify anomalies in a simulated real-world dataset. Debriefing on the results and limitations.
4 Analyzing Root Causes with the Help of AI
- Review traditional methods (Ishikawa, 5 Whys, FMEA, 8D).
- Combine quality data with process data to enhance the analysis.
- Use AI techniques to identify correlations and influencing factors.
- Prioritize root causes based on objective data.
- Develop an analytical approach that combines domain expertise and AI.
Hands-on work
Small-group simulation exercise: analysis of a complex non-compliance case. Participants use a cause-and-effect matrix supplemented with data from an analytical tool. They construct a reasoned cause tree and present their findings.
5 Develop and implement data-driven action plans
- Define appropriate corrective and preventive actions.
- Prioritize actions based on impact, cost, and feasibility (decision matrix).
- Implement monitoring metrics and dashboards (Power BI, Excel).
- Using AI to Predict the Effectiveness of Actions (A Simple Approach).
- Develop a continuous improvement process that incorporates AI.
Hands-on work
Group role-playing exercise: Developing an action plan in response to a major nonconformity. Participants prioritize actions and define monitoring indicators. Simulation of management using a dashboard. Presentation and group feedback.
PARTICIPANTS
Quality managers, quality engineers, quality technicians, production managers, continuous improvement managers, industrial project managers, and data analysts working in an industrial setting.
PREREQUISITES
None.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Quality managers, quality engineers, quality technicians, production managers, continuous improvement managers, industrial project managers, and data analysts working in an industrial setting.
PREREQUISITES
None.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.
Dates and locations
Select your location or opt for the remote class then choose your date.
Remote class
Dernières places
Date garantie en présentiel ou à distance
Session garantie
This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.