AIAG & VDA SPC in Practice (Practitioner Training)

What will I learn?

• Key SPC concepts and terminology
• Requirements for effective SPC application 
• Process stability, performance, capability, and selecting appropriate control charts 
• How to plan, conduct, evaluate, and improve Machine Performance studies 
• How to identify and apply SPC for normal and non-normal distributions 
• How to plan, conduct, evaluate, and improve Ongoing Process Capability 
• Best practices for documenting and reporting SPC results 

Who should attend?

This course is designed for SPC practitioners, including quality engineers, manufacturing engineers, industrial engineers, and other multidisciplinary team members who are responsible for implementing and managing the application of Statistical Process Control techniques in manufacturing environments 

Agenda

Day 1

• Basic statistical concepts 
• Foundational linkages between SPC, the Quality Management System (QMS), and the Automotive Core Tools 
• Requirements for SPC application 
• Process Performance (PpK) versus Process Capability (Cpk) Indices 
• Machine performance for releasing production equipment 
• Process performance and capability for releasing manufacturing processes 
• Controls charts to monitor process stability and ongoing capability 

Day 2

• OCAP and process log documentation 
• Selection of control charts for ongoing process monitoring 
• Differences between retrospective (analysis) control charts and SPC control charts 
• Rational sampling and rational subgrouping, including evaluation of control chart effectiveness (OC and ARL) 
• SPC approach for processes with irremovable disturbance causes (time-dependent distribution models) 
• One-sided tolerance limits 
• Reporting of SPC studies 
• Effective use of SPC software

Requirements

Basic knowledge of manufacturing processes, measurement systems, statistics*, and use of electronic spreadsheets (MS Excel®). No prior SPC training is required. 

*Theoretical knowledge of general statistical terms (e.g., population, discrete characteristic, continuous characteristic, sample, average, range, standard deviation, random sample, run chart, bar chart, probability, histogram).