Statistical Process Control
Statistical Process Control (2 days)
Statistical Process Control (SPC) is a quality management technique that uses statistical methods to monitor and control processes. It involves collecting and analyzing data to determine if a process is in control or experiencing variations. SPC helps identify trends, detect deviations from desired performance, and enables proactive interventions to maintain process stability and improve overall quality. Applying statistical process control (SPC) on big data without sampling offers several advantages:
- Increased Accuracy: Working with the entire dataset rather than a sample provides a more accurate representation of the process behavior, as it captures all variations and patterns within the data. This accuracy allows for more precise control limits and insights into process performance.
- Enhanced Sensitivity: Analyzing big data enables greater sensitivity in detecting small process variations or anomalies that may not be evident in smaller samples. This increased sensitivity can help identify potential issues or opportunities for improvement that might have been missed with traditional sampling methods.
- Real-time / Inline Monitoring and Assessment: Big data analysis allows for real-time monitoring and control, providing immediate feedback on process performance. This enables timely interventions, proactive decision-making, and the ability to address issues before they escalate, leading to improved process stability and product quality.
- Scalability and Flexibility: SPC applied on big data offers scalability and flexibility, accommodating the ever-increasing volume, velocity, and variety of data generated in modern manufacturing processes. It enables the analysis of large datasets without compromising the representativeness or generalizability of the results.
This course introduces the basic concepts and principles underlying Statistical Process Control. Furthermore you will learn how to use Python software to execute all relevant SPC methods on large unsampled data volumes.
The theoretical topics of the training will be intensified by hands-on exercises, based on real-world IIoT datasets. By default, all exercises are performed with Python.
Course Outline
- Introduction to SPC
- SPC and variation
- Common SPC plots
- Introduction to Control Charts
- Types of Control Charts
- Select best-suited Control Chart
- Interpretation of Control Charts
- Performance and Capability Indices
- Essential steps to prove Process Capability
- Stationary vs non-stationary processes
- Identifying Distribution Models
- Assessing process results on the basis of the distribution model
- The Capability Analysis & Process Monitoring Pipeline
- Measurement Capability
- Machine Capability
- Short-term Process Capability
- Long-term Process Capability
- Time-dependent Distribution Models
- Introduction of time models
- Calculation of Location
- Calculation of Dispersion
- Process-related Control Limits
- Tolerance-related Control Limits
- Measurement of Uncertainty
- Process Capability Assessments
- Implementing Inline SPC
Who should attend
Managers of quality control & quality management, production plants, production planning, and production operations. Quality experts who want to learn about the new capabilities of big data analytics in quality control. Data Scientists who want to apply their capabilities into practice in the field of big data based quality control. Consultants whose objective is to put additional value on the latest innovative technology in the smart factory sector.
What you need to succeed
Basic programming skills in Python (e.g. our training courses Python for Smart Factory Analytics or Getting started with Python).
Did not find the training you are looking for? Please feel free to ask for any other Advanced Analytics training.