SPC is a method of measuring and controlling quality by monitoring the manufacturing process. Quality data is collected in the form of product or process measurements or readings from various machines or instrumentation. The data is collected and used to evaluate, monitor, and control a process. SPC is an effective method to drive continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential.
After going through Statistical Process Control (SPC) process improvement training participants will understand and learn about:
- Understand the quality tools used to solve problems determined by SPC and the data collection process.
- Learn how to calculate basic statistical parameters.
- Understand different types of control charts and interpretation of SPC.
- Learn how to perform control analysis and take valid corrective actions to prevent re-occurrence.
- Understand the process capability analysis.
Who Should Attend
- Senior Management
- Quality Engineers
- Production Engineers
- Quality Managers
- Manufacturing Engineers
- Product Engineers
- Maintenance Personnel
- Machine Operators
Benefits of Attending
Statistical Process Control (SPC) is a proven and effective technique that helps teams and organizations monitor critical process outputs and drive continuous performance. Learning how to correctly collect process data and then how to properly construct and interpret control charts is required to compete in today’s worldwide marketplace. The benefits of this two-day training workshop are to introduce the formulas, statistical symbols, terminology, and interpretation of basic SPC.
Statistical Process Control (SPC) process improvement training workshop will offer the following benefits:
- Apply a structural approach to any improvements and problem-solving processes.
- Identify the difference between special cause variation and common cause variation.
- Implement control charts to evaluate process performance and process capability.
- Analyze information regarding process performance and process capability.
- Identify actions on the process to maintain performance or capability.
- Use of appropriate control chart to determine upper and lower limits for a given process.
1. Introduction to SPC
– Use and Importance of SPC in the industry
– Measures to the central tendency
– Variation and Shape
– Exploratory data analysis
– Basics and Conditional Probability
– Decision Trees
– Expected Value
2. Modeling Process Quality
– Describing Variation
– Important Discrete Distributions,
– Poisson Distribution
– Important Continuous Distributions
– Normal Distribution
– Exponential Distribution
– Gamma Distribution
3. Inference About Process Quality
– Statistics and Sampling Distribution
– Point Estimation of Process Quality
– Statistical Inference for a Single Sample
– Statistical Inference for Two Samples
4. Fundamentals of Hypothesis Testing
– Hypothesis – Testing Methodology
– Z Test of Hypothesis for the Mean
– One Tail Test
– T-Test of Hypothesis For the Mean
– Z Test of The Hypothesis for the Proportion
5. Simple Linear Regression
– Type of Regression Models
– Determining the Simple Linear Regression Equation Residual Analysis
– Measuring Autocorrelation: The Durbin – Watson Statistic
– Estimation of Mean Values and Prediction of Individual Values
6. Control Charts
– Introduction to Control Charts
– Variable Control Charts
– Attribute Charts
– Cumulative Sum Control Chart
– Exponentially Weighted Moving Average Control Chart
– Moving Average Control Chart
7. Process Capability
– Process Capability Analysis Using a Histogram or a Probability Plot
– Process Capability Ratios
– Process Capability Analysis Using a Control Chart
– Process Capability Analysis Using a Designed Experiment
– Process Capability Analysis with Attribute Data
8. Engineering Process Control and SPC
– Process Monitoring and Process Regulation
– Process Control by Feedback adjustment (Simple Adjustment Scheme, Adjustment Charts)
– Combining SPC and EPC
9. Confidence Interval Estimation
– Confidence Interval Estimation for the Mean
– Confidence Interval Estimation for the Proportion
Feedback From Past Participants
This workshop has helped us in prioritizing the design failures.
Our product Design capability has improved significantly.
I think the tool is essential for improving product design.
It is useful for all manufacturing companies.
The exercises were very helpful for a clear understanding of how to design products.