Guide: Statistical Process Control (SPC)
Statistical Process Control uses statistical methods to monitor and control production processes, ensuring product quality and operational efficiency through data analysis.
Guide: Statistical Process Control (SPC)
Statistical Process Control is a key method used in making sure products are made to specification and efficiently, especially in making expensive products like cars or electronics where margins can be low and the cost of defects can eliminate profitaility. It uses a detailed, numbers-based way to monitor, manage, and improve processes where product are made or services are provided.
SPC focuses on carefully examining data to help businesses understand problems or issues in how they make products or provide services. It’s all about making decisions based on solid evidence rather than guesses or feelings, with the goal of finding and fixing any changes in the process that could affect the quality of the product.
What is Statistical Process Control?
Statistical Proces Control is a key method used in quality control, and Lean Six Sigma used to maintain and improve product quality and efficiency. It is used in various industries but is primarily used in manufacturing as a systematic, data-driven approach to uses statistical methods to monitor, control, and improve processes and process performance.
SPC is a data-driven methodology that relies heavily on data to analyze process performance. By collecting and analyzing data from various stages of a manufacturing or service process, SPC enables organizations to identify trends, patterns, and anomalies. This data-driven approach helps in making informed decisions rather than relying on assumptions or estimations.
The main aim of SPC is to detect and reduce process variability. Variability is a natural aspect of any process, but excessive variability can lead to defects, inefficiency, and reduced product quality. By understanding and controlling this variability, organizations can ensure that their processes consistently produce items within desired specifications. SPC involves continuous monitoring of the process to quickly identify deviations from the norm. This systematic approach ensures that problems are detected early and can be rectified before they result in significant quality issues or production waste.
History and Background of the Development of SPC
Walter Shewhart and Control Charts
The foundations of SPC were laid in the early 20th century by Walter Shewhart working at Bell Laboratories. Shewhart’s primary contribution was the development of the control chart, a tool that graphically displays process data over time and helps in distinguishing between normal process variation and variation that signifies a problem. The control chart remains a cornerstone of SPC and is widely used in various industries to monitor process performance.
W. Edwards Deming and Post-War Japan
After World War II, W. Edwards Deming brought the concepts of SPC to Japan, where they played a key role in the country’s post-war industrial rebirth. Deming’s teachings emphasized not only statistical methods but also a broader philosophical approach to quality. He advocated for continuous improvement (Kaizen) and total quality management, integrating SPC into a more comprehensive quality management system.
Impact on Manufacturing and Beyond
The implementation of SPC led to significant improvements in manufacturing quality and efficiency. It allowed companies to produce goods more consistently and with fewer defects. The principles of SPC have since been adopted in various sectors beyond manufacturing, including healthcare, finance, and service industries, demonstrating its versatility and effectiveness in process improvement.
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Fundamental Concepts of SPC
To understand and read what control charts are telling you it is first important to understand how variation and how it might be displayed on the chart. In everything, there is always a level of variation relative to what is being measured. But we must identify what acceptable variation and what is a variation that needs to be explored and addressed.
Understanding Process Variation
In SPC, process variation is categorized into two types: common causes and special causes.
- Common Cause Variation: These are inherent variations that occur naturally within a process. They are predictable and consistent over time. In the image below common cause variation is the variation within the control limits
- Special Cause Variation: These variations are due to external factors and are not part of the normal process. They are unpredictable and can indicate that the process is out of control. In the image below, the special cause variation is the data point outside the upper control limit
Control Charts
Control charts are essential tools in SPC, used to monitor whether a process is in control.
- Graphical Representation: They graphically represent data over time, providing a visual means to monitor process performance.
- Control Limits: Control charts use upper and lower control limits, which are statistically derived boundaries. They help in distinguishing between normal process variation (within limits) and variations that require attention (outside limits).
Types of Control Charts
After understanding the types of variation you might find on a control chat, it is important to understand the types of control charts in SPC. This is crucial for effectively monitoring and improving various processes. These charts are broadly categorized based on the type of data they handle: variable data and attribute data. Additionally, the implementation of SPC in a process, from data collection to continuous improvement, is a systematic approach that requires diligence and precision. Let’s explore these aspects in more detail.
Variable Data Control Charts
Variable data control charts are used for data that can be measured on a continuous scale. This includes characteristics like weight, length, or time.
X-bar and R Chart
- Purpose: Used to monitor the mean (average) and range of a process.
- Application: Ideal for situations where sample measurements are taken at regular intervals and the mean and variability of the process need to be controlled.
- Structure: The X-bar chart shows how the mean changes over time, while the R chart displays the range (difference between the highest and lowest values) within each sample.
Individual-Moving Range (I-MR) Chart
- Purpose: Monitors individual observations and the moving range between observations.
- Application: Useful when measurements are not made in subgroups but as individual data points.
- Structure: The I chart tracks each individual data point, and the MR chart shows the range between consecutive measurements.
Attribute Data Control Charts
Attribute data control charts are used for data that are counted, such as defects or defective items.
P-chart (Proportion Chart)
- Purpose: Monitors the proportion of defective items in a sample.
- Application: Ideal for quality characteristics that are categorical (e.g., defective vs. non-defective) and when the sample size varies.
- Structure: It plots the proportion of defectives in each sample over time.
C-chart (Count Chart)
- Purpose: Tracks the count of defects per unit or item.
- Application: Used when the number of opportunities for defects is constant, and defects are counted per item or unit.
- Structure: It plots the number of defects in samples of a constant size.
Implementing SPC in a Process
Implementing SPC in a process is a structured approach that involves several key steps: data collection, establishing control limits, monitoring and interpretation, and continuous improvement. Each of these steps is critical to the successful application of SPC. Let’s explore these steps in more detail.
Step 1: Data Collection
First, data must be collected systematically to ensure it accurately represents the process. This involves deciding what data to collect, how often to collect it, and the methods used for collection. The selection of data is important. It should be relevant to the quality characteristics you want to control. For example, in a manufacturing process, this might include measurements of product dimensions, the time taken for a process step, or the number of defects.
The data should be representative of the actual operating conditions of the process. It means collecting data under various operating conditions and over a sufficient period.
The sample size and frequency of data collection should be adequate to capture the variability of the process. It’s a balance between collecting enough data for reliability and the practicality of data collection.
Step 2: Establishing Control Limits
Control limits are calculated using historical process data. They are statistical representations of the process variability and are usually set at ±3 standard deviations from the process mean.
These limits reflect what the process can achieve under current operating conditions.
To help you calculate your data control limits, you can use our Control Limits Calculator.
Control limits are not fixed forever. As process improvements are made, these limits may be recalculated to reflect the new level of process performance.
When significant changes are made to a process (like new machinery, materials, or methods), it might be necessary to recalculate the control limits based on new performance data.
Step 3: Monitoring and Interpretation
Regularly reviewing control charts is essential for timely detection of out-of-control conditions. Apart from individual points, it’s crucial to look for patterns or trends in the data, which could indicate potential issues.
When data points fall outside the control limits or exhibit non-random patterns, it triggers a need for investigation. The goal is to identify the root cause of the variation, whether it’s a common cause that requires a process change, or a special cause that might be addressed more immediately.
Step 4: Continuous Improvement
SPC is not just about maintaining control; it’s about continuous improvement. The insights gained from SPC should drive ongoing efforts to enhance process performance.
Based on SPC data, processes can be adjusted, improved, and refined over time. This might involve changes to equipment, materials, methods, or training.
Conclusion
In conclusion, SPC is a key tool in the aim for quality control and process improvement. Its strength lies in its ability to make process variability visible and manageable. From the seminal contributions of Walter Shewhart and W. Edwards Deming, SPC has evolved into a comprehensive approach that integrates seamlessly with various quality management systems.
By continuously monitoring processes through control charts and adapting to the insights these charts provide, SPC empowers organizations to maintain control over their processes and pursue relentless improvement. Thus, SPC not only sustains but also elevates the standards of quality, efficiency, and customer satisfaction in diverse industrial landscapes.
References
- Madanhire, I. and Mbohwa, C., 2016. Application of statistical process control (SPC) in manufacturing industry in a developing country. Procedia Cirp, 40, pp.580-583.
- Gérard, K., Grandhaye, J.P., Marchesi, V., Kafrouni, H., Husson, F. and Aletti, P., 2009. A comprehensive analysis of the IMRT dose delivery process using statistical process control (SPC). Medical physics, 36(4), pp.1275-1285.
Q: What is Statistical Process Control?
A: SPC is a method used to monitor, control, and improve processes by analyzing performance data to identify and eliminate unwanted variations.
Q: Why is SPC important?
A: SPC helps ensure processes are consistent and predictable. It aids in early detection of issues, reducing defects, and improving overall product or service quality.
Q: What is a control chart?
A: A control chart is a graphical representation used in SPC to plot process data over time, with control limits that help distinguish between common and special cause variations.
Q: How are control limits determined?
A: Control limits are typically set at three standard deviations above and below the process mean, based on historical data. However, these limits can be adjusted depending on the specific chart type and industry standards.
Q: What's the difference between common cause and special cause variation?
A: Common cause variation is the inherent variability in a process, while special cause variation arises from specific, unusual events and is not part of the normal process.
Author
Daniel Croft
Hi im Daniel continuous improvement manager with a Black Belt in Lean Six Sigma and over 10 years of real-world experience across a range sectors, I have a passion for optimizing processes and creating a culture of efficiency. I wanted to create Learn Lean Siigma to be a platform dedicated to Lean Six Sigma and process improvement insights and provide all the guides, tools, techniques and templates I looked for in one place as someone new to the world of Lean Six Sigma and Continuous improvement.
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