In today's active manufacturing and production environment, ensuring product quality and process consistency is vital. One of the most effective tools for monitoring and controlling various process conditions is a Shewhart control chart. ISO standards, especially IS 397 (Part 10): 2024 and ISO 7870-2: 2023, provide complete guidance regarding these chart applications.

Overview of IS 397 (Part 10): 2024 and ISO 7870-2: 2023

Statistical Quality Control (SQC) provides many ways to monitor and control processes during the world's production and the Shewhart Control Chart has been among the most widely used. In 1920s this chart was introduced by, Walter Shewhart, forms the backbone of Statistical Process Control (SPC), assisting manufacturers to distinguish and address variations in their processes. This Indian Standard (Part 10) which is identical to ISO 7870-2 : 2023 ‘Control charts Part 2: Shewhart control charts’ issued by the International Organization for Standardization (ISO) was adopted by the Bureau of Indian Standards on the recommendation of the Statistical Methods for Quality, Data Analytics and Reliability Sectional Committee and approval of the Management and Systems Division Council.

IS 397 (Part 10): 2024 and ISO 7870-2: 2023 underlined the principles which aim to provide a robust framework for effecting Shewhart control charts in production settings. This Standard focus on ways to monitor, control, and improve consistency of manufacturing processes, the variation can be identified as general causes (random variations inherent in the process) or assignable causes (variations due to specific factors, machine malfunctions, etc.). ISO 7870-2:2023 is based on the creation, implementation and continuous monitoring of the control chart. This standard ensures that minimal disruption and control of the production processes can be maintained, ensuring consistency and quality all over the manufacturing cycle.

Shewhart Control Charts: The Backbone of Process Control

Shewhart control charts work as one of the most essential tools in statistical process control (SPC). They help monitor time as well as variability of a process and distinguish between the natural randomness of the process (normal causes) and abnormal and potentially harmful (assigned causes).

The Shewhart control chart tracks time as well as statistical data points and compares them to predefined control limits. These bounds indicate the expected bounds of variation when the process is stable and free of any allotted causes. Data points outside of these control limits specify the presence of potential issues in the process, requiring investigation and curative action.

The use of Control charts in Statistical quality control includes plotting process data points and comparing both upper and lower control limits to zero. A control chart a visual representation of how a process performs over time, permitting for initial detection of any shifts, trends, or irregularities that could disturb product quality.

Scope

This document establishes a guide to the use and understanding of the Shewhart control chart approach to methods for statistical control of a process. The scope is particularly limited to no statistical process control methods in which only the Shewhart system of Charts has been used. Some Supplementary Material introduced in the chart, which is regular with the Shewhart approach, summarizes the warning limit of utilization, trend pattern analysis and process capabilities. Though, it is vital to note that some other types of control charts which can be used under different conditions based on the nature of the process and the data being analysed.

Key Terminology

The fundamental principles of Shewhart control charts, it's vital to recognize some key terminologies that form the substance of the methodology. These terms help define the concepts of variation, control, and process stability:

Shewhart Control Chart: A graphical tool used to monitor the stability and variation within a process over time. It plots data points and defines control limits to help distinguish between common cause variation and assignable cause variation.
Common Cause Variation: Natural, expected variations in a process that occur as part of normal operation. This type of variation is inherent to the system and doesn’t typically require intervention.
Assignable Cause Variation: Variations that arise from specific, identifiable factors outside of the normal process, such as machine malfunction or operator error. These cause disruptions in the process and need to be addressed promptly.
Control Limits: Upper and lower boundaries on a control chart that define the acceptable range for process variation. Data points that fall outside these limits suggest that the process may be out of control and needs attention.
Process Capability: A measure of the process's ability to consistently produce output that meets specified requirements. It's often assessed using the Cp and Cpk indices to gauge how well the process is performing within the defined limits.
Rational Subgrouping: Organizing data into subgroups that are homogenous in nature. This ensures that variations within each subgroup are due to common causes, while differences between subgroups are indicative of assignable causes, allowing for effective process control.

Also Read: IS 18750 (Part 6): 2024 Unani Medicine- Glossary of Terms Part 6 Standardized Terminology Used for Respiratory System Diseases

Key Principles of Shewhart Control Charts

The Shewhart control chart is a fundamental tool in statistical quality control. They assist businesses and ensure product consistency by identifying variations in the business process, and differentiate between general causes and assignment causes. Following are some key principles that explain the working of Shewhart control charts and why are they important in monitoring process.

Distinguishing between Common and Assignable Causes: Shewhart control charts aim to differentiate between variations arising naturally in the process (common causes) and those caused by external or unexpected factors (assignable causes). This difference helps in concentrating on addressing only the causes that need involvement.
Process Stability: A steady process works around the defined control boundary, whereas data points constantly come inside the boundary. Stability states that the process is assumed, however no corrective action is necessary until the point is out of control.
Categorization of Variation Types: The control chart classifies process varieties into inherent randomness (common cause) and disconcertion (can be assigned). Inherent variation ensures smooth operation of managed actions, while identification of assigned action causes allows targeted improvement.
Rational Subgrouping: Data collection is assumed to be rational subgroups, whereas variation in a subset is assumed to be due to common causes, while assigning variation to subgroups is due to common causes. This confirms Reliable and meaningful data analysis.
Sampling for Data Collection: The Shewhart chart relies on a collection of samples over daily intervals. These samples help track trends, see deviations and predict future process performance on the basis of previous behaviour.
Predictability: When a procedure is in statistical control, it turns into predictable. This lets operators to recognize potential issues and take preventive measures, guaranteeing that the process continues to meet quality standards over time.

Control Chart Procedure and Interpretation

The application of Shewhart control charts follows a systematic procedure to ensure that the process remains in statistical control. Following is an outline of the procedure followed in IS 397 (Part 10): 2024 for interpreting and maintaining these charts:

Underlying Principle of Control Charts: The Shewhart system relies on the assumption that the process is in control, with variations occurring randomly due to chance causes. As long as the individual plotted statistics do not fall outside of control limits and no obvious trends or patterns are evident, the process is considered stable.
Examining the R or S Chart: Before interpreting the X-chart (which monitors averages), it is critical to examine the range (R) or standard deviation (s) charts. These charts help detect shifts in process variability. Any data point that exceeds the upper control limit or shows an unusual pattern should prompt an investigation into potential assignable causes. Once these causes are identified, the affected subgroups are excluded from further calculations.
Homogenization for S or R Chart: After excluding subgroups influenced by assignable causes, the centreline and control limits are recalculated. The process is then re-examined to ensure that all remaining data points fall within the updated control limits, confirming that the process variability is under statistical control.
Homogenization for X Chart: Similarly, after the S or R chart is in control, the X chart (average chart) is adjusted by excluding the subgroups with assignable causes. The centreline and control limits for the X chart are recalculated to ensure that all data points fall within the revised limits.
Ongoing Monitoring: Once the process has been stabilized and is in statistical control, the control limits can be used for ongoing monitoring of the process. New subgroups are compared against these limits to detect any abnormal variation. However, it is important to update the control limits if any significant changes occur in the process.

Addressing Unnatural Patterns and Assignable Causes

A significant role of Shewhart control charts is to detect unnatural patterns that may specify basic problems in the process. In some cases, it is significant to examine the exisiting causes of the patterns to implement curative actions and ensure the process proceeds to statistical control. Some common unnatural patterns consist of:

Instability: Points falling outside control limits indicate issues that require immediate attention.
Stratification: A tight clustering of points near the centreline suggests minimal variation, which might indicate an issue with the measurement system or that the process is not running with sufficient variation.
Mixture: A tendency for points to cluster near control limits can suggest a mixture of different process conditions.
Cyclic Patterns: Regular oscillations in the data points indicate possible systemic issues, like operator shifts or mechanical problems.
Trend: A series of consecutive points moving in one direction suggests a shift in the process that requires intervention.

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Process Control, Capability, and Improvement

Process Control ensures that the manufacturing process operates within specified limits, providing consistency and predictability. It is through process control that manufacturers are able to identify when variation is due to random causes or when intervention is necessary to address specific issues.

Process Capability refers to the ability of a process to consistently produce products within specification limits. Continuous process improvement is also a critical aspect of Shewhart control charts. By constantly monitoring the process and identifying areas of variation, manufacturers can make informed decisions that lead to higher quality products, reduced waste, and better overall process performance.

Conclusion

Shewhart control charts, as outlined in IS 397 (Part 10): 2024 and ISO 7870-2: 2023, are indispensable tools in modern statistical quality control. They offer a clear, visual method for monitoring and regulatory process variations, helping manufacturers preserve steady product quality and improve their processes. The capability to differentiate between collective and assigned causes of dissimilarity ensures that corrective action can be taken quickly, refining product quality and minimizing waste.

Whether one is working in manufacturing, service industry or any sector, quality control is necessary, Shewhart control chart is necessary to understand and implement to ensure, more consistency, efficiency, reliability in your process. With regulation provided by ISO standards, the manufacturer can make sure that its procedures are consistent and that it produces high-quality products that meet customer expectations.

IS 397 (Part 10): 2024 ISO 7870-2: 2023 Methods for Statistical Quality Control during Production Part 10 Shewhart Control Charts

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