A Control Chart, also known as Shewhart chart or process-behavior chart, is a statistical tool used in data analysis, particularly in quality control processes. It is a type of graph used to study how a process changes over time. This article will delve into the intricacies of Control Charts, their purpose, types, construction, interpretation, and application in business analysis.
Control Charts are a fundamental tool in statistical process control (SPC) because they help determine the stability of the process. They are used to ascertain if a business or manufacturing process is in a state of statistical control. If the process is not in control, the chart can help identify issues that need to be addressed to bring the process back into control.
History and Purpose of Control Charts
The concept of Control Charts was first introduced by Dr. Walter A. Shewhart while he was working at Bell Laboratories in the 1920s. He developed the tool as a means to improve the quality of telephone transmission systems. Since then, Control Charts have been widely adopted in various industries for quality control and process improvement.
The primary purpose of a Control Chart is to monitor, control, and improve process performance over time by studying variation and its source. The chart separates variation due to special causes and common causes, and it helps us predict future process performance. The control limits on the chart indicate whether the process is in control or out of control.
Common Causes and Special Causes
Common causes of variation are inherent to the process; they are a result of the way the process was designed and are always present. These causes create a stable and predictable level of variation. On the other hand, special causes of variation are not part of the process design. They arise unexpectedly and cause the process to become unstable or unpredictable.
By identifying and eliminating special causes, a process can be brought into control. A Control Chart helps in identifying these special causes. Once a process is stable, the Control Chart can be used to monitor the process for changes in the level of common cause variation.
Types of Control Charts
There are several types of Control Charts, each designed to analyze different types of data. The choice of chart depends on the type of data you have (continuous or attribute), the subgroup size, and the sampling strategy. The most common types include the X-bar and R chart, the Individual and Moving Range (I-MR) chart, the p-chart, and the u-chart.
Each type of Control Chart serves a specific purpose and is used under different circumstances. Understanding the type of data and the purpose of analysis is crucial to select the appropriate chart. The following sections will delve into the details of these common types of Control Charts.
X-bar and R Charts
The X-bar and R chart is used when you have a subgroup size greater than one and you are dealing with continuous data. The X-bar chart monitors the changes in the average value of the process, while the R chart monitors the changes in the process variability.
These charts are usually used together. If both charts indicate that the process is in control, you can conclude that the process is statistically stable. However, if either chart shows an out-of-control signal, it suggests that there are special causes of variation in the process.
Individual and Moving Range (I-MR) Charts
The Individual and Moving Range (I-MR) chart is used when you have a subgroup size of one. The Individual chart (I-chart) monitors the process level, while the Moving Range chart (MR-chart) monitors the process variability.
Like the X-bar and R charts, these two charts are used together. They are particularly useful when you cannot collect data in subgroups, or when you want to monitor every single data point in your process.
Construction of Control Charts
Constructing a Control Chart involves several steps, starting with data collection and ending with chart interpretation. The process begins with defining the problem or process performance characteristic that you want to monitor. Then, you collect data, decide on the type of Control Chart to use, calculate control limits, plot the data, and finally interpret the chart.
The following sections will provide a detailed explanation of each step in the construction of Control Charts.
Data Collection
Data collection is the first step in constructing a Control Chart. The data should be collected in a manner that represents the process accurately. This may involve collecting data in a random manner or in a sequence that represents the process flow. The data should be collected over a sufficient period to cover the variation in the process.
Once the data is collected, it is organized into a suitable form for analysis. This could involve arranging the data in chronological order or grouping the data into subgroups. The choice of data organization depends on the type of Control Chart to be used and the nature of the process.
Selection of Control Chart
The next step is to select the appropriate Control Chart based on the type of data and the purpose of analysis. As mentioned earlier, different types of Control Charts are suitable for different types of data and analysis purposes. The choice of chart should be made carefully to ensure accurate analysis.
After selecting the chart, you calculate the control limits for the chart. Control limits are statistical boundaries that define the expected variation in the process. They are calculated based on the data collected and are used to determine if the process is in control or out of control.
Interpretation of Control Charts
Once the Control Chart is constructed, the next step is to interpret the chart. The interpretation involves looking for patterns or trends in the data that indicate the presence of special causes of variation. The Control Chart provides a visual representation of the process performance and helps in identifying patterns that would not be apparent in a table of numbers.
There are several rules or tests for interpreting Control Charts, known as Western Electric rules or Nelson rules. These rules provide guidelines for identifying out-of-control signals. For example, one rule states that if a single data point falls outside the control limits, it indicates the presence of a special cause of variation.
Western Electric Rules
The Western Electric rules are a set of four rules that were developed by the Western Electric Company to detect “out-of-control” or non-random conditions on Control Charts. These rules are based on the principle that a process is in control if all data points are within the control limits and if they are randomly distributed around the average.
The four rules are: one point is more than 3 standard deviations from the center line; two out of three consecutive points are more than 2 standard deviations from the center line on the same side; four out of five consecutive points are more than 1 standard deviation from the center line on the same side; and nine consecutive points are on the same side of the center line.
Nelson Rules
Nelson rules are a set of eight rules for detecting out-of-control or non-random conditions on Control Charts. These rules are more comprehensive than the Western Electric rules and are widely used in industry. The rules include all the Western Electric rules and add four more to provide a more sensitive detection of out-of-control conditions.
The additional rules include: six consecutive points steadily increasing or decreasing; fourteen consecutive points alternating in direction, up and down; two out of three consecutive points more than 2 standard deviations from the center line on opposite sides; and fifteen consecutive points within 1 standard deviation of the center line on either side.
Application of Control Charts in Business Analysis
Control Charts are widely used in business analysis for various purposes. They are used to monitor and control processes, identify process improvement opportunities, and make informed decisions based on data. They provide a visual representation of the process performance and help in identifying trends, patterns, and causes of variation in the process.
Business analysts use Control Charts to monitor key performance indicators (KPIs), track changes in business processes over time, and identify special causes of variation that need to be addressed. By identifying and eliminating special causes, business analysts can help improve the stability and predictability of business processes, leading to improved quality, efficiency, and customer satisfaction.
Monitoring and Controlling Processes
One of the main applications of Control Charts in business analysis is for monitoring and controlling processes. By plotting data on a Control Chart, business analysts can visually monitor the process performance over time. They can identify any changes in the process and take corrective action if necessary.
Control Charts provide a real-time view of the process performance, allowing for immediate detection of changes. This enables businesses to maintain control over their processes and ensure consistent quality and performance.
Identifying Process Improvement Opportunities
Control Charts are also used to identify opportunities for process improvement. By studying the variation in the process and identifying its sources, business analysts can find ways to reduce variation and improve the process. This can lead to improved quality, reduced costs, and increased customer satisfaction.
Control Charts provide a systematic approach to process improvement. They provide a clear picture of the process performance and highlight areas where improvement is needed. This makes them a valuable tool for continuous improvement initiatives in businesses.
Making Informed Decisions
Control Charts aid in making informed decisions based on data. They provide a visual representation of the data, making it easier to interpret and understand. This helps business analysts and decision-makers to make decisions that are based on facts, rather than assumptions or gut feelings.
By using Control Charts, businesses can make data-driven decisions that lead to improved process performance and business outcomes. They can identify trends and patterns in the data, predict future performance, and make proactive decisions to improve the process.
Conclusion
In conclusion, Control Charts are a powerful tool for data analysis in business. They provide a visual representation of the process performance, help in identifying sources of variation, and aid in making informed decisions. By using Control Charts, businesses can improve their processes, maintain control over their operations, and achieve their performance goals.
Whether you are a business analyst, quality control professional, or anyone involved in process improvement, understanding Control Charts and their application can be a valuable skill. It can help you monitor and improve processes, make data-driven decisions, and contribute to the success of your organization.
