Control charts in software

To see how these tools can benefit you, we recommend you download and install the free trial of NCSS. The quality control procedures in NCSS are visual and numeric tools used to monitor whether a process is in control by examining whether measurement summaries stay within the specified precision limits. This page provides a general overview of the tools that are available in NCSS for statistical quality control. There you will find formulas, references, discussions, and examples or tutorials describing the procedure in detail.

These procedures generate various control charts useful for monitoring the average and variability of a process. Capability analysis and various reports are also available. These charts are useful for displaying count data such as the number of defectives or the number of defects on a certain item.

Here is a brief description of each of these four charts:. This procedure generates Levey-Jennings control charts on single variables. It finds out-of-control points using the Westgard rules. The Levey-Jennings control chart is a special case of the common Shewart X-bar chart in which there is only a single stream of data and sigma is estimated using the standard deviation of those data.

An Italian economist, Vilfredo Pareto , noticed a great inequality in the distribution of wealth. A few people owned most of the wealth. In quality control, Pareto analysis refers to the tendency for the bulk of the quality problems to be due to a few of the possible sources. Hence, by isolating and correcting the major problem areas, you obtain the greatest increase in quality.

It is rather difficult to have innovation while trying to reduce variation at the same time. Of course, Control Charting a process doesn't necessarily mean one reduces variation as identifying assignable causes is very useful. Anyway, my question is: "What should one chart in this industry? I've seen people applying this visualization to Lead Time variation. But even for this measurement I cannot find that much value in visualizing its variation. Right now I'm working on a big project with tens of teams working on different parts of the product, with QA teams separated from development teams.

I started to feel that the exchange was missing the whole point. That is, are control charts even appropriate and applicable to new software development? In the employee example above, we might determine that the employee was not properly trained, and so the action would be to train that employee and ensure that future employees are properly trained.

Type 1: Mistaking a common cause for a special cause: Asking for the reason for variation when the variation is inherent in the process. Investigating in isolation why that particular incident occurred would be treating it as a special cause. Type 2: Mistaking a special cause for a common cause: Not taking note of a significant deterioration in performance simply because it still remains within specifications.

For example, ignoring an upward trend in service level agreement SLA timings e. The concept of common and special causes, and the two types of mistakes can be explained through a simple example given by Deming himself in his book, The New Economics MIT Press, second edition and elaborated by Brian L. This example is illustrated below. The chart below was prepared by year-old Patrick Nolan.

It shows the time at which the school bus picked him up on successive school days covering a period of a few weeks , in time order. On most days the pick-up time falls within a narrow band, but there are two days on which specific events delayed the pick-up time considerably: One day there was a new driver, on another a faulty door closer. Figure 1: Time of arrival of school bus, by Patrick Nolan, 11 credit: W. Edwards Deming, The New Economics. Some of the factors that might affect the pick-up time on any day are the weather, the amount of traffic, how long the bus driver waited for the children at previous stops, and what time the driver got started.

The variation within the narrow band is due to a combination of these factors—i. The new driver and the faulty door closer are special causes of variation. These are not always present in the process, appear sporadically, and come from outside the usual process.

We need to investigate the reasons for such variation, and if we do not do so, we would be making a Type 2 mistake. A control chart or process behavior chart is a statistical tool used to distinguish between process variation resulting from common causes, and variation resulting from special causes. It is a time-series graph with three horizontal lines—a central line representing the average , and upper and lower control limits or the upper and lower natural process limits as these are often, more appropriately, referred to.

Individual variations in outputs can have any number of contributory causes. The process behavior chart guides us as to when it is economically worthwhile to look for a reason why a particular result occurred i. Stable processes If all outputs from the process are within the process limits and do not show any patterns or trends, then the process is said to be in statistical control or stable. In such cases, all the outcomes or results are typical of those produced by the whole system of common causes.

Stable processes display controlled variation—i. This variation is due to the way the process has been designed, built, and set up as well as the way people have been trained to work on it. Controlled variation is caused by a multiple number of random causes acting simultaneously where no single cause is predominant. This means that we cannot assign a particular cause for any variation observed when the variation is controlled within the process limits.

Understanding the above-mentioned implications of stable processes and making use of the guidance provided by process behavior charts can result in huge savings for businesses. Many organizations put a lot of fruitless effort into explaining monthly, weekly, or daily differences in sales, production, profits, and performance of all kinds, when in fact the variation is due to common causes.

This futile practice also increases stress for employees, especially when individual below-average results are often seen as justification for aggressive management action, and above-average results are seen as evidence of the effects of such action. The simple but important message is that as long as the data lie between the process limits, do not get distracted by short-term data or even worse individual data points.

Unstable processes In contrast to stable processes, unstable processes show changes in behavior and are unpredictable. This is uncontrolled variation—i. Uncontrolled variation occur due to causes alien to the process, or outside causes. The variation observed can be attributed to a single cause that is dominant, known as a special cause. Unlike common causes, it is profitable to discover and remove special causes. Obviously, we would only want to remove a special cause if the change in behavior is bad.

If the change is good, we would still want to identify the cause, but the purpose for doing so would be to see if it is possible to absorb it into the system. Here the process behavior chart helps us in two ways: It warns us of the appearance of special causes and also indicates approximately when they arrive, by giving us a signal, which is a vital clue to identify them. The difference between stable and unstable processes is, therefore, of great practical importance. If we look for the cause of some particular detail in the data, are we likely to find something useful?

Stability Analysis Rules. Control Chart Rules. How to Change Rules. Control Chart Templates. Automated Chart Tasks. Multi-Chart Dashboard. Levey Jennings Dashboard.

A line chart of data measuring the process over time. A Center Line calculated as the average or median of the data. Control Chart Types There are seven Shewhart control charts and many more for special situations.

Control Limit Calculations Each type of control chart has a different formula for calculating control limits. Stability Analysis Rules Stability rules identify and highlight points or trends that need to be investigated. Fourth Reason: QI Macros control chart software is affordable and easy to use.