Research on software quality control method based on control chart

A nurse on the committee suggests that a possible contributor to this increase is the use of flash sterilisation FS in the operating theatres. Traditionally, FS was used only in emergency situations—for example, when an instrument was dropped during surgery—but recently it seems to have become a more routine procedure.

Some committee members express the opinion that a new group of orthopaedic surgeons who recently joined the hospital staff might be a contributing factor—that is, special cause variation. This suggestion creates some defensiveness and unease within the committee. Rather than debating opinions, the committee decides to take a closer look at this hypothesis by analysing some data on the FS rate number of FS per surgeries to see how it has varied over time.

During the baseline period the mean FS rate was around 33 per surgeries the centre line on the baseline control chart and the process appeared to be in control. However, arrival of the new surgeons indicated an increase special cause variation to a mean FS rate of about 50 per surgeries. For example, the third data point week 13 is beyond the baseline UCL, as are weeks 17, 18, 19, and Additionally, several clusters of two out of three points are more than 2SD beyond the mean, several clusters of four out five points are beyond 1SD, and all of the new points are above the baseline period mean.

All these signals are statistical evidence of a significant and sustained shift in process performance. The IC committee can now look further into this matter with confidence that it is not merely an unsupported opinion. It must be noted that this analysis does not lead to the conclusion that the new surgeons are to blame for the increase. Rather, the data simply indicate that it is highly likely that something about the process of handling surgical instruments has fundamentally changed, coincident with the arrival of the new surgeons.

Further investigation is warranted. The laboratory manager decides to investigate this assertion with data rather than just opinions. The data are stratified by shift and type of request urgent versus routine to ensure that the analysis is conducted by reasonably homogeneous processes. Since TAT data often follow normal distributions, X-bar and S types of control charts are appropriate here fig 2. Each day the mean and SD TAT were calculated for three randomly selected orders for complete blood counts.

The top chart X-bar shows the mean TAT for the three orders each day, while the bottom chart S shows the SD for the same three orders; during the day shift the mean time to get results for a routine complete blood count is about 45 minutes with a mean SD of about 21 minutes.

Instead, it appears that the process is performing consistently and in a state of statistical control. An in control process can therefore be predictably bad. In this case the process is stable and predictable but not acceptable to the clinicians. Since the process exhibits only common cause variation, it is appropriate to consider improvement strategies to lower the mean TAT and reduce the variation lower the centre line and bring the control limits closer together.

This would produce a new and more acceptable level of performance. The next steps for the team are therefore to test an improvement idea, compare the new process with these baseline measurements, and decide whether the process has improved, stayed the same, or worsened. An interdisciplinary team has been meeting to try to reduce the postoperative surgical site infection SSI rate for certain surgical procedures.

A g type of control chart based on the geometric distribution for one type of surgery is shown in fig 3. Instead of aggregating SSIs in order to calculate an infection rate over a week or month, the g chart is based on a plot of the number of surgeries between occurrences of infection. This chart allows the statistical significance of each occurrence of an infection to be evaluated 11 rather than having to wait to the end of a week or a month before the data can be analysed. This ability to evaluate data immediately greatly enhances the potential timeliness of the analysis.

The g chart is also particularly useful for verifying improvements such as reduced SSIs and for processes with low rates. An initial intervention suggested by the team is to test a change in the postoperative wound cleaning protocol. As shown in fig 3, however, this change does not appear to have had any impact on reducing the infection rate. Although this intervention did not result in an improvement, the control chart was useful to help prevent the team from investing further time and resources in training staff and implementing an ineffective change throughout the hospital.

After more brainstorming and review of the literature, the team decided to try experimenting with the shave preparation technique for preparing the surgical site before surgery.

Working initially with a few willing surgeons and nurses, they developed a new shave preparation protocol and used it for several months. The control chart in fig 3 indicates that this change resulted in an improvement with the SSI rate reducing from approximately 2.

Note that on this type of chart data plotted above the UCL indicate an improvement, as an increase in the number of surgeries between SSIs equates to a decrease in the SSI rate. A GP practice is working hard on improving appointment access and has decided to track several performance measures each month. After exploring ideas that had been successful for other practices, the staff implemented several changes at the same time: reducing the number of appointment types, simplifying the telephone scripts, and offering appointments with the practice nurse in lieu of the doctor for certain minor conditions.

As shown in the control chart, there was a notable improvement in appointment access satisfaction soon after these changes were implemented. Since the changes were not tried one at a time, however, we do not know the extent to which each change contributed to the improvement; further testing could be conducted to determine this, similar in approach to traditional screening experiments.

This chart can also be used to monitor the sustainability of improvements by detecting any future special cause variation of a decrease in appointment access satisfaction.

If several staff were asked to identify the criteria for determining what constitutes infectious waste in a hospital, a wide variety of responses would probably be obtained. Faced with this lack of standardization, most hospitals spend more time and money disposing of infectious waste than is necessary.

It has also been estimated that an average size hospital spends the equivalent of a new CAT scanner every year disposing of improperly classified infectious waste such as soft drink cans, paper, milk cartons, and disposable gowns.

Armed with this knowledge, a team decides to address this issue. Since the team had no idea how much infectious waste they produced each day, they first established a baseline. As shown on the left side of fig 5 an XmR chart based on the normal distribution , the mean daily amount of infectious waste during the baseline period was a little over 7 lb 3. The process was stable and exhibited only common cause variation, so an intervention improvement strategy is appropriate.

If the process is not changed, the amount of infectious waste in future weeks might be expected to vary between 6 lb 2. To reduce the mean amount of infectious waste produced daily, the team first established a clear operational definition of infectious waste and then conducted an educational campaign to make everyone more aware of what was and was not infectious waste. They next developed posters, designed tent cards for the cafeteria tables, made announcements at departmental meetings, and assembled displays of inappropriate items found in the infectious waste containers.

The results of this educational effort are shown on the right side of fig 5. The process has shifted to a new and more acceptable level of performance. Since the process has clearly changed, new control limits have been calculated for the data after the improvement. The new mean daily production of infectious waste is a little more than 4 lb 1.

The control chart provided the team with a useful tool for testing the impact of these efforts. In this case, the shift in the process was very noticeable and in the correct direction. It is interesting, however, that, although the mean amount of waste was reduced, these same improvements inadvertently also caused the day-to-day variation to increase note the wider control.

Not all changes lead to the desired results. A challenge for the team now is to reduce the variation back to at least its original level. These examples illustrate several general points about control charts. Control charts can help policy makers avoid wasted investments in changes that sound good but do not actually deliver, as was the case in the surgical site infection example. That case further illustrated how control charts might be able to detect statistically significant signals from the patterns in the data more quickly than with traditional statistical methods.

The appointment access satisfaction example illustrated the general application of control charts for conducting rapid screening experiments as an efficient prelude to a more traditional experiment. More generally, these examples illustrate how control charts help teams to decide on the correct improvement strategy—whether to search for special causes if the process is out of control or to work on more fundamental process improvements and redesign if the process is in control.

In each example the control charts can also be used as a simple monitoring aid to assure that improvements are sustained over time. One of the benefits of using control charts is that they do not require as much data as traditional statistical analysis which relies on large aggregated data sets. For example, the g chart in fig 3 uses each incident of infection as a data point for decision making, the X bar and S charts in fig 2 are based on samples of three randomly selected laboratories per day, and the u chart in fig 1 uses rates based on the mean of 80 surgeries per week performed in the hospital.

Generally speaking, 20—30 such data points are needed to calculate the UCL and LCL, but after that each new data point can be judged for its statistical significance. Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

Use of this web site signifies your agreement to the terms and conditions. Research on software quality control method based on control chart Abstract: Improving the software quality unceasingly, is an important work of throughout the software life cycle, is a reliable guarantee of software projects which is successful implemented and completed. Some companies establish internal quality control divisions when defining what is quality control.

They do this to monitor products and services, while others rely on external bodies to track products and performance. These controls may be largely dependent on the industry of the business.

In the long run, investments in quality control measures can protect the reputation of a company, prevent products from being unreliable, and increase trust on the side of consumers. These processes are determined through rigorous methodology and testing, as well as industry standards and best practices. Moreover, quality control is necessary because it ensures that a company will look at evidence-based data and research — not just anecdotal observations — to ensure that products are living up to their standard.

No consumer wants to risk using a product that could endanger them or fail expectations. When answering what is quality control, it is critical to understand that it consists of multifaceted responsibilities and roles.

Whereas quality assurance looks at the processes used to prevent defects, quality control is focused specifically on the measurement and analysis processes involved with determining product quality. Quality control uses specific research tools to accomplish fact-finding processes and conduct analyses. A quality control professional is tasked with analyzing these measurements against some sort of standard determined by the quality management department, company policies, and industries or regulatory bodies.

Based on this evidence-gathering, quality control will recommend changes. We can see from this roadmap, too, how quality assurance and quality control differ.