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The 7 quality management tools

January 09, 2007 17:09 IST

The Japanese began applying the thinking developed by Walter Shewhart and W Edward Deming during the 1930s and 1940s. Japan's progress in continuous improvement led to the expansion of the use of these tools.

Kaoru Ishikawa, the then head of the Japanese Union of Scientists and Engineers (JUSE), thus, decided to expand the use of these approaches in Japanese manufacturing in the 1960s with the introduction of the seven quality control (7QC) tools.

7QC tools are fundamental instruments to improve the quality of products. They are used to analyse the production process, identify major problems, control fluctuations of product quality and provide solutions to avoid future defects.

These tools use statistical techniques and knowledge to accumulate data and analyse them. They help organise the collected data in a way that is easy to understand. Moreover, by using 7QC tools, specific problems in a process can be identified.

The first is the check sheet, which shows the history and pattern of variations. This tool is used at the beginning of the change process to identify the problems and collect data easily.

The team using it can study observed data (a performance measure of a process) for patterns over a specified period of time. It is also used at the end of the change process to see whether the change has resulted in permanent improvement.

The Pareto chart is named after Wilfredo Pareto, the Italian economist who determined that wealth is not evenly distributed. The chart shows the distribution of items and arranges them from the most frequent to the least frequent, with the final bar being miscellaneous.

The Pareto chart is used to define problems, to set their priority, to illustrate the problems detected and determine their frequency in the process. It is a graphic picture of the most frequent causes of a particular problem. Most people use it to determine where to put their initial efforts to get maximum gain.

The cause and effect diagram is also called the "fishbone chart" because of its appearance and the Ishikawa chart after the man who popularised its use in Japan. It is used to list the cause of particular problems. Lines come off the core horizontal line to display the main causes; the lines coming off the main causes are the subcauses.

This tool is used to figure out any possible causes of a problem. It allows a team to identify, explore, and graphically display, in increasing detail, all of the possible causes related to a problem or condition to discover its root cause(s).

The histogram is a bar chart showing a distribution of variables. This tool helps identify the cause of problems in a process by the shape as well as the width of the distribution. It shows a bar chart of accumulated data and provides the easiest way to evaluate the distribution of data.

Then there's the scatter diagram, which shows the pattern of relationship between two variables that are thought to be related.

The closer the points are to the diagonal line, the more closely there is a one-to-one relationship. The scatter diagram is a graphical tool that plots many data points and shows a pattern of correlation between two variables.

Graphs are among the simplest and best techniques to analyse and display data for easy communication in a visual format. Data can be depicted graphically using bar graphs, line charts, pie charts and control charts. While the first three are commonly used, the last is a line chart with control limits.

By mathematically constructing control limits at three standard deviations above and below the average, one can determine what variation is due to normal ongoing causes (common causes) and what variation is produced by unique events (special causes).

By eliminating the special causes first and then reducing common causes, quality can be improved. Control chart provides control limits that are three standard deviations above and below average, whether or not our process is in control.

This tool enables the user to monitor, control and improve process performance over time by studying variation and its source.

The author is chairman, CII Mission for Manufacturing Innovation, and chairman and managing director, Sona Koyo Steering Systems.

Surinder Kapur
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