IX Международная студенческая научная конференция Студенческий научный форум - 2017

In 1950, the Japanese Union of Scientists and Engineers (JUSE) invited legendary quality guru W. Edwards Deming to go to Japan and train hundreds of Japanese engineers, managers and scholars in statistical process control. Deming also delivered a series of lectures to Japanese business managers on the subject, and during his lectures, he would emphasize the importance of what he called the “basic tools” that were available to use in quality control.

One of the members of the JUSE was Kaoru Ishikawa, at the time an associate professor at the University of Tokyo. Ishikawa had a desire to ‘democratize quality’: that is to say, he wanted to make quality control comprehensible to all workers, and inspired by Deming’s lectures, he formalized the Seven Basic Tools of Quality Control.

Ishikawa believed that 90% of a company’s problems could be improved using these seven tools, and that –- with the exception of Control Charts — they could easily be taught to any member of the organization. This ease-of-use combined with their graphical nature makes statistical analysis easier for all.

The seven tools are:

Cause and Effect Diagrams

Check sheet

Control (Run) Charts

Histograms

Pareto Charts

Scatter Plots

Flow Charts

The Cause & Effect (CE) diagram, also sometimes called the ‘fishbone’ diagram, is a tool for discovering all the possible causes for a particular effect. The effect being examined is normally some troublesome aspect of product or service quality, such as ‘a machined part not to specification’, ‘delivery times varying too widely’, ‘excessive number of bugs in software under development’, and so on, but the effect may also relate to internal processes such as ‘high rate of team failures’.

The major purpose of the CE Diagram is to act as a first step in problem solving by generating a comprehensive list of possible causes. It can lead to immediate identification of major causes and point to the potential remedial actions or, failing this, it may indicate the best potential areas for further exploration and analysis. At a minimum, preparing a CE Diagram will lead to greater understanding of the problem.

The CE Diagram was invented by Professor Kaoru Ishikawa of Tokyo University, a highly regarded Japanese expert in quality management. He first used it in 1943 to help explain to a group of engineers at Kawasaki Steel Works how a complex set of factors could be related to help understand a problem. CE Diagrams have since become a standard tool of analysis in Japan and in the West in conjunction with other analytical and problem-solving tools and techniques.

CE Diagrams are also often called Ishikawa Diagrams, after their inventor, or Fishbone Diagrams because the diagram itself can look like the skeleton of a fish.

Use it when you start investigating a problem. Construct a CE Diagram whenever you need to investigate the causes or contributing factors for an effect (be it a quality characteristic or other outcome) which is of concern to you. This will most likely be after you have conducted a general investigation of problems for a particular function, product, or service, and ranked them using a Pareto Chart. The effect ranked highest provides the starting point for a CE Diagram.

For example, you may just have completed an investigation of all the reasons recorded for goods being returned by customers and found that the highest incidence relates to incorrect goods being sent. A CE Diagram can be constructed to explore the possible causes for this.

Developing a CE Diagram in a team meeting is a very effective technique for,

• concentrating team members’ attention on a specific problem

• pooling, and reflecting back, team thinking

• constructing a picture of the problem at hand without resorting to the tight discipline of a flowchart

How to draw CE diagram. This is a three step process.

Step 1. Write down the effect to be investigated and draw the ‘backbone’ arrow to it. In the example shown below the effect is ‘Incorrect deliveries’.

Step 2. Identify all the broad areas of enquiry in which the causes of the effect being investigated may lie. For incorrect deliveries the diagram may then become:

For manufacturing processes, the broad areas of enquiry which are most often used are Materials (raw materials), Equipment (machines and tools), Workers (methods of work), and Inspection (measuring method).

Step 3. This step requires the greatest amount of work and imagination because it requires you (or you and your team) to write in all the detailed possible causes in each of the broad areas of enquiry. Each cause identified should be fully explored for further more specific causes which, in turn, contribute to them.

You continue this process of branching off into more and more directions until every possible cause has been identified. The final result will represent a sort of a ‘mind dump’ of all the factors relating to the effect being explored and the relationships between them.

A check sheet is a simple tool that was once a part of the seven basic tools of six sigma. It is said that check sheet has become obsolete because of the introduction of software which have the capability to record high volumes of data and present them in a format as required.

The check sheet was designed to be immensely simple. This is for two reasons. Firstly it was meant to be a tool for data recording which itself is quite simple. Secondly the check sheet was meant to be used by the people on the shop floor. It would not be very intelligent to expect them to be able to deal with complexity. Hence there was an inherent need for designing the check sheet the way it is.

A check sheet is meant to record simple facts and statistics that happen on the shop floor for over a period of time. The sheet is designed in such a way that it has the possible sources of error already written down. Users can add more possibilities. Then they record the data pertaining to the errors on a daily basis. This data can then be used as evidence in brainstorming sessions. A check sheet provides the raw materials that help users discover the problems that they need to know about before they solve them.

There were various types of check sheets available in the past. This was because the important point for each metric was different. For some defects it was important to find out the times at which they occurred. For certain other defects it was important to find out the location where they occurred so on and so forth.

These tally marks were then used to unearth a pattern which would then suggest possible sources of disturbance as well as help in solving the problem preventing further occurrence.

Check sheets have now become obsolete. They have been replaced by modern day Business Process Management software. This has enables more complex data to be automatically recorded. The process now depends neither of the intelligence of the human nor on the reliability of the check sheet.

Data is now automatically recorder and can be arranged in whatever manner required in a few clicks. Many software even produce the data in a ready to use graphical format enabling further convenience for the users.

Control charts are used to routinely monitor quality. Depending on the number of process characteristics to be monitored, there are two basic types of control charts. The first, referred to as a univariate control chart, is a graphical display (chart) of one quality characteristic. The second, referred to as a multivariate control chart, is a graphical display of a statistic that summarizes or represents more than one quality characteristic.

If a single quality characteristic has been measured or computed from a sample, the control chart shows the value of the quality characteristic versus the sample number or versus time. In general, the chart contains a center line that represents the mean value for the in-control process. Two other horizontal lines, called the upper control limit (UCL) and the lower control limit (LCL) are also shown on the chart. These control limits are chosen so that almost all of the data points will fall within these limits as long as the process remains in-control.

The control limits as pictured in the graph might be 0.001 probability limits. If so, and if chance causes alone were present, the probability of a point falling above the upper limit would be one out of a thousand, and similarly, a point falling below the lower limit would be one out of a thousand. We would be searching for an assignable cause if a point would fall outside these limits. Where we put these limits will determine the risk of undertaking such a search when in reality there is no assignable cause for variation.

Since two out of a thousand is a very small risk, the 0.001 limits may be said to give practical assurances that, if a point falls outside these limits, the variation was caused be an assignable cause. It must be noted that two out of one thousand is a purely arbitrary number. There is no reason why it could not have been set to one out a hundred or even larger. The decision would depend on the amount of risk the management of the quality control program is willing to take. In general (in the world of quality control) it is customary to use limits that approximate the 0.002 standard.

Histogram is a bar chart to show frequencies of causes of problems to understand preventive or corrective action.

A histogram is a way to represent tabulated frequencies shown as adjacent rectangles.

Check sheet is used as an input to develop histogram, Please refer to the Blog Check Sheet as a Component of Seven Basic Quality Tool

An IT test team member is evaluating work products to detect problems from the specifications. Team may choose to categorise data about quality problems in following categories:

Categories suggested by Roger S. Pressman:

• Incomplete or erroneous specification (IES)

• Misinterpretation of customer communication (MCC)

• Intentional deviation from specifications (IDS)

• Violations of programming standards. (VPS)

• Error in data representations (EDR)

• Inconsistent component interface (ICI)

• Error in design logic (EDL)

• Incomplete or erroneous testing (IET)

• Inaccurate or inconsistent documentation (IID)

• Error in programming language translation of design (PLT)

• Ambiguous or inconsistent human/computer interface (HCI)

• Miscellaneous (MIS)

While examination the work product, test team member assesses the defects and enter frequencies in their respective category of causes like:

Check Sheet suggested by roger s. pressman in software engineering a practitioner’s approach

As a further explanation, first two columns are used to develop Pareto chart, as shown below:

Histogram is showing using bar chart that most of the frequencies of causes are coming from IES. MCC and EDR and we need to take corrective action to solve the source of problems.

When Histogram is used in “Plan Quality”, serves as a preventive approach to improve processes where historical data is used to identify categories of causes effecting most. Processes are selected to improve, for example due to higher frequencies in IES, MCC and EDR we may select improvements in “Collect Requirement”, “Define Scope” processes.

Histogram is used in “Control Quality” to identify causes of poor performance in process and work products.

Histogram is a kind of bar chart showing a distribution of variables or causes of problems. A histogram represents cause of a problem as a column and the frequency of each cause of problem as the height of the column.

In short, A histogram is a bar chart that show the frequency of a cause of a problem occurring using the height of the bar as an indicator.

A Pareto chart, also called a Pareto distribution diagram, is a vertical bar graph in which values are plotted in decreasing order of relative frequency from left to right. Pareto charts are extremely useful for analyzing what problems need attention first because the taller bars on the chart, which represent frequency, clearly illustrate which variables have the greatest cumulative effect on a given system.

The Pareto chart provides a graphic depiction of the Pareto principle, a theory maintaining that 80% of the output in a given situation or system is produced by 20% of the input.

The Pareto chart is one of the seven basic tools of quality control. The independent variables on the chart are shown on the horizontal axis and the dependent variables are portrayed as the heights of bars. A point-to-point graph, which shows the cumulative relative frequency, may be superimposed on the bar graph. Because the values of the statistical variables are placed in order of relative frequency, the graph clearly reveals which factors have the greatest impact and where attention is likely to yield the greatest benefit.

A Simple Example

A Pareto chart can be used to quickly identify what business issues need attention. By using hard data instead of intuition, there can be no question about what problems are influencing the outcome most. In the example below, XYZ Clothing Store was seeing a steady decline in business. Before the manager did a customer survey, he assumed the decline was due to customer dissatisfaction with the clothing line he was selling and he blamed his supply chain for his problems. After charting the frequency of the answers in his customer survey, however, it was very clear that the real reasons for the decline of his business had nothing to do with his supply chain. By collecting data and displaying it in a Pareto chart, the manager could see which variables were having the most influence. In this example, parking difficulties, rude sales people and poor lighting were hurting his business most. Following the Pareto Principle, those are the areas where he should focus his attention to build his business back up.

A scatter plot (also called a scatter graph, scatter chart, scattergram, or scatter diagram) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are color-coded you can increase the number of displayed variables to three. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.

A scatter plot can be used either when one continuous variable that is under the control of the experimenter and the other depends on it or when both continuous variables are independent. If a parameter exists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horizontal axis. The measured or dependent variable is customarily plotted along the vertical axis. If no dependent variable exists, either type of variable can be plotted on either axis and a scatter plot will illustrate only the degree of correlation (not causation) between two variables.

A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, weight would be on y axis and height would be on the x axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it indicates a positive correlation between the variables being studied. If the pattern of dots slopes from upper left to lower right, it indicates a negative correlation. A line of best fit (alternatively called 'trendline') can be drawn in order to study the relationship between the variables. An equation for the correlation between the variables can be determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear regression and is guaranteed to generate a correct solution in a finite time. No universal best-fit procedure is guaranteed to generate a correct solution for arbitrary relationships. A scatter plot is also very useful when we wish to see how two comparable data sets agree with each other. In this case, an identity line, i.e., a y=x line, or an 1:1 line, is often drawn as a reference. The more the two data sets agree, the more the scatters tend to concentrate in the vicinity of the identity line; if the two data sets are numerically identical, the scatters fall on the identity line exactly.

One of the most powerful aspects of a scatter plot, however, is its ability to show nonlinear relationships between variables. The ability to do this can be enhanced by adding a smooth line such as LOESS. Furthermore, if the data are represented by a mixture model of simple relationships, these relationships will be visually evident as superimposed patterns.

The scatter diagram is one of the seven basic tools of quality control.

Scatter charts can be built in the form of bubble, marker, or/and line charts.

A flowchart is a type of diagram that uses an algorithm, workflow or process, showing the steps as boxes of various kinds, and their order by connecting them with arrows. This diagrammatic representation illustrates a solution model to a given problem. Flowcharts are used in analysing, designing, documenting or managing a process or program in various fields.

Flowcharts are used in designing and documenting simple processes or programs. Like other types of diagrams, they help visualize what is going on and thereby help understand a process, and perhaps also find flaws, bottlenecks, and other less-obvious features within it. There are many different types of flowcharts, and each type has its own repertoire of boxes and notational conventions. The two most common types of boxes in a flowchart are:

a processing step, usually called activity, and denoted as a rectangular box

a decision, usually denoted as a diamond.

A flowchart is described as "cross-functional" when the page is divided into different swimlanes describing the control of different organizational units. A symbol appearing in a particular "lane" is within the control of that organizational unit. This technique allows the author to locate the responsibility for performing an action or making a decision correctly, showing the responsibility of each organizational unit for different parts of a single process.

Flowcharts depict certain aspects of processes and they are usually complemented by other types of diagram. For instance, Kaoru Ishikawa defined the flowchart as one of the seven basic tools of quality control, next to the histogram, Pareto chart, check sheet, control chart, cause-and-effect diagram, and the scatter diagram. Similarly, in UML, a standard concept-modeling notation used in software development, the activity diagram, which is a type of flowchart, is just one of many different diagram types.

Nassi-Shneiderman diagrams and Drakon-charts are an alternative notation for process flow.

Common alternative names include: flowchart, process flowchart, functional flowchart, process map, process chart, functional process chart, business process model, process model, process flow diagram, work flow diagram, business flow diagram. The terms "flowchart" and "flow chart" are used interchangeably.

The underlying graph structure of a flow chart is a flow graph, which abstracts away node types, their contents and other ancillary information.

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