Ishikawa Diagrams or Cause & Effect Diagrams

Ishikawa Diagrams Definition

Ishikawa diagrams (also called fishbone diagrams, cause-and-effect diagrams orFishikawa) are diagrams that show the causes of a certain event -- created by Kaoru Ishikawa (1990). Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation. The categories typically include:

• People: Anyone involved with the process
• Methods: How the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and laws
• Machines: Any equipment, computers, tools etc. required to accomplish the job
• Materials: Raw materials, parts, pens, paper, etc. used to produce the final product
• Measurements: Data generated from the process that are used to evaluate its quality
• Environment: The conditions, such as location, time, temperature, and culture in which the process operates

Fig:-Fishbone Diagram

Cause & Effect Diagrams Definition

•  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.

Fig:-Cause and Effect Diagram

Run Charts

Run Charts Definition

• A run chart is a line graph of data plotted over time. By collecting and charting data over time, you can find trends or patterns in the process. Because they do not use control limits, run charts cannot tell you if a process is stable. However, they can show you how the process is running. The run chart can be a valuable tool at the beginning of a project, as it reveals important information about a process before you have collected enough data to create reliable control limits.

Fig:-Run Charts

• Run charts (often known as line graphs outside the quality management field) display process performance over time. Upward and downward trends, cycles, and large aberrations may be spotted and investigated further. In a run chart, events, shown on the y axis, are graphed against a time period on the x axis. For example, a run chart in a hospital might plot the number of patient transfer delays against the time of day or day of the week. The results might show that there are more delays at noon than at 3 p.m. Investigating this phenomenon could unearth potential for improvement. Run charts can also be used to track improvements that have been put into place, checking to determine their success. Also, an average line can be added to a run chart to clarify movement of the data away from the average.

Alternatives with run charts:

1. An average line, representing the average of all the y values recorded, can easily be added to a run chart to clarify movement of the data away from the average. An average line runs parallel to the x axis.
2. Several variables may be tracked on a single chart, with each variable having its own line. The chart is then called a multiple run chart.
3. Run charts can also be used to track improvements that have been put into place, checking their success.

Scatter Diagrams

Scatter Diagrams Definition

• Scatter diagrams show the relationship between two sets of variables. By looking at the diagram you can see whether there is a link between variables. Where there is a link it is called correlation. 
• The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve. The better the correlation, the tighter the points will hug the line.

Fig:-Scatter Diagram

When to Use a Scatter Diagram

• When you have paired numerical data.
• When your dependent variable may have multiple values for each value of your independent variable.
• When trying to determine whether the two variables are related, such as…
• When trying to identify potential root causes of problems.
• After brainstorming causes and effects using a fishbone diagram, to determine objectively whether a particular cause and effect are related.
• When determining whether two effects that appear to be related both occur with the same cause.
• When testing for autocorrelation before constructing a control chart.

Pareto Chart

Definition Pareto Chart (Pareto Diagram)

A Pareto chart is a bar graph. The lengths of the bars represent frequency or cost (time or money), and are arranged with longest bars on the left and the shortest to the right. In this way the chart visually depicts which situations are more significant.

Ex:- Pareto Chart

When to Use a Pareto Chart

• When analyzing data about the frequency of problems or causes in a process.
• When there are many problems or causes and you want to focus on the most significant.
• When analyzing broad causes by looking at their specific components.
• When communicating with others about your data.

How To Make A Pareto Diagram

• STEP #1 - Determine the category classifications that you are going to use to group your defect data by. Use your check sheets to collect the data for the Pareto.

• STEP #2 - Decide on the time period to be used to record your information. One week, a month, etc. It is best to be consistent so that you have a standard to compare to if the data collection exercise is to be repeated again. You can't measure results achieved accurately without consistent measurement periods.

• STEP #3 - From the Check Sheet, total the occurrence of each item for the period measured. Each total will be represented by the length of a vertical bar, much like the Pareto chart example above.

• STEP #4 - (It is easier to keep your scale accuracy correct if you use graph paper). Draw horizontal and vertical axes on graph paper; or if no graph paper available, use a ruler to measure and draw evenly scaled vertical and horizontal lines that meet evenly (see figure 2 below).

 

Figure 2

• STEP #5 - Make your scale units at even multiples, such as 10, 20, etc. so as to have an even scale system (see figure 3 below).

 
Figure 3

• STEP #6 - Draw in the bars that correspond to the total numbers collected from your Check Sheet, starting on the far left, with the most frequent (highest number recorded) defective item. It is recommended that you leave a gap between each item bar for reading clarity. (Note: If you have several defective items with very small quantities, you can group them together in a category called "other", as long as their total is less than the previous bar heighth). Notice the figure 4 below.

 
Figure 4

• STEP #7 - Under the horizontal axis (line), label each of the bars so that you know which defect is represented by which bar.

• STEP #8 - Draw another vertical line and label the percentage scale in the same manner that you did on the left side (see figure 5 below)

 
Figure 5

• STEP #9 - Plot a dot for each item on the graph, starting from the left side, on or above the bar corresponding to the related percentage of defectives for each item. Once each dot is plotted, use a ruler and connect the line graph from dot-to-dot, as shown in the "Pareto example" up above.

• STEP #10 - Title the graph and briefly write the source of the data below the graph, that describes the data and method used to gather. Include all pertinent facts which will define the method of observation (for example, time period, production line, and whether this was before or after any modifications to the line). Recording this data on the bottom of your chart, will help further analysis as well as to provide a record of what was done on this date, for consideration in future studies.

Control Charts for Variable and Attributes

Variable Control Charts

Consider that you are evaluating the output from a process.  Conceptually, you could evaluate the products in two basic ways.  In the first way you would simply classify the products as "conforming" or "non conforming."  This produces attribute (discrete) data.  In the second way you could measure a key characteristic using a continuous scale.  This produces variable (continuous) data.

Variables control charts are used to evaluate variation in a process where the measurement is a variable--i.e. the variable can be measured on a continuous scale (e.g. height, weight, length, concentration). There are two main types of variables control charts.  One (e.g. x-bar chart, Delta chart) evaluates variation between samples. Non-random patterns (signals) in the data on these charts would indicate a possible change in central tendency from one sampling period to the next.  One way of thinking about the use of a variables control chart is that you are testing the hypothesis that a particular sample mean came from the population of sample means represented by the control limits of the process.  If the particular sample mean is within the control limits, your concusion is that it does come from that population.  If the particular sample mean is outside the control limits, you conclusion is that it may have come from some other distribution (i.e. a distribution with a mean that is higher or lower than this population mean.  [NOTE:  There are other signals that may indicate an out-of-control signal that will be discussed in the Lesson Six Presentation.]

The other type of variables control chart (e.g. R-chart, S-chart, Moving Range chart) evaluates variation within samples.  Non-random patterns (signals) in the data on these charts would indicate a possible change in the variation within the samples.

Non-random patterns in the data plotted on the control charts provide evidence of the process being in-control (only common cause variation present; predictable) or out-of-control (common cause andassignable cause variation present; unpredictable).  Adjusting a process which is in-control will result in increased variation.  Failing to adjust a process which is out-of-control results in a loss of predictability.  Control charts help a machine operator or manager to decide when it is appropriate to make an adjustment and when it is better to leave the process alone.

                                                 Attribute Control Charts

These charts are applied to data that follow a discrete distribution.

Types of attributes control chart:

p chart

This chart shows the fraction of nonconforming or defective product produced by a

manufacturing process.

It is also called the control chart for fraction nonconforming.

np chart

This chart shows the number of nonconforming. Almost the same as the p chart.

c chart

This shows the number of defects or nonconformities produced by a manufacturing process.

u charts

This chart shows the nonconformities per unit produced by a manufacturing process.

Process Control Chart

Process Control Chart Definition

• The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).

• Control charts for variable data are used in pairs. The top chart monitors the average, or the centering of the distribution of data from the process. The bottom chart monitors the range, or the width of the distribution. If your data were shots in target practice, the average is where the shots are clustering, and the range is how tightly they are clustered. Control charts for attribute data are used singly

When to Use a Control Chart

• When controlling ongoing processes by finding and correcting problems as they occur.
• When predicting the expected range of outcomes from a process.
• When determining whether a process is stable (in statistical control).
• When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process).
• When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

Elements of a Control Chart

There are three main elements of a control chart are

• A control chart begins with a time series graph.
• A central line (X) is added as a visual reference for detecting shifts or trends – this is also referred to as the process location.
• Upper and lower control limits (UCL and LCL) are computed from available data and placed equidistant from the central line. This is also referred to as process dispersion.