tqm old tools

SEVEN QUALITY CONTROL TOOLS

(or)

OLD SEVEN TOOLS

TQM Tools and Techniques Tools and techniques are practical methods, skills,

means or mechanisms that can be applied to particular tasks.

A single tool may be describes as a device which has a clear role. It is often narrow in focus and is usually used on its own.

A techniques on the other hand, has a wider application than a tool. Viewed simplistically, techniques can be thought as a collection of tools.

The Seven Basic Quality Control Tools

The Seven Management Tools Other Tools Techniques

Cause and Effect Diagram Affinity Diagram Brainstorming Benchmarking

Check Sheet Arrow Diagram Control Plan Departmental Purpose Analysis

Control Chart Matrix Diagram Flow chart Design of Experiment

Graphs Matrix Data Analysis Method

Force Field Analysis

Failure Mode and Effect Analysis

Histogram Process Decision Program Chart Questionnaire Fault Tree Analysis

Pareto Diagram Relations Diagram Sampling Poka Yoke

Scatter Diagram Systematic Diagram Problem Solving Methodology

Quality Costing

Quality Function Deployment

Quality Improvement Teams

Statistical Process Control

Dr.Kaoru Ishikawa, Professor at Tokyo University & Father of Q C in Japan.

CAUSE ANALYSIS TOOLS are Cause and Effect diagram, Pareto analysis & Scatter diagram.

EVALUATION AND DECISION MAKING TOOLS are decision matrix and multivoting

DATA COLLECTION AND ANALYSIS TOOLS are check sheet, control charts, DOE, scatter diagram, stratification, histogram, survey.

IDEA CREATION TOOLS are Brainstorming, Benchmarking, Affinity diagram, Normal group technique.

PROJECT PLANNING AND IMPLEMENTATIONTOOLS are Gantt chart and PDCA Cycle.

Cause-and-effect diagram (also called Ishikawa or fishbone chart )

DESCRIPTION The fishbone diagram identifies many possible

causes for an effect or problem. It can be used to structure a brainstorming session. It immediately sorts ideas into useful categories.

WHEN TO USE When identifying possible causes for a problem. Especially when a team’s thinking tends to fall

into ruts

PROCEDURE MATERISLS REQUIRED: Flipchart (or) White Board, Marking Pens.

Agree on a problem statement (effect). Write it at the center right of the flipchart or whiteboard. Draw a box around it and draw a horizontal arrow running to it.

Brainstorm the major categories of causes of the problem. If this is difficult use generic headings: Methods Machines (equipment) People (manpower) Materials Measurement Environment

Write the categories of causes as branches from the main arrow.

Brainstorm all the possible causes of the problem. Ask: “Why does this happen?” As each idea is given, the facilitator writes it as a branch from the appropriate category. Causes can be written in several places if they relate to several categories.

Again ask “why does this happen?” about each cause. Write sub-causes branching off the causes. Continue to ask “Why?” and generate deeper levels of causes. Layers of branches indicate causal relationships.

When the group runs out of ideas, focus attention to places on the chart where ideas are few.

How to use the tool Identify the problem:

Write down the exact problem you face in detail. Where appropriate identify who is involved, what the problem is, and when and where it occurs. Write the problem in a box on the right hand side of a large sheet of paper. Draw a line across the paper horizontally from the box. This arrangement, looking like the head and spine of a fish, gives you space to develop ideas.

How to use the tool

Work out the major factors involved:Next identify the factors that may contribute to the problem. Draw lines off the spine for each factor, and label it. These may be people involved with the problem, systems, equipment, materials, external forces, etc. Try to draw out as many possible factors as possible. If you are trying to solve the problem as part of a group, then this may be a good time for some brainstorming. Using the 'Fish bone' analogy, the factors you find can be thought of as the bones of the fish.

Identify possible causes:For each of the factors you considered in stage 2, brainstorm possible causes of the problem that may be related to the factor. Show these as smaller lines coming off the 'bones' of the fish. Where a cause is large or complex, then it may be best to break it down into sub-causes. Show these as lines coming off each cause line.

Analyse your diagram:By this stage you should have a diagram showing all the possible causes of your problem. Depending on the complexity and importance of the problem, you can now investigate the most likely causes further. This may involve setting up investigations, carrying out surveys, etc. These will be designed to test whether your assessments are correct.

Example

The managing director of a weighing machine company received a number of irate letters, complaining of slow service and a constantly engaged telephone. Rather surprised, he asked his support and marketing managers to look into it. With two other people, they first defined the key symptom as 'lack of responsiveness to customers' and then met to brainstorm possible causes, using a Cause-Effect Diagram, as illustrated.

Example They used the 'Four Ms' (Manpower, Methods, Machines and

Materials) as primary cause areas, and then added secondary cause areas before adding actual causes, thus helping to ensure that all possible causes were considered. Causes common to several areas were flagged with capital letters, and key causes to verify and address were circled.

On further investigation, they found that service visits were not well organized; engineers just picked up a pile of calls and did them in order. They consequently set up regions by engineer and sorted calls; this significantly reduced traveling time and increased service turnaround time. They also improved the telephone system and recommended a review of suppliers' quality procedures..

CHECK SHEET (or) DEFECT CONCENTRATION DIAGRAM

DESCRIPTION A check sheet is a structured,

prepared form for collecting and analyzing data. This is a generic tool that can be adapted for a wide variety of purposes

WHEN TO USE When data can be observed and

collected repeatedly by the same person or at the same location.

When collecting data on the frequency or patterns of events, problems, defects, defect location, defect causes, etc.

When collecting data from a production process.

PROCEDURE Decide what event or problem will be observed.

Develop operational definitions. Decide when data will be collected and for how

long. Design the form. Set it up so that data can be

recorded simply by making check marks or Xs or similar symbols and so that data do not have to be recopied for analysis.

Label all spaces on the form. Test the check sheet for a short trial period to be

sure it collects the appropriate data and is easy to use.

Each time the targeted event or problem occurs, record data on the check sheet.

EXAMPLE

The figure below shows a check sheet used to collect data on telephone interruptions. The tick marks were added as data was collected over several weeks.

EXAMPLE A customer response group use a Check Sheet to track

the time band during the day when each customer calls. They then ensure that there are enough people available to cope with the heavy load periods. This reduces fatigue and increases customer satisfaction.

A drinks retailer has a Check Sheet to log the types of

purchase made, and changes her stock and displays to expand the range in the more popular types of wine. The result is a measurable increase in turnover.

A garage uses a Checklist to ensure all service points are completed. The engineer then signs it and gives a copy to the customer as an assurance. This significantly reduces service omission errors.

Histogram

A Histogram is a vertical bar chart that depicts the distribution of a set of data. Unlike Run Charts or Control Charts, which are discussed in other modules, a Histogram does not reflect process performance over time. It's helpful to think of a Histogram as being like a snapshot, while a Run Chart or Control Chart is more like a movie

Histogram

When you are unsure what to do with a large set of measurements presented in a table, you can use a Histogram to organize and display the data in a more user friendly format. A Histogram will make it easy to see where the majority of values falls in a measurement scale, and how much variation there is. It is helpful to construct a Histogram when you want to do the following

When Are Histograms Used?

• Summarize large data sets graphically • Compare measurements to specifications • Communicate information to the team • Assist in decision making

WHEN TO USE The data are numerical values. To see the shape of the data’s distribution,

especially when determining whether the output of a process is distributed approximately normally.

Analyzing whether a process can meet the customer’s requirements.

Analyzing what the output from a supplier’s process looks like.

Whether a process change has occurred from one time period to another.

To determine whether the outputs of two or more processes are different.

To communicate the distribution of data quickly and easily to others.

Constructing a Histogram

Step 1 - Count number of data points Step 2 - Summarize on a tally sheet Step 3 - Compute the range Step 4 - Determine number of intervals Step 5 - Compute interval width Step 6 - Determine interval starting points Step 7 - Count number of points in each interval Step 8 - Plot the data Step 9 - Add title and legend

Histogram Shapes and Meaning

Normal. A common pattern is the bell-shaped curve known as the “normal distribution.” In a normal distribution, points are as likely to occur on one side of the average as on the other.

Skewed. The skewed distribution is asymmetrical because a natural limit prevents outcomes on one side. The distribution’s peak is off center toward the limit and a tail stretches away from it.

These distributions are called right- or left-skewed according to the direction of the tail.

Double-peaked or bimodal. The bimodal distribution looks like the back of a two-humped camel. The outcomes of two processes with different distributions are combined in one set of data. A two-shift operation might be bimodal.

Plateau. The plateau might be called a “multimodal distribution.” Several processes with normal distributions are combined.Because there are many peaks close together, the top of the distribution resembles a plateau.

Dog food. The dog food distribution is missing something—results near the average. If a customer receives this kind of distribution, someone else is receiving a heart cut, and the customer is left with the “dog food,” the odds and ends left over after the master’s meal

Vilfredo Pareto was an economist who is credited with establishing what is now widely known as the Pareto Principle or 80/20 rule. When he discovered the principle, it established that 80% of the land in Italy was owned by 20% of the population. Later, he discovered that the pareto principle was valid in other parts of his life, such as gardening: 80% of his garden peas were produced by 20% of the peapods.

The Pareto PrincipleThe Pareto Principle

Pareto Chart (or) Pareto diagram (or) Pareto analysis

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.

Some Sample 80/20 Rule Applications 80% of process defects arise from 20% of the process

issues. 20% of your sales force produces 80% of your company

revenues. 80% of delays in schedule arise from 20% of the possible

causes of the delays. 80% of customer complaints arise from 20% of your

products or services.(The above examples are rough estimates.)

The Pareto PrincipleThe Pareto Principle

Graph that ranks data classifications in descending order from left to right

Pareto diagrams are used to identify the most important problems

Advantage: Provide a visual impact of those vital few characteristics that need attention

Resources are then directed to take the necessary corrective action

The Pareto DiagramThe Pareto Diagram

Helps a team focus on causes that have the greatest impact

Displays the relative importance of problems in a simple visual format

Helps prevent “shifting the problem” where the solution removes some causes but worsens others

The Pareto DiagramThe Pareto Diagram

Steps:Steps:

1. Determine the method of classifying the data: by problem, cause, type of nonconformity, etc

2. Decide if dollars (best), weighted frequency, or frequency is to be used to rank the characteristics

3. Collect data for an appropriate time interval

Constructing a Pareto DiagramConstructing a Pareto Diagram

Steps cont’d:Steps cont’d:

4. Summarize the data and rank order categories from largest to smallest

5. Compute the cumulative percentage if it is to be used

6. Construct the diagram and find the vital few

Constructing a Pareto DiagramConstructing a Pareto Diagram

When to UseWhen 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.

To identify the ‘VITAL FEW FROM TRIVIAL MANY’ and to concentrate on the vital few for improvement.

PARETO DIAGRAM

A Pareto diagram indicates which problem we should solve first in eliminating defects and improving the operation.

The Pareto 80 / 20 rule80 % of the problems are produced by 20 % of the causes.

The simplest way to determine if a cause and-effect The simplest way to determine if a cause and-effect

relationship exists between two variablesrelationship exists between two variables

Scatter DiagramScatter Diagram

Supplies the data to confirm a hypothesis that Supplies the data to confirm a hypothesis that

two variables are relatedtwo variables are related

Provides both a visual and statistical means Provides both a visual and statistical means

to test the strength of a relationshipto test the strength of a relationship

Provides a good follow-up to cause and effect Provides a good follow-up to cause and effect

diagramsdiagrams

Scatter DiagramScatter Diagram

Scatter Diagram (or) Scatter plot (or) X–Y graph

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.

When to Use 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.

Example If students who spend more time watching tv

have higher or lower grades

Relationship between production speed of an operator and a number of defective parts made

Focuses attention on detecting and Focuses attention on detecting and

monitoring process variation over timemonitoring process variation over time

Distinguishes special from common causes of Distinguishes special from common causes of

variationvariation

Serves as a tool for on-going controlServes as a tool for on-going control

Provides a common language for discussion Provides a common language for discussion

process performanceprocess performance

Control ChartsControl Charts

Run chart 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.

Run chart What it is: A run chart is a graph that shows the

changes in a process measurement over time. It can help you: . Recognize patterns of performance in a

process . Document changes over time

Run chart How to use it: Construct the chart. Label the vertical axis with the

key measurement of the process being measured. Collect the data. Collect data for an appropriate

number of time periods, in accordance with your data collection strategy. Plot the data. Plot each data point on the chart. Calculate and plot the average. This provides a

reference for drawing conclusions about individual data points.

Run chart 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.

Stratification (or) Flowchart (or) Run chartStratification is a technique

used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be impossible to see

When to Use Before collecting data. When data come from several sources

or conditions, such as shifts, days of the week, suppliers or population groups.

When data analysis may require separating different sources or conditions.

The ZZ-400 manufacturing team drew a scatter diagram to test whether product purity and iron contamination were related, but the plot did not demonstrate a relationship. Then a team member realized that the data came from three different reactors. The team member redrew the diagram, using a different symbol for each reactor’s data

Benefit from stratification.

Always consider before collecting

data whether stratification might be needed during analysis. Plan to collect stratification information. After the data are collected it might be too late.

On your graph or chart, include a legend that identifies the marks or colors used.

Control ChartsSlide 1 of 3

Control Charts Defined Control charts are used to determine whether a

process will produce a product or service with consistent measurable properties.


Steps Used in Developing Process Control Charts Identify critical operations in the process where inspection

might be needed. Identify critical product characteristics. Determine whether the critical product characteristic is a

variable or an attribute. Select the appropriate process control chart. Establish the control limits and use the chart to monitor

and improve. Update the limits.


An Example of When to Use a Control Chart Counting the number of defective products or

services Do you count the number of defects in a given

product or service? Is the number of units checked or tested

constant?

Activity Process Flow Chart for Finding the Best Way

Home Construct a process flow chart by making the best

decisions in finding the best route home. Refer to the prior notes on flowcharts.

Remember: Define and analyze the process, build a step-by step picture of the process, and define areas of improvement in the process.

Answer is on the next slide Example obtained from:

<http://deming.eng.clemson.edu/pub/tutorials/qctools/flowm.htm#Example>

Control Chart (or) Statistical process control

VARIATIONS Different types of control charts can be used,

depending upon the type of data. The two broadest groupings are for variable data and attribute data.

Variable data are measured on a continuous scale. For example: time, weight, distance or temperature can be measured in fractions or decimals. The possibility of measuring to greater precision defines variable data.

Attribute data are counted and cannot have fractions or decimals. Attribute data arise when you are determining only the presence or absence of something: success or failure, accept or reject, correct or not correct. For example, a report can have four errors or five errors, but it cannot have four and a half errors.

Variables charts X and R chart (also called averages and range

chart) X and s chart chart of individuals (also called X chart, X-R chart,

IX-MR chart, Xm R chart, moving range chart) moving average–moving range chart (also called

MA–MR chart) target charts (also called difference charts,

deviation charts and nominal charts) CUSUM (also called cumulative sum chart) EWMA (also called exponentially weighted moving

average chart) multivariate chart (also called Hotelling T2)

Attributes charts

p chart (also called proportion chart) np chart c chart (also called count chart) u chart

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