QMS Seven Improvemt Tools

Hochschule Emden/Leer

Faculty of Technology

Department of Mechanical Engineering

Seven Improvement Tools

Study course: Technical Management

Lecture: Quality Management Systems

Submitted by: Monica Posada (Matr.-No.5025888)

Adeel Rao (Matr.-No.5006747)

Daniel Orrego (Matr.-No.7001406)

Semester: WS 09/10

Lecture by: Prof.Dr.-Ing.Werner Kiehl

Date: Dicember 2010

Seven improvement tools Monica Posada, Adeel Rao, Daniel Orrego

TABLE OF CONTENTS

1. Introduction·························································································1

2. Flow chart····························································································1

2.1 Definition·······················································································1

2.2 Application Area··············································································2

2.3 Example························································································2

2.4 Advantages and

disadvantages··························································2

3. Check Sheet·························································································3

3.1 Definition·······················································································3

3.2 When to Use a Check

Sheet······························································3

3.3 Procedure·····················································································3

3.4 Example························································································4

3.5 Advantages and

disadvantages··························································4

4. Scatter Diagram·····················································································4

4.1 What is it·······················································································4

4.2 How to use it··················································································5

4.3 When to use it and not to use

it···························································5

4.4 Why to use it··················································································6

4.4.1 Advantages············································································6

4.4.2 Limitations··············································································6

4.5 Example························································································6

5. Histogram·····························································································6

5.1 Definition·······················································································7

5.2 Application Area··············································································7

5.3 How to use it·················································································7

5.4 Example·······················································································8

5.5 Advantages and

disadvantages··························································8

6. Pareto Analysis······················································································8

6.1 Definition·······················································································8

6.2 When to Use it················································································9

6.3 Procedure·····················································································9

6.4 Advantages and disadvantages························································10

7. Fishbone Diagram················································································10

7.1 What is it·····················································································10

7.2 How to use

it·················································································10


7.3 When to use it and not to use

it·························································11

7.4 Why to use

it·················································································11

7.4.1Advantages············································································11

7.4.2Limitations·············································································11

7.5 Example······················································································11

8. Control Chart························································································12

8.1 Introduction··················································································12

8.2 Monitoring Process········································································12

8.3 Description of control

charts·····························································13

8.4 Control charts for variables······························································13

8.5 Control charts for attributes·····························································14

9. Brainstorming·······················································································14

9.1 Introduction··················································································14

9.2 Approach·····················································································15

9.3 Order of events·············································································15

10. Mindmap······························································································15

10.1 Introduction···············································································15

10.2 Approach··················································································16

11. Conclusion···························································································16

References

APPENDIX


Table of figures

Figure 2.1 Simple

flowchart··············································································1

Figure 2.2 Production process flowchart·····························································2

Figure 4.1 Scatter Diagram Data·······································································6

Figure 4.2 Scatter Diagram··············································································6

Figure 5.1 Histogram······················································································8

Figure 6.1 Pareto Diagram··············································································9

Figure 7.1.Fishbone

Diagram··········································································12

Figure 8.1 Control Chart················································································13

Figure 9.1 Brainstorming: unsorted·································································15

Figure 9.2 Brainstorming: sorted·····································································15

Figure 11.1 Cost

optimisation··········································································16

Figure 11.2 Cost minimisation·········································································16



1. Introduction

Over 30 years, Japanese have studied and practiced what they call “Total Quality

Control” and one of the leaders in this movement has been Kaoru Ishikawa, whom,

with others repeatedly point to the fact that Japanese industrial workers are among the

worlds finest in their level of education and quantitative skills.

Consequently, one of the critical features of the Japanese approach to quality control is

its focus on quantitative methods on the factory floor.

Based on his long experience in Japanese industry, Ishikawa states that as much as

95 percent of quality related problems in the factory can be solved with seven

fundamental quantitative tools.

These tools are:

1. Flowchart

2. Check sheet

3. Scatter diagram

4. Histogram

5. Pareto analysis

6. Fishbone diagram

7. Control chart.

We will describe and explain these tools, in the following text. [1]

2. Flowchart

2.1 Definition

A flowchart is a schematic representation of an algorithm or a stepwise process,

showing the steps as boxes of various kinds, and their order by connecting these with

arrows.

The two most common types of boxes in a flowchart:

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

A decision (usually denoted as a diamond)

There are hundreds if not thousands of different

types of flowcharts, each with their own

repertoire of boxes and notational conventions.

1


Figure2.1 A simple flowchart representing a process for dealing with a broken lamp.

2.2 Application area

Flowcharts are used in designing and documenting complex processes. Like other

types of diagrams, they help visualize what is going on and thereby help to find flaws,

bottlenecks, or other non-obvious properties of a process.

A good flowchart should show all process steps under analysis by the quality

improvement team, identify critical process points for control, suggest areas for further

improvement, and help explain and solve a problem.

When to use a flowchart:

To develop understanding of how a process is done.

To study a process for improvement.

To communicate to others how a process is done.

To document a process.

When planning a project.

2.3 Example

The flowchart in Figure 2 illustrates a simple production process in which parts are

received, inspected, and sent to subassembly operations and painting. After

completing this loop, the parts can be shipped as subassemblies after passing a final

test or they can complete a second cycle consisting of final assembly, inspection and

testing, painting, final testing, and shipping. [4]

Figure2.2 A basic production process

flowchart displays several paths a part

can travel from the time it hits the

receiving dock to final shipping.

2


2.4 Advantages and disadvantages

Advantages of flowcharts:

1. Communication: Flowcharts are better way of communicating the logic of a

process to all concerned.

2. Effective analysis: With the help of flowchart, problem can be analysed in more

effective way.

3. Proper documentation: Flowcharts serve as a good documentation, which is

needed for various purposes.

4. Simplicity: A flowchart simplifies the maintenance of the procedural steps for a

process.

Disadvantages of flowcharts:

1. Alterations and Modifications: If alterations are required the flowchart may

require re-drawing completely.

2. Reproduction: As the flowchart symbols cannot be typed, reproduction of

flowchart becomes a problem.

3. Check Sheet

3.1 Definition

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.

3.2 When to Use a Check Sheet

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.

3.3 Check Sheet Procedure

1. Decide what event or problem will be observed. Develop operational definitions.

2. Decide when data will be collected and for how long.

3. 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.

4. Label all spaces on the form.

5. Test the check sheet for a short trial period to be sure it collects the appropriate

3


data and is easy to use.

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

sheet.

3.4 Check Sheet Example

(Taken from Root Cause Analysis, by Anderson and Fagerhaug):

A book store located in a large shopping mall consistently achieved lower sales per day

than budgeted. The staff noted that quite a few customers came into the store to

browse, but left without buying anything. When considering this problem a wide range

of possible causes surfed. The difficultly in identifying the actual problem and how often

it occurred make it difficult for the store personnel to implement any changes. Thus,

during a two-week period, many of the customers leaving without making purchases

were courteously asked why this happened. The responses were logged in a check

sheet, shown below, and give a much clearer idea of where to start to improve the

situation.

Cause of no purchase Week 1 Week 2Total number of

Occurences.

Could not find the item IIIII IIIII IIIII II IIIII IIIII IIIII IIIII 37

No offer to help IIIII IIIII IIII IIIII I 20

Item sold out II III 5

Item not carried III IIIII I 9

Prices too high I 1

Line too long I III 4

Wrong credit cards II 2

Poor lighting IIIII II IIIII IIIII II 19

No place to sit II IIII 6

Total number of causes 49 54 103

Table 3.1 Check Sheet [2]


Advantages:

Easy to use.

Effective way of displaying data.

Can identify the root cause of a problem

Provides structure for uniform data collection.

Can be used to substantiate or refute allegations

4


Disadvantages:

If the categories have been meticulously defined and inserted in the check

sheet, other significant occurrences may be overlooked.

4. SCATTER DIAGRAM

4.1 What is it

The scatter diagram shows the relationship between two variables acting continuously

on the same item. The scatter diagram illustrates the strength of the correlation

between the variables. This correlation can point to, but does not prove, a causal

relationship. It does not prove that one variable causes another one. For example,

analyzing a scatter diagram of the relationship between weight and height would lead

one to believe that the two variables are related. This relationship, however, does not

mean causality. For instance, while growing taller might cause one to weigh more, but

gaining weight does not indicate that one is growing up.

4.2 When to use it

It can be used to show relationship between two effects to see if they might result

from a common cause.

It can be used to examine the relationship between two causes.

4.3 How to use it

Step 1: Determine which two variables are to be plotted on the diagram.

Step 2: Collect data: Gather as more as possible paired samples of data, each sample

will contain one of each variable.

Step 3: Determine the highest and lowest values of each group of variables. This will

help determine the scale of both the horizontal(X) axis and the vertical(Y) axis.

Step 4: Draw the diagram

Draw an (X) and (Y) axis of equal lengths and label them from left to right and

bottom to top with values that will include all data points.

Plot all data points on the diagram.

Step 5: Interpret the data: Scatter diagram will generally show one of six possible

correlations between the variables:

Scatter Diagram Types of Correlation Interpretation

Strong Positive CorrelationThe value of Y clearly increases

as the value of X increases.

5


Strong Negative Correlation

The value of Y clearly

decreases as the value of X

increases.

Weak Positive Correlation

The value of Y increases

slightly as the value of X

increases.

Weak Negative Correlation

The value of Y decreases

slightly as the value of X

increases.

Complex Correlation

The value of Y seems to be

related to the value of X, but the

relationship is not easily

determined.

No Correlation

There is no demonstrated

connection between the two

variables

Table 4.1 Types of Correlation

4.4 Why to use it

4.4.1 Advantages

It can show the relationship between two variables which is difficult to find the

relationship.

Save time to determine the relationship between two variables.

It is easy to find which correlation of two variables through interpreting the

tendency from the diagram.

4.4.2 Limitations

Have to collect a large of data for clearly show the relationship, some expert

suggests that at least 30 paired samples of data should be gathered.

4.5 Example

Situation: The new commissioner of the American Basketball League wants to

construct a scatter diagram to find out if there is any relationship between a player’s

weight and her height. [10]

According to this scatter diagram, there does seem to be a positive correlation between

a player's weight and her height. In other words, the taller a player is the more she

tends to weight.

6


Figure X2. Scatter Diagram

Figure 4.2 Scatter Diagram

Figure 4.1 Scatter Diagram Data

5. HISTOGRAM

5.1 Definition

A histogram is a frequency distribution shows how often each different value in a set

of data occurs. A histogram is the most commonly used graph to show frequency

distributions. What distinguishes the histogram from a check sheet is that its data are

grouped into rows so that the identity of individual values is lost. [5]

5.2 Application area

Commonly used to present quality improvement data, histograms work best with small

amounts of data that vary considerably. When used in process capability studies,

histograms can display specification limits to show what portion of the data does not

meet the specifications.

When to use a histogram:

When the data are numerical.

When you want to see the shape of the data’s distribution, especially when

determining whether the output of a process is distributed approximately

normally.

When analyzing whether a process can meet the customer’s requirements.

7


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

When seeing whether a process change has occurred from one time period to

another.

When determining whether the outputs of two or more processes are different.

When you wish to communicate the distribution of data quickly and easily to

others. [5]

5.3 How to use it

Data collection: To ensure good results, a minimum of 50 data points, or

samples, need to be collected

Calculate the range of the sample data: The range is the difference between

the largest and smallest data points. Range = Largest point - smallest point.

Data points need to be divided on the X axis into classes. Table 5.1 below

lists some of the rules of thumb for determining the number of classes to use with

respect to the number of collected data points.

Calculate the size of the class interval. The class interval is the width of each

class on the X axis. It is calculated by the following formula: Class interval =

Range / Number of classes

Determine the class boundary. They are the largest and smallest data points

that can be included in each class.

Calculate the number of data points (frequency) that are in each class.

Draw the Histogram and plot the data.

Interpret the data. A histogram's shape shows the nature of the distribution of

the data, as well as central tendency (average) and variability. [6].

Table 5.1 Rules of thumb for class selection.

Figure 5.1 Histogram

5.4 Example

Company X manufactures small resistors

with a length of 5mm. A sample of 50

8

Sample size No of classes0-50 5-751-99 8-10100-250 10-15


resistors from the production line was taken for the construction of the histogram

(Figure 5.1).

It is clear that the majority of resistors have a length between 4 and 6mm. That means

that the manufacturing process is correct.


Advantages of histogram:

• Histograms are quite useful for depicting large differences in shape or

symmetry.

• Histograms are excellent when displaying data which have natural categories or

groupings - categorical data.

Disadvantages of histogram:

• Histograms cannot be used for more precise judgments such as depicting

individual values.

• Histograms are thus not recommended for data measured on a continuous

scale

6. Pareto Analysis

"The Pareto principle is named after Vilfredo Pareto, a 90th-century Italian economist.

He observed that 20 percent of the population controlled 80 percent of the wealth in

Italy during his time." The same observation was made for more people in other areas

of study, like Josep Juran a quality manager pioneer in 1930, who used pareto’s

principle "in observing that 29% of something is responsible for 80% of the results. The

80/20 rule has been known ever since as the Pareto Principle" {Webber 2007 #12: 127}

6.1 Definition

A Pareto chart is a specialized histogram, a "graph for showing frequency distributions,

which illustrate how often each unique value in a set of data occurs" {Webber 2007

#12: 137}, and it’s used to identify the issues that cause a large amount of your quality

problems. The tool provides a route to group and organize your data in a way that you

focus your time and resources in the most important problems.

Pareto Analysis is a statistical technique in decision making that is used for the

selection of a limited number of tasks that produce significant overall effect. It uses the

Pareto Principle also know as the 80/20 rule. The idea that by doing 20% of the work

you can generate 80% of the benefit of doing the whole job. Or in terms of quality

improvement, a large majority of problems (80%) are produced by a few key causes

9


(20%). This is also known as the vital few and the trivial many.

6.2 When to use it.

The 80/20 rule can be applied to almost anything:

80% of customer complaints arise from 20% of your products or services.

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

20% of your products or services account for 80% of your profit.

20% of your sales-force produces 80% of your company revenues.

20% of a systems defects cause 80% of its problems.

Pareto ordering is used to guide corrective action and to help the project team take

action to fix the problems that are causing the greatest number of defects first.

6.3 Procedure.

The 80/20 rule can be applied to almost anything:

Seven steps to identifying the important causes using Pareto Analysis:

1. Form a table listing the causes and their frequency as a percentage.

2. Arrange the rows in the decreasing order of importance of the causes, i.e. the

most important cause first.

3. Add a cumulative percentage column to the table.

4. Plot with causes on x-axis and cumulative percentage on y-axis.

5. Join the above points to form a curve.

6. Plot (on the same graph) a bar graph with causes on x-axis and percent

frequency on y-axis.

7. Draw a line at 80% on y-axis parallel to x-axis.

Then drop the line at the point of intersection with the curve on x-axis.

This point on the x-axis separates the important causes on the left and less important

causes on the right.

Figure6.1 Pareto Diagram

This is a simple example of a Pareto diagram using sample data showing the relative

10


frequency of causes for errors on websites. It enables you to see what 20% of cases

are causing 80% of the problems and where efforts should be focused to achieve the

greatest improvement.

The value of the Pareto Principle for a project manager is that it reminds you to focus

on the 20% of things that matter. Of the things you do during your project, only 20% are

really important. Those 20% produce 80% of your results. Identify and focus on those

things first, but don't totally ignore the remaining 80% of causes.


Advantages

By doing 20% of work you can generate 80% of the advantage of doing the

entire job.

Pareto analysis not only shows you the most important problem to solve, it also

gives you a score showing how severe the problem is.

Disadvantages

Work on the group with the highest score and dismiss the rest of the causes.

7. Fishbone Diagram

7.1 What is it

Dr. Ishikawa, a Japanese quality control statistician, invented the fishbone diagram.

Therefore, it is also called Ishikawa diagram. Because the design of the diagram looks

like the skeleton of a fish, it is often referred to as the fishbone diagram. The fishbone

diagram is used to explore all the potential or real causes that result in a single effect.

Due to the function of the fishbone diagram, it may be referred to as a cause and effect

diagram.

7.2 How to use it

Step 1: Identify and clearly define the Effect to be analyzed.

Step 2: Draw the structure of a fishbone diagram

Draw the effect in the ‘head of the fish’ at right side.

Draw a line as the spine of the fish to connect to its head across the middle.

Step 3: Identify the major cause categories and connect them to the backbone of the

fish.

List the categories of major factors which cause the effect.

Check the list against the following standard patterns[3]:

Typical production process categories

Machines – facilities and equipment

Methods – how work get done

Materials – components or raw materials

People – the human factor

11


Typical service process categories:

Policies – higher level decision rules

Procedures – steps in a task

Plant – equipment and space

People – the human factor

Other typical categories:

Environment – work culture, organizational structure, logistics

Measurement – calibration and data collection

Step 4: Identify sub-causes for each of the major categories

Identify as many causes or factors as possible and attach them as sub-

branches, and sub-sub-branches of the major branches.

Step 5: Identify potential root causes and take corrective actions

Analyzing each cause can eliminate causes one by one

Selecting the most probable root cause for corrective action

Assigning a priority to root causes for improvement (Pareto Diagram can be

used here to prioritize the causes)

7.3 When to use it and not to use it

It may be helpful to use the fishbone diagram in the following cases:

To analyze and find the root cause of a complicated problem

When there are many possible causes for a problem

If the traditional way of approaching the problem(trial and error, trying all

possible causes, and so on) is very time consuming

Of course, the fishbone diagram is not applicable to every situation. Normally we

should assembly a team to carry out the diagram. So here are a few cases in which we

should not use the fishbone diagram.

The problem is simple or is already known.

The team has experts who can fix any problem without much difficulty.

7.4 Why to use it

7.4.1 Advantages

Fishbone diagram is adaptable to analyze a variety of causes of problems. It has

been used successfully in business and industry.

Group can usually complete the work in a session lasting 1 to 2 hours.

There is a strong sense of involvement in resolving problems and in ownership of

results.

Participators need little training to complete the procedure.

No special equipment is needed.

The technique results in a graphic representation of the relationships that exist

between effects and their causes. [3]

7.4.2 Limitations

Although groups can quickly determine potential causes, fishbone does not usually

12


clarify sequences of causes.

The causes identified require verification of some kind.

7.5 Example

To diagram the causes relating to a car’s getting poor gas mileage. [9]

Figure7.1. Fishbone Diagram

8. CONTROL CHART

This report covers just the basics of control charts. Detailed information is

provided in the report “Statistical Process Control” of Group No. 3.

8.1 Introduction

Quality control is required to ensure that (production) processes are performing in an

acceptable manner. This can be done by using statistical techniques to monitor the

process output. No further action is required, when the results are acceptable,

otherwise correction is needed. Basically quality assurance can be distinguished in

“acceptance sampling” and “statistical process control”. The first one relies on

inspection after production and the second one on inspection during production. [11]

8.2 Monitoring Process

Monitoring (inspection) can be done at three different points:

Before production:

Used to make sure that all inputs are acceptable

During production:

Used to make sure that transformation/conversion of inputs into outputs is

acceptable

After production:

Used to make sure that all outputs are acceptable

13


The basic idea of inspection is to collect data which can be used to determine whether

items conform to a standard. Accordingly the following issues are important:

1. How often and how much to inspect?

High-volume and low-cost products such as wooden pencils do not

require many inspections, because production processes are generally

very reliable and the cost associated with passing defectives is quite

low.

Low volume and high-cost products such as aircraft require many

inspections because of the high cost of mission failure as well as the risk

to human safety.

When the sum of passing defectives and costs of inspection is minimized, the

amount of inspection is optimal.

2. At what points (where) within the process?

Raw materials:

Saving costs for goods which do not meet the required quality

Before a costly operation:

Do not monitor items in a lab which are already defective

Before a irreversible process:

Pottery can be reworked prior to firing

Before a covering process:

Painting, Assemblies, Plating

Finished products

It is more expensive to replace or repair already delivered products

3. On-site or centralized inspection?

On-site inspection is used for products such as ships, because those can not be

taken into a laboratory, whereas centralized inspection in a lab is used for items

such as medical products, food and so on.

4. Inspection of variables or attributes?

Variables are measured data, typically on a continuous scale (length of nails)

and attributes are counted data, for example the amount of products passing a

measure point.

[11]

8.3Description of control charts

Control charts are time-ordered plots of sample statistics and used to distinguish

between random and non-random variability.

14


One example of control chart is

presented on the right side.

Basically there are four different types of

control charts. Two types are used for

attributes (p- and c-chart) and two are

used for variables (mean- and range-

chart). [11]Figure 8.1 Control Chart

8.4 Control charts for variables

As mentioned above, range and mean charts (also known as “x-bar”) are used to

observe variables. The range charts observes the dispersion and the mean charts the

central tendency of a process.

The mean chart using the Upper-control-limit (UCL) and Lower-control-limit (LCL),

which are sensitive to shifts in the process mean, can be constructed by using the

following formulas:

and

The range control chart, which is sensitive to change in process dispersion, can be

constructed by using the following formulas:

and

where

: Average of sample means

: Standard normal deviate

:Standard deviation of distribution of sample means

:Given factor for x-bar-chart

:Given factor for lower control limit (R-chart)

:Given factor for upper control limit (R-chart)

: Average of sample ranges

[11]

8.5 Control charts for attributes

As mentioned above, the p- and c-charts are used to observe attributes, which are

counted rather than measured. A p-chart is for data consisting of two categories (pass

or fail, operate or do not operate) whereas a c-chart is appropriate when the number of

occurrences per unit of measure can be counted and non occurrences cannot be

counted (scratches, errors, faults).

15


The control limits (p-chart) can be calculated by using the following formulas:

and

While the control limits for a c-chart can be calculated by using the following formulas:

and

where

and : Number of defectives

: Sample size

[11]

9 BRAINSTORMING

9.1 Introduction

Brainstorming, a group creativity technique developed in 1930s by Alex Faickney

Osborn, is often used to find as many ideas as possible for a solution to a certain

problem. The technique is used areas of operations such as courtship/advertisement,

product development or construction.

Nowadays brainstorming is a popular technique, although researchers have not

confirmed its effectiveness. [12], [13]

9.2 Approach

According to [12], [13] Brainstorming is based on four basic rules to reduce the social

inhibitions in groups. These are:

1. Focus on quantity

2. No criticism

3. Unusual ideas are welcome

4. Combine and improve ideas

9.3 Order of events

First of all a group consisting of 5-20 people is required, thereby it is unimportant

whether they are experts or not. In the next step the problem needs to be identified and

explained, for example by a moderator or leader.

In the first main part of the process all participants are asked for their ideas concerning

the topic, which can be collected/noted on a blackboard, for instance. Within this step

the already mentioned four basic rules are applied. In the second main part all ideas

needs to be evaluated and unimportant ideas can be sorted out. Consequently the

amount of ideas can be reduced and a structured. [12], [13]

The following two pictures (FIGURE XY) show the result of a brainstorming process unsorted on

the left side and sorted on the right side.

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Figure 9.1 Braimstorming: unsorted Figure 9.2 Braimstorming: sorted

10 MINDMAP

10.1 Introduction

Mindmaps are diagrams and used to structure and visualize ideas around a central

idea, problem or key word. Furthermore they can be used to show the relationships

between different words and ideas.

Mindmaps have been used for centuries for problem solving by engineers or learning

by educators, for instance.

10.2 Approach

Mindmaps start with the key word or a picture in the middle. Around the centre are

several main branches which contain at least one word or a picture which is related to

the key word in the centre. Those main branches then again have several subbranches

which are also described by words. These words are directly related to the main branch

and indirectly to the centre word.

11. Conclusion

All mentioned tools can be used to improve the quality of products directly or at least

indirectly. As one can see, figure XX shows the importance of finding the right amount

of quality tests, whereas figure YY shows, that it is very important to find problems

concerning the product as soon as possible.

17


Figure 11.1 Cost optimisation Figure 11.2 Cost minimisation

18


References[1] K.Ishikawa, What is Total Quality Control? The Japanese Way, Englewood Cliffs, NJ:

Prentice-Hall.

[2] http://personnel.ky.gov/NR/rdonlyres/F974E25A-E77D-48B6-8435-0265CEE8D72C/0/

CheckSheet.pdf

[3] Fishbone (Cause-and-Effect) Diagram, EMRA, 1998

[4] http://www.devicelink.com/mddi/archive/98/04/012.html, Quality improvement

[5] http://www.asq.org/learn-about-quality/data-collection-analysis-tools/overview/

histogram.html, Histogram

[6] http://class.et.byu.edu/mfg340/lessons/seventools/histograms.html Histograms

[7] http://www.isixsigma.com/library/content/c010527a.asp

[8] Fishbone (Cause-and-Effect) Diagram, EMRA, 1998

[9] The example is taken from the following website

http://www.saferpak.com/cause_effect_articles/howto_cause_effect.pdf

[10] The example is taken from the following website

http://deming.eng.clemson.edu/pub/tutorials/qctools/scatm.htm

[11] William J. Stevenson (1996). Quality Assurance, In: Production/Operations Management

[12] http://de.wikpidia.org/wki/brainstorming

[13] http://en.wikpidia.org/wki/brainstorming

[14] http://de.wikpidia.org/wki/mindmap

[15] http://en.wikpidia.org/wki/mindmap


APPENDIX

Documents

QMS Seven Improvemt Tools