7 QC Tools
Basic QC Tools
1. Pareto Diagram
2. Cause & Effect Diagram
3. Graph
7 Tools
4. Check Sheet
5. Scatter Diagram
6. Histogram
7. Control Chart
Pareto Diagram
 Pareto charts are a type of bar chart in which the horizontal axis
represents categories of interest, rather than a continuous scale. The
categories are often “defects.”
 This tool is based on the idea that the majority of defects are
caused by few defective item, which classifies the quality problem into
the “vital few” and “trivial many” (80-20 rule).
 A cumulative percentage line helps you judge the
added contribution of each category.
 Pareto charts can help to focus improvement efforts on
areas where the largest gains can be made.
Example
50
100
40
80
30
60
80-20 rule
20
40
10
20
0
0
Defect
Count
Percent
Cum %
ng
w ro
ec
v ic
r
e
s
21
ode
pa
ym
t
no
ent
i ted
c red
on
Wr
d
g ad
res
ss
c on
ing
fu s
at
form
rate
fa
r in
c to
10
8
6
5
42.0
20.0
16.0
12.0
10.0
42.0
62.0
78.0
90.0
100.0
Figure 1 : Example of Pareto diagram
rect
cor
Percent
Count
Pareto Chart
Procedure
1. Decide on the problem to be addressed or items to study and
collect data.
2. Decide also the period for which the data is to be collected.
3. Arrange the data in order of decreasing size.
4. Calculate the cumulative number and percentage.
5. Draw horizontal and vertical axes on graph paper.
6. Draw the bar graph.
7. Draw the vertical axis on the right edge and scale it.
8. Draw the cumulative curve.
Complete the diagram with titles and units of reference.( Figure 1)
Cause and Effect Diagram
 The cause and effect diagram analysis was first developed by
Professor Kaoru Ishikawa of the University of Tokyo in the 1940s’, is
also known as the ‘Fishbone Diagram’ or the ‘Ishikawa Diagram’.
 His first application of this technique was in the Fulsai iron work
1953. Due to its’ final form, some people called it the “Fishbone
Diagram”.
 This tool is a picture of lines and symbols designed to represent the
relationship between the effects as problems and the causes
influencing them.
There is no “correct” way to construct a fishbone diagram, some
types lend themselves well to many different situations.
Example
Cause and Ef f ect Diagram
Measurements
Manpower
Coil
Speaker
Factor
Lack of
training
Small bone
Cone
Effect
attitudes
Wire
Poor
instruction
Bad transmitter
Solder joints
Defective volume
con
Backbone
Middle bone
wire destroyed by mice
Methods
Missing maintanance
tools
Intermitten
Irregular
voltage
Machines
Figure 2 : Example of Cause and Effect Diagram
Uses of Ishikawa Diagram
1. To recognize important causes
2. To understand all effects and causes
3. To compare operational procedures
4. To find major solutions
5. To figure out, what to do?
6. To improve the process
Procedure
1. State the problem as precisely as possible and draw the back bone.
2. Draw the large bone.
3. Get all members involved by participating in the brainstorming
session to obtain as many ideas as possible.
4. The ideas collected are then critically examined to classify them
into the main grouping and subsequent grouping (middle bone, small
bone and fine bone). (Figure 2)
5. Dram the middle bones, small bones and fine bones.
6. Check to see whether any causes have been left.
7. Identify the important causes by members vote, proper analysis of
data and Pareto diagram.
8. Fill in all related information such title, product, process, etc..
Graph
 Graph refer to the results of statistical analysis of data (numbers)
which are shown in diagrammatic form to communicate information.
 There are numerous types of graphs as listed are commonly use;
a. Bar graph
b. Line graph
c. Radar graph
f. Pie graph
 Each of above graphs is applicable based on analysis requirement.
Example
Weekly Brown Stain Fallout
Brown Stain Fallout
0.90
1.400
0.80
1.200
0.70
1.000
0.60
0.800
%
%
0.50
0.40
0.600
0.30
0.400
0.20
0.200
0.10
0.000
0.00
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
9
week
week
Bar Graph
Line Graph
Attandance Chart
5%
Self Improvement
3
Decisive
2
Leadership
1
0
Knowledge
Team Spirit
95%
Communication
Before
After
Radar Graph
Pie Graph
10
11
12
Check Sheet
Check sheets are sheets that are design in advance to collect the
necessary data easily and systematically, which allow the efficient
checking of all items for inspection and verification.
Procedure
1. Specify the aim of collecting data
2. Decide on the item to be check
3. Decide on the method for stratification
4. Format the check sheet
5. Analyze the data
6. Make clear the causes
7. Implementation of counter measure
8. Grasp the effect
9. Standardization of operations to practice the new and improves
method properly.
Scatter Diagram
 Scatter diagram is a diagram where the relationship between two
characteristic value are plotted and analyze as to whether a
correlation exists between the two set of data.
 Several types of correlation could be found from scatter diagram
are;
1. Positive strong correlation
2. Negative strong correlation
3. Positive moderate correlation
4. Negative moderate correlation
5. Absence of correlation
Example
Scatter Diagram f or BTU
19
BTU.In
14
9
4
5
10
15
20
BTU.Out
Figure 3 : Example of Scatter Diagram (Positive strong correlation)
Procedure
1. Collect and count the number of data
2. Determine the largest (L) and smallest (S) value of data
3. Select number of classes (bars)
Number of Data
30 - 50
Number of Classes (K)
5-8
51 - 100
6 - 10
101 - 300
7 - 13
4. Find class interval (H)
H=L-S/K
5. Determine starting point of classes
6. Calculate mid value of each class (half of the measurement unit)
7. Count frequency of data
8. Prepare the histogram
Histogram
 A histogram is a vertical bar chart that depicts the distribution of a
set of data.
 It is a useful tool to study the dispersion of data and analyze
certain quality characteristic of the product or service to which the
data in histogram refers.
 A histogram does not reflect the process behavior over time
Example
Histogram f or Camshaf t
Frequency
20
10
0
598
599
600
Supp1
Figure 4 : Example of Histogram
601
Procedure
1. Collect and count the number of data
2. Determine the largest (L) and smallest (S) value of data
3. Select number of classes (bars)
Number of Data
30 - 50
Number of Classes (K)
5-8
51 - 100
6 - 10
101 - 300
7 - 13
4. Find class interval (H)
H=L-S/K
5. Determine starting point of classes
6. Calculate mid value of each class (half of the measurement unit)
7. Count frequency of data
8. Prepare the histogram