Training Module

Problem Solving
Technique
Training module 1
By Vaseem Ahamad

Contents:
1. What is AQC, SQC and SPC?
2. What is data and type of data
3. Basic static
4. Flow diagram
5. Brain Storming
6. Brain storming
7. Graphs
8. Stratification
9. Pareto Analysis
10. Cause and effect diagram
11. Scatter diagram
12. Histogram
13. Control chart
Introduction
Basic Tools

What is AQC, SQC and SPC
AQC: Acceptance quality control
Acceptance or rejection of any material or product on the bases of
specification designed by the process/product designer.
There is no problem solving involves in AQC.
SQC and SPC : Statistical quality control and statistical quality
control
Common: Both are problem solving technique based on static
SPC: online problem solving technique
(Helps to rectify the problems at the time of occurrence of the
problem.)
SQC:
(Helps to rectify the problems at the time of occurrence of the problem.)
Difference:

What is data and type of data
Data
Variable data Attribute data
A set of required information in the form
of figures for statistical analysis of
problem.
Yes / No type data Counting data
1. Variable data is information
that can be measured on a
continuum or scale. Continuous
data can have almost any
numeric value and can be
meaningfully subdivided into
finer and finer increments,
depending upon the precision
of the measurement system.
Examples: 1. Cost of goods
2. Weight of the pouch
3.

mportance of data
Gut based statement Data based statement
1. I think that this year the production is
very high.
1. Last year Vs current year production
data
Last year
production
Current year
Production % Growth
50000 55000 10
2.I think the the problem of leakage
has been improved
Month
Sampled checked
for leakage
Leakaged
observed
%ofleaked
sample
Jan 1000 90 9
Feb 1000 80 8
March 1000 60 6
April 1000 50 5
2.Analysis of leakage related data

Supari drying process
2.8
3
3.2
3.4
3.6
3.8
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UCL
LCL
Mean
3.I think we have good control on our drying process
Gut based statement
Data based statement
mportance of data
3. Control chart of supari drying process

Basic Statistics: Average and Standard deviation
Average: It is simply the some of all observation and divided by the no of observation
Sample no 1 2 3 4 5 6 7 8 9 10 SUM X
Weight of the
pouch 19.5 19.8 20.1 20.3 20.5 20.1 19.8 19.6 19.9 19.8 199.4 19.94
1n
)XX(
s
2
−
−
=
∑
Standard deviation:
The standard deviation can be thought of as an average distance (the
standard) that each individual point is away from the mean.
S = 0.31
X = 199.4 / 10 = 19.94

low Diagram:
Flow diagram is graphical or a pictorial way to depict a process
A example of simple process of shoe purchasing:
Start
Go to the mkt.
Search out the
shoe shop
Enter in to the
Shoe shop
Ask for the type of the
shoe required by you
A
A
Check the shoes
for fitness
Not Ok
OK
Ask for price
Negotiate the
price
Not Ok
B
OK
Pay the price
Get it packed
Go back to
home
End
B

Symbol used in making the flow diagram:
Definition Symbols
Start and End
(rounded rectangle)
Activity Symbol
(Rectangle)
Decision symbol
(Diamond )
Connecting symbol
(Connector)
Flow line
(Arrow)

Type of flow diagram:
1. Macro level flow diagram : This is making the diagram of the total
process in a broad way. Here details are avoided. We do not use specific symbols
in making the macro level flow diagram.
2. Micro level flow diagram: It the detailed flow diagram of the process.
Different symbols are utilized to make the this.
3. Matrix flow diagram:There are some limitation even with micro level flow
diagram. Like there are lot of agencies are involved in a process and different
activity are performed by different person. But in micro level flow diagram the
responsibility, authority and control points not defined. This problem have been
addressed well in the matrix flow diagram.

Examples of matrix flow diagram:
S.N. Buyer Sales Person Shop owner Responsibility
Control
point
Go to the market
Search out the shoe shop
Enter in to the shop
Ask for the type
of the shoe required by
you
Show different variety
to the buyer
Explain the quality of
shoe
Check the shoe for
fitness
A
1
2
3
4
Not Ok
Ok
5

S.N. Buyer Sales Person Shop owner Responsibility
Control
point
A
Ask the price
Negotiate the price
Pay the amount
Receive the
amount
Pack the shoe
and hand over
to buyer
Ask for pack
the shoe
Receive the pack
6
7
8
9
10
Matrix flow diagram contd----

Significance of flow diagram:
1. It gives the complete picture of a process in one view.
2. It talks about the sequence of the activities in a process.
3. It also talks about the responsibility, authority and control points.
4. It is helpful to explain the process in very short time.
When to use the flow diagram;
1. For defining the process flow.
2. For defining the problem.
3. For identifying the root cause.
4. For devising the solution.
5. For implementing the solution.
6. At the time of review and follow up.

What is Brainstorming ?
A technique to generate a
large number of ideas or
possibilities in a relatively
short time frame.
Why use Brainstorming ?
•A tool for the Team(not individual).
•A method to generate lot of ideas
•Two persons’ knowledge and ideas are
always more than an individual’s
•Input for other C & E tools

Team Selection for BrainstormingTeam Selection for Brainstorming
Potential Members
•Stakeholders - Insure buy-in
•Process Owners - Vested Interest
•Process Experts - Historic view
•Process Participants - Daily experience
•Facilitator - Impartial guide
•Expert in the tool - Technical advice
•Testimonial Person - Adds Credibility
Diverse team Delivers an Objective Outcome

Rules of BrainstormingRules of Brainstorming
Rules
•Take turns to speak
•Every member has equal opportunity to
contribute ideas
•Encourage each other to contribute
•Listen and respect others’ ideas
•Build on existing ideas
•Focus on the topic
•Do not criticize ideas, no negative
comments

How to Conduct a Brainstorming SessionHow to Conduct a Brainstorming Session
•Agree on and write down the problem statement
•Allow each team member to contribute - No Criticism!
•In rotation or free flow
•List all ideas on Chart Paper/ Board - visible to all
•Continue untill ideas are exhausted
•Review the list for clarity/duplicates
•Use as input for next step in Cause & Effect Process

What is Graphs
Graph is a pictorial representation of data which, when presented , is easily
understandable. It helps to represent large amount of information
comprehensively and in a compact manner.
Month Jan Feb March April May June Jully August Sep Oct Nov Dec
Production 4000 3800 3900 3000 3800 4200 4300 8000 5100 5500 6000 7000
Trend of production for year 2003
0
2000
4000
6000
8000
10000
Jan
Feb
March
April
May
June
Jully
August
Sep
Oct
Nov
Dec
month
Prodnfigure

Types of Graphs
Graphs can be divided in to two main groups:
A. Commonly used graph
1. Line graph
2. Bar graph
3. Pie chart or circle graph
B. Special purpose graph
1. Belt graph
2. Compound graph
3. Strata

0%
20%
40%
60%
80%
100%
Machine
setting
Rework Mfg Defect
January Febuary March
6
6.5
7
7.5
8
8.5
January Febuary March
Actual
Target
Shar e
61%
22%
17%
RG Tulsi Zarda Gutkha
0.7 0.8
3.01
1 1
3.5
0
1
2
3
4
RG1.75 gm RG4 gm RG100 gm
Actual wastage
Target
Sales
300
390 400
450
500
0
100
200
300
400
500
600
1199 2000 2001 2002 2003
Line Graph
Bar Graph Pie Graph
Belt Graph Compound Graph
Strata Graph
AnalysisofQualitycost
0
100
200
300
2002 2003 2004
Years
Qualitycostin
lacs
Preventioncost
Appraisalcost
Failurecost

The points which are to be considered while
making the graph
1. Use the appropriate form of the graph to show the
data
2. In line & bar graphs , the X and Y axes must be
appropriately labeled with correct unit of measures.
3. Be sure to give your graphs an appropriate title that
explains what the data measures.
4. Use the correct font size to match the tax with graph
area.
5. Chose the different colors for different items.

Line Graph
1. It is used to show continuing data
2. When the data changes continuously
over the time.
3. When to show the effect of an
independent variable on the
dependent variable.
4. When to see the tend of the data
5. When to show the comparison of two
series of data for different period.
When to use line graph:
Examples
1. Yearly maintenance cost
2. Yearly wastage
3. Month wise absenteeism
4. Overheads
5. Year wise production
Analysis of yearlymaintenance cost
5
7 8
10
13
16
19
0
5
10
15
20
1997 1998 1999 2000 2001 2002 2003
years
Costinlacs
Interpretation:
1. There is increasing trend
2. Maintenance cost increases as the
time passes

Bar Graph
Where to use bar chart
1. When the data is one time.
2. When there are the different item.
3. When two items being compared do
not need to affect each other.
Examples
1. Analysis of actual wastage Vs target
wastage variety wise.
2. Budgeted maintenance cost Vs actual
maintenance cost shed wise for year 2003
3. Analysis of actual production Vs target.
Analysis of Actual maint. Cost Vs Budgeted Maint. cost
4
5
9
3.5 4
7
0
5
10
Shed 39 Shed 5 Shed 6&7
Maintcostinlacs
Actual maintenance cost Budgeted maintenance cost
Shed39 Shed5 Shed6&7
Actualmaintenancecost 4 5 9
Budgetedmaintenancecost 3.5 4 7
How to arrange the data Bar graph of the data

Pie chart
Where to use pie chart
1. Used to show the relative proportion
of various components.
2. This is very effective way to show the
percentage contribution in the whole.
Examples
How to arrange the data Pie graph of the data
1. Contribution of different cost
component in the overheads
2. Contribution of different business
unit in the total sales of DS Group.
3. Proportion of time spends in the
different activities by a manager.
4. Percentage of different components
of your expenditure.
Allocation of a manager's time in different activities.
35%
25%
15%
15%
10%
Communication
Planning
Operation
Problem solving
IR handling
Cost component Time Percentage
Communication 35
Planning 25
Operation 15
Problem solving 15
IR handling 10

Belt Graph
When to use belt graph
1. It is like a pie chart but here we use
bar to show the percentage
2. The main difference is that in belt
chart we can show the information for
more than one item by a single graph.
Examples
1. To show the breakage of wastage
for different shed
2. To show the receiving status of
delivery
Analysis of performance of delivery of consignment
75
90
60
15
10
25
10
0
15
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan Feb March
consignmentintermsof%
Delay
Before time
On time
Belt graph of the data
Month Ontime Beforetime Delay
Jan 75 15 10
Feb 90 10 0
March 60 25 15
How to arrange the data

Compound Graph
When to use compound graph
1. It is the combination of line and bar
chart.
2. It is generally used when we compare
the actual performance with the set
target
3. It is also applicable in the pareto
diagram.
Examples
1. Analysis of actual wastage of RG 4
gm against the target.
2. Analysis of of break down hours as
a percentage of total running hours
against target.
3. Defect-wise % and cumulative % in
pareto diagram.
Compound graph of the dataHow to arrange the data
Analysis of break down hrs
1.5
1
0.8 0.75
0
0.5
1
1.5
2
Jan Feb March April
b/dhrsas%oftotalrunning
time Actual b/d %
Target
Month Actualb/d% Target
Jan 1.5 1
Feb 1 1
March 0.8 1
April 0.75 1

Compound Graph
When to use strata graph
1. It is used to show the trend in the total
and component of the total.
Examples
1. Analysis of the quality cost.
Strata graph of the dataHow to arrange the data
Analysis of Quality cost
0
100
200
300
2002 2003 2004
Years
Qualitycostin
lacs
Prevention cost
Appraisal cost
Failure cost
Quality 2002 2003 2004
Failurecost 120 80 30
Appraisalcost 45 30 25
Preventioncost 35 40 55

What is Stratification
Stratification is a process of
separation of data in to categories.
It is normally done for identifying
the categories contributing to the
problem tackled.

• Quality is influenced by multiple causes
or element. It means that every problem is
the manifestation of several causes.
• Compounding effect of these causes
make it difficult to find a clear
relationship between causes and problem.
•So that we do the stratification of data to
find out whether we can get the indication
of problem points.
Why do we do stratification

Illustration of stratification
Example: DSL- Guwahati have 10 % absenteeism for the year 2003
How to apply the stratification in this case.
Step 1. See the absenteeism month wise
Step 2. See the absenteeism cadre wise
Step 3. See the absenteeism temporary Vs
permanent employee.

Step 1. Month wise stratification
Month w ise absenteeims of the employee of DSL-Guw ahati
1
0 0.5 1 0.5 1
4
6
0 0
1
0
0
2
4
6
8
Ja
n
F
e
b
M
a
rch
A
p
ril
M
a
y
Ju
n
e
Ju
lyA
u
g
u
s
t
S
e
p
O
ct
N
o
v
D
e
c
Absenteeismin%
Month Jan Feb March April May June July August Sep Oct Nov Dec
%of Absenteeism 1 0 0.5 1 0.5 1 4 6 0 0 1 0
Inferences drawn from the graph:
• 80% of the absenteeism is in the month of July and august.
•There is heavy rain fall in these two month there fore rain fall may be the main cause of
absenteeism.

Step 1. Cadre wise stratification
Cadre M E O S W
% of Absenteeism 0 1 2 3 9
• 73 % of the absenteeism is because of workmen.
•There may be dissatisfaction specially among the worker.
Analysis of absenteeism cadre wise
0 1 1 2
11
0
5
10
15
M E O S W
Cadre
Absenteeisnin%

Step 1. Temporary Vs permanent workmen stratification
Analysis of Absenteeism in W/M Temp. Vs
Permanent
3
8
0
2
4
6
8
10
Permanent Temporary
Absenteeism%
Type of workmen Permanent Temporary
Absenteeism % 3 8
• 73 % of the absenteeism is because of temporary workmen
•There may be lack of ownership among the temporary workmen.

Pareto Principle or 80/20 rule or Vital few and trivial many:`
Origin of Concept:
The Italian economist VILFREDO PARETO developed this concept
while studying the distribution of wealth in his country.
He found that 80-90% of Italy,s wealth lay in the hands of 10-20% of
the population.
A similar distribution has been found practically true in many other
fields like:
1. 80 percent of problem / defects are because of 20 % causes.
2. 80 % of rejection are because of 20 % of defects.
3. 80% of salary is drawn by 20 % of employee.
4. 80 % absenteeism are because of 20 % of causes.
5. 80 % of accident are because of 20 % of causes.

Pareto Analysis:
The technique of arranging data according to priority or
importance and using it to a problem solving frame
work is called pareto analysis.
Implication:
1. Identifying the really important problem
2. Establishing the priorities for action.

Pareto analysis procedure :
Pareto analysis is comprises of following steps
1. List all the element
2. Measure the element
3. Rank the element.
4. Create cumulative distribution.
5. Draw the pareto curve.
6. Interpret the pareto curve.

Illustration of pareto analysis with the help of a example:
Sl. No. Causes of rework
1 Empty pouches
2 over weight
3 leakage
4 wrinkle
5 lining
6 under weight
7 Misprinting
8 wrong position of perforation
9 Desealing
10 No V notch
Problem : Rework
Step 1. List out all the causes of rework

Sl.No. Causes of rework 1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-May Sum
1 Empty pouches 0.5 0.4 1 0.8 1 0.6 0.8 0.6 0.5 0.4 6.6
2 over weight 4 4.5 4 3 3.5 3 3.5 4 4 3.6 37.1
3 leakage 0.8 0.5 0.6 0.6 0.8 0.5 0.6 0.4 0.8 0.7 6.3
4 wrinkle 0.3 0.5 0.4 0.5 0.6 0.4 0.6 0.5 0.4 0.3 4.5
5 lining 0.7 0.6 0.8 0.1 0.8 0.2 0.5 0.2 0.4 0.7 5
6 under weight 3.5 3 2.5 2 3 3.1 2.9 3.5 3 2.6 29.1
7 Misprinting 1 0.5 0.6 0.3 0.9 0.8 0.6 0.8 0.4 0.8 6.7
8
wrong position of
perforation 0.5 1 0.8 0.9 0.2 0.3 0.5 0.4 0.7 0.8 6.1
9 Desealing 1 1.5 0.6 1 1 0.6 0.5 0.8 0.6 0.7 8.3
10 No V notch 0.9 0.3 0.4 0.8 0.8 0.9 0.5 0.6 0.3 0.54 6.04
Rework per day 13.2 12.8 11.7 10 12.6 10.4 11 11.8 11.1 11.14 115.7
Illustration contd-----
Step 2. Measurement of element / causes

Step 2. Ranking and rearrangement of the element as per ranking
Sl No. Causes of Rework
Waste
laminate
(Kg) Ranking
1 over weight 37.1 1
2 under weight 29.1 2
3 Desealing 8.3 3
4 Misprinting 6.7 4
5 Empty pouches 6.6 5
6 leakage 6.3 6
7
wrong position of
perforation 6.1 7
8 No V notch 6.04 8
9 lining 5 9
10 wrinkle 4.5 10

Sl No. Causes of Rework
Waste
laminate
(Kg) Cum %
Cum % of
total
1 over weight 37.1 37.1 32.05 32.05
2 under weight 29.1 66.2 25.14 57.20
3 Desealing 8.3 74.5 7.17 64.37
4 Misprinting 6.7 81.2 5.79 70.16
5 Empty pouches 6.6 87.8 5.70 75.86
6 leakage 6.3 94.1 5.44 81.30
7
wrong position of
perforation 6.1 100.2 5.27 86.57
8 No V notch 6.04 106.24 5.22 91.79
9 lining 5 111.24 4.32 96.11
10 wrinkle 4.5 115.74 3.89 100
Step 2. Create cumulative distribution

over weight
under w eight
Desealing
Misprinting
Empty pouches
leakage
wrong position of perforation
No V
notch
lining
Others
37.10 29.10 8.30 6.70 6.60 6.30 6.10 6.04 5.00 4.50
32.1 25.1 7.2 5.8 5.7 5.4 5.3 5.2 4.3 3.9
32.1 57.2 64.4 70.2 75.9 81.3 86.6 91.8 96.1 100.0
0
20
40
60
80
100
120
0
20
40
60
80
100
Defect
Count
Percent
Cum %
Percent
Count
10 20 30 40 50 60 70 9080 100
Step 2. Draw the pareto curve

Step 6. Interpret the pareto curve
• 30 % of the causes (ie over weight, under weight and
diseasing) are responsible for almost 80 % of the problem ie
rework.
• It means that these three causes are vital and other are trivial
but useful.
Conclusion:
• First we should attack the vital causes.
• If we eliminate these vital causes, 80 % rework would be
eliminated.

Cause and Effect Analysis:
Cause & Effect analysis is used to generate / draw all possible
causes of problem
This technique comprises usage of cause and effect diagram and
brain storming
Brain storming To generate the all possible
causes of the problem
Cause & effect diagram
Or
Ishikawa diagram
Or
Fishbone diagram
Logical and systematic
representation of all possible
causes in the form of diagram
1
2

How to make a cause and effect diagram:
1. One who wants to make a cause and effect diagram should
first understand cause and effect relationship
Lungs cancer
Heavy smoking
2. One who solves a problem successfully, is the one who can
make a useful cause effect diagram
There are three type of cause and effect diagram.
1. Dispersion analysis type.
2. Production process classification type.
3. Cause enumeration type.

1. Dispersion analysis type.
In this method we broadly divides the root
causes in to 6 categories i.e. 5 Ms and 1E
(Man, Material, Methods, Machine,
Measurement and Environment.)
Then we calls together everyone involved
with the process and ask for the sub-
causes under these main causes.

Illustration with the help of the examples: Problem : Desealing
Desealing of Pouches
Improper sealer
setting
wrong temp setting
No variable temp
as per req.
Increase of gap
between the plates
Change in the GSM of
laminate
Mfg defect in the
laminate
No std methods of se
Improper fixing of
laminate
Faulty calibration
system
Moisture over lamina
Dust over laminate
Personnel
Machines
Materials
Methods
Measurements
Environment
Cause and effect diadram of desealing of pouches

. Cause enumeration type:
1. Problem to be explained to everyone who involves in
the brain storming session
2. Brain storming is carried without categorization of
main causes to get the maximum possible causes.
3. Allocation/arrangement of all possible sub-causes
under the broad category of main causes.

Correlation Analysis:
In “Cause and effect analysis “ we found that there
may be various causes for a single problem and inter-
relationship which exist between the cause and effect is
purely qualitative and hypothetical.
But in correlation analysis we quantify the extent of
relationship of a cause with the problem.

Correlation analysis
Scatter Diagram Coefficient of correlation
Scatter Diagram is a graphical
representation of relationship
between two variable. It can be
cause and effect and between
two causes.
It also reveals the nature of
relationship.
Coefficient of correlation
reflect the strength of
relationship in quantitative
form. It is denoted by r.

How to make the scatter diagram:
1. Identify the two factors which are supposed to be inter-related
with each other.
2. For the selected values of the independent factors, collect
observation for the dependent factor and record on the data
sheet.
3. Plot the points on the scatter diagram, using the horizontal
axis for the independent factor and vertical axis for the
dependent factor.
4. Analyze the diagram.

Month
Preventive
Maint. Hrs
Break Down
Hrs
Loss of
Production
Eff of
machine Sale
Jan 100 16 10 75 300
Feb 125 13 8 78 295
March 150 11 6 94 290
April 90 18 11 65 315
May 160 10 6 100 340
June 130 12 7 81 310
July 140 11 7 88 300
August 95 17 10 70 298
Sep 150 11 6 94 298
Oct 125 13 8 78 300
Nov 190 8 5 119 320
Dec 180 9 5 113 380
Illustration with the help of the examples.

Interpretation of scatter diagram and correlation of coefficient.
Preventive
Maint. Hrs
Eff of
machine
X Y
100 75
125 78
150 94
90 65
160 100
130 81
140 88
95 70
150 94
125 78
190 119
Scatter diagramof M/c Eff. and PMHrs.
0
50
100
150
0 50 100 150 200
PMHrs
M/CEfficiency
correlation of coefficient
Coefficient of correlation between X
and Y i.e. r = .981
Interpretation
• There is a positive correlation between PM
Hrs and Eff. of machine
•.Coefficient of correlation i.e. r = .981which
shows that there is very strong relationship
between both factors.

and Y i.e. r = -0.974
Interpretation
• There is a negative correlation between PM
Hrs and b/d hrs.
•.Coefficient of correlation i.e. r = -0.974
which shows that there is very strong inverse
relationship between both factors.
Pre ve ntive
Ma int. Hrs
Brea k
Dow n
Hrs
x Y
100 16
125 13
150 11
90 18
160 10
130 12
140 11
95 17
150 11
125 13
190 8
Scatter diagram of PM hrs vs B/D hrs.
0
5
10
15
20
0 50 100 150 200
PM Hrs
B/DHrs

Preventive
Maint. Hrs Sale
X Y
100 350
125 295
150 300
90 315
160 340
130 310
140 250
95 298
150 298
125 300
190 320
Scatter diagramof PMHrs Vs Sales
280
300
320
340
360
0 50 100 150 200
PMHrs
MonthlySalesin
Cases
and Y i.e. r = -0.071
Interpretation
• There is no correlation between preventive
maintenance and sales figure.
•.Coefficient of correlation i.e. r = -0.071
which shows that there is no correlation
between

Significance of the correlation in problem solving:
• Correlation is help to find out the nature and strength of
relationship between the cause and effect.
• One we know the strength of relationship, we can control
the dependable factor ( effect) by controlling the independent
factor (cause).
• We can again check the value of ‘r’ in the controlled
condition.

Control Charts
KEY REQUIREMENTS
• Working atmosphere suitable for action
• Fundamental process knowledge
• Charting of KPIV’s or KPOV’s over time
• A well-defined, accurate and precise measurement system
• Elimination of unnecessary external causes of variation

Control Chart Structure
SAMPLE NUMBER
Region of Nonrandom Variation
Region of Nonrandom Variation
Region of Random Variation
UCL
LCL
X Center Line
Lower Control Limit
Upper Control Limit
UCL
UCL
LCL
LCL
X
In Control
Out of Control
Control Limits
are NOT Spec
Limits !!!!!

What is the Center Line?
•The average of value of the characteristic you are
attempting to control
•Calculated during the time a time when your process was
“in control”
The center line is not necessarily the center of
your spec...but will be for a centered process!!!
The center line is not necessarily the center of
your spec...but will be for a centered process!!!

Control Limits are NOT Specification Limits !!!Control Limits are NOT Specification Limits !!!
So, what are Control Limits ?
•Statistical limits based on the standard deviation (σ) or the
common cause variation inherent in your process
when it is “in control”
•Generally, “3σ control limits” are used because they cover
99.73% of the normal distribution
•Minimizes our risk of taking action when it is not needed
•The control limits are set during a time frame that the
process in ‘in control’
•Control limits are NOT related to specification limits in any
way !!

CAUTION: Being “in control” does not always
mean you are “in spec”!
CAUTION: Being “in control” does not always
mean you are “in spec”!
What do you mean “In Control”?
•‘In Control’ is a statistical term
•A process is “in control” when only common
causes of variation are acting on it
•Common causes of variation are the myriad of
factors that cause random variation, variation
that is inherent in the process

How do I use it?
•Select KPIV or KPOV you want to control
•Identify local process owner
•Select appropriate control chart type
•Define data collection process (include GRR)
•Identify signals and corrective actions
•Collect and plot data during a period of time when
the process is “in control” to create the chart
•Train the local process owner on how to create
and use the control chart

Control Chart Structure
Xdbar = average Xbar UCL = Xdbar + A2*Rbar LCL = Xdbar - A2*Rbar
SIGNALS FOR ACTION
1
2
3
4
5
6
ACTION INSTRUCTIONS
1
2
3
Rbar = Average R UCL = D4Rbar LCL = D3Rbar 4
5
6
CONTROL CHART FACTORS
Size A2 D3 D4
2
3
4
Date/Time 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 5
1 6
2 7
3 8
4 9
5 10
Xbar The process must be in control before
Range capability can be calculated.
UCL
LCL
UCL
LCL
Center Line
Center Line
3. SIGNALS
5. CALCULATION
FACTORS
1. PLOT AREA
2. DATA LOG
4. ACTIONS

HSS03/96 34
How do I know when I’m “out of control”?
Anything outside the upper or lower control limits always
signals that the process has gone out-of-control
Otherwise we look for nonrandom patterns using established
rules for out-of-control (Minitab has these built in)
Note: You don’t have to use all of the rules...limit the rules to
only those where its feasible to take action.
© AlliedSignal 1995 - Dr. Steve Zinkgraf

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