3. Flow Charts:
The benefits of flowcharts are as follows:
Communication: Flowcharts are better way of communicating the flow of a
system to all concerned.
Effective analysis: With the help of flowchart, problem can be analyzed in
more effective way.
Proper documentation: Program flowcharts serve as a good process
documentation, which is needed for various purposes.
Identifying errors: By breaking the process down into its constituent steps,
flowcharts can be useful in identifying where errors are likely to be found in the
system
• Flowcharts are pictorial representations of a process.
5. What is a check sheet ?
The Check Sheet is a data-gathering and interpretation tool
A Check Sheet is used for:
distinguishing between fact and opinion
gathering data about how often a problem is occurring
gathering data about the type of problem occurring
The most straight forward check sheet is simply to make a list of items that you
expect will appear in a process and to mark a check beside each item when it does
appear. This type of data collection can be used for almost anything, from checking
off the occurrence of particular types of defects to the counting of expected items
(e.g., the number of times the telephone rings before being answered).
6. Example of Check Sheet:
(continuous data use) No.___________741
PRODUCTION CHECK SHEET
Product Name_________________________________Alternator Pulley Date_________________________________12- 02- 02
Usage________________________________________Pulley Bolt Torque Factory_______________________________Church Street
Specification__________________________________2.2 +/- .5 Section Name__________________________SI Line
No. of Inspections______________________________185 Data Collector__________________________Sam The Man
Total Number__________________________________185 Group Name___________________________
Lot Number___________________________________1631 Remarks:_____________________________
Dimensions 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2
40
35
SpecLSL
SpecUSL
30
25
20
15 X
XXXX
XX X
XXXXX
XX
10
XXX
XXXXX
XXXXX
XXXXX
XX
XXXXX
XXXXX
XXXXX
XXX
5
XX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXX
X
0 X
XX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XXXXX
XX
X
TOTAL
FREQUENCY 1 2 7 13 10 16 19 17 12 16 20 17 13 8 5 6 2 1
8. • The purpose of a Histogram is to take the data that is
collected from a process and then display it graphically to
view how the distribution of the data, centers itself around
the mean, or main specification. From the data, the
histogram will graphically show:
The center of the data.
The spread of the data.
Any data skew ness (slant, bias or run at an angle).
The presence of outliers (product outside the specification
range).
The presence of multiple modes (or peaks) within the data.
9. Calculations needed…
Mean The average of all the values.
Minimum The smallest value.
Maximum The biggest value.
Std Dev An expression of how widely spread the values are
around the mean.
Class
Width
The x-axis distance between the left and right edges of
each bar in the histogram.
Number of
Classes
The number of bars (including zero height bars) in the
histograms.
11. The standard normal
distribution, with its zero
skewness and zero kurtosis
The standard normal distribution, with
its zero skewness and zero kurtosis
Example: Histogram
12. A truncated curve, with the peak at or
near the edge while trailing gently off
to the other side, often means that part
of the distribution has been removed
through screening, 100% inspection, or
review. These efforts are usually costly
and make good candidates for
improvement efforts
A double-peaked curve often means
that the data actually reflects two
distinct processes with different
centers. You will need to distinguish
between the two processes to get a
clear view of what is really happening
in either individual process
Example: Histogram
13. Outliers in a histogram – bars that are
removed from the others by at least the
width of one bar – sometimes indicate that
perhaps a separate process is included. It
may also indicate that special causes of
variation are present in the process and
should be investigated
A plateau-like curve often means that
the process is ill-defined to those
doing the work, which leaves
everyone on their own. Since
everyone handles the process
differently, there are many different
measurements with none standing
out. The solution here is to clearly
define an efficient process
Example: Histogram
15. • Pareto chart
• The Pareto chart is a specialized version of a histogram that ranks the
categories in the chart from most frequent to least frequent. A Pareto
Chart is useful for non-numeric data, such as "cause", "type", or
"classification". This tool helps to prioritize where action and process
changes should be focused. If one is trying to take action based upon
causes of accidents or events, it is generally most helpful to focus efforts
on the most frequent causes. Going after an "easy" yet infrequent cause
will probably not reap benefits.
What Questions The Pareto Chart Answers
What are the largest issues facing our team or business?
What 20% of sources are causing 80% of the problems (80/20 Rule)?
Where should we focus our efforts to achieve the greatest improvements?
16. Build a Pareto chart
Sort your data in descending order by frequency of occurrence
Delay Due To Frequency % Cumulative %
Tool Change 31 36 36
No Raw material 26 30 66
No Steam Pressure 15 17 83
Power failure 9 10 93
others 6 7 100
87
Create Percentage and Cumulative Percentage columns, as shown above.
19. Cause & Effect Diagram
The cause and effect diagram is used to explore all the
potential or real causes (or inputs) that result in a single
effect (or output).
Causes are arranged according to their level of importance or
detail.
Causes in a cause & effect diagram are frequently arranged
into four major categories. While these categories can be
anything, you will often see:
Manpower, Methods, Materials, and Machinery (recommended for
manufacturing)
equipment, policies, procedures, and people (recommended for
administration and service).
20. Example
MAN MACHINE
METHOD MATERIAL ATMOSPHERE
Lack of
operator
Skill.
Lack of
knowledge
H E efficiency
Bypass blower efficiency
Quality of ICW.
Speed of R/C Fan.
Proper sealing
Of I/C flange
& Base flange
Servo flapper
Opening of
bypass
Water
servo
opening
Stacking rule
Start bypass gas
Tempr.
Cooling water temp.
cooling water flow.
EXCESS
BYPASS
COOLING
TIME.Quality of
degd. coils
Coil
chemistry
Climatic
effect
ICW quality
22. Example of scatter plot
Study
Hours
Test
Score
3 80
5 90
2 75
6 80
7 90
1 50
2 65
7 85
1 40
7 100
The data displayed on the graph resembles a line rising from left to right. Since the
slope of the line is positive, there is a positive correlation between the two sets of
data. This means that according to this set of data, the longer one studies, the better
grade he will get in examination.
23. Types of scatter plots
Scatter Plot: No Relationship
Scatter Plot: Strong Linear
(positive correlation) Relationship
24. Scatter Plot: Strong Linear
(negative correlation) Relationship
Scatter Plot: Exact Linear (positive
correlation) Relationship
Types of scatter plots
27. All control charts have three basic components:
•a centerline, usually the mathematical average of all the samples plotted.
•upper and lower statistical control limits that define the constraints of common
cause variations.
•performance data plotted over time.
28. Elements of Typical Control Chart
1. Horizontal axis for sample number
2. Vertical axis for sample statistics e.g.
mean, range, standard deviation of sample.
3. Target Line
4. Upper control line
5. Upper warning line
6. Lower control line
7. Lower warning line
8. Plotting of sample statistics
9. Line connecting the plotted statistics
29. Elements of Typical Control Chart
1 2 3 4 5
Sample Number
Upper control line
Target
Lower control line
Upper warning line
Lower warning line
SampleStatistics
31. Types of Control Chart
We have two main types of control charts. One for
variable data and the other for attribute data.
Since now world-wide, the current operating level is
‘number of parts defective per million parts
produced’, aptly described as ‘PPM’; control charts
for ‘attribute data’ has no meaning. The reason
being that the sample size for maintaining control
chart at the ‘PPM’ level, is very large, perhaps
equal to lot size, that means 100% inspection.
32. Most Commonly Used Variable Control Charts
Following are the most commonly used
variable control charts:
To track the accuracy of the process
- Mean control chart or x-bar chart
To track the precision of the process
- Range control chart
33. Most Common Type of Control Chart for
Variable Data
Variable
Control
Chart
For tracking
Accuracy
For tracking
Precision
Mean
control chart
Range
control chart
35. Case When Process Mean is at Target
43 48 49 50 51 52 5344 45 46 47
Target Process
Mean
Chances of getting a reading beyond U & L is almost nil
42
U
L
- 3 s +3 s U - L = 6 s
36. Case - Small Shift of the Process Mean
43 48 49 50 51 52 5344 45 46 47
Target
Process
Mean
Chances of getting a reading outside U is small
Small shift in process
42
Shaded area
shows the
probability of
getting
a reading
beyond U
UL
U-L = 6 s
37. Process
Mean
Case - Large Shift of the Process Mean
43 48 49 50 51 52 5344 45 46 47
Target
Chances of getting a reading outside U is large
Large shift in process
42
Shaded area
shows the
probability of
getting
a reading
beyond U
UL
U-L = 6 s
38. Summary of Effect of Process Shift
When there is no shift in the process nearly all the
observations fall within -3 s and + 3 s.
When there is small shift in the mean of process
some observations fall outside original -3 s and
+3 s zone.
Chances of an observation falling outside original
-3 s and + 3 s zone increases with the increase
in the shift of process mean.
39. Our Conclusion from Normal Distribution
When an observation falls within original +3 s
and -3 s zone of mean of a process, we
conclude that there is no shift in the mean of
process. This is so because falling of an
observation between these limits is a chance.
When an observation falls beyond original +3 s
and -3 s zone of process mean, we conclude
that there is shift in location of the process
40. If the process output follows these rules, the
process is said to be stable or in control with
only common causes of variation present. If it
fails to follow these rules, it may be out of
control with special causes of variation present.
These special causes must be found and
corrected.
Interpreting Control Chart
41. Interpreting Control Chart
A single point above or below the control limits.
Probability of a point falling outside the control limit
is less than 0.14%. This pattern may indicate:
- a special cause of variation from a material,
equipment, method, operator etc.
- mismeasurement of a part or parts.
- miscalculated or misplotted data point.
43. Interpreting Control Chart
Seven consecutive points are falling on one side
of the centre line.
Probability of a point falling above or below the
centre line is 50-50. The probability of seven
consecutive points falling on one side of the
centre line is 0.78% ( 1 in 128)
This pattern indicates a shift in the process
output from changes in the equipment, methods,
or material or shift in the measurement system.
44. Interpreting Control Chart
UCLUCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWLUWL
LCLLCL
TargetTarget
LWLLWL
Seven consecutive points on one
side of the centre line
45. Interpreting Control Chart
Two consecutive points fall between warning limit and
corresponding control limit.
In a normal distribution, the probability of two consecutive
points falling between warning limit and corresponding
control limit is 0.05%
(1 in 2000).
This could be due to large shift in the process, equipment,
material, method or
measurement system.
46. Interpreting Control Chart
UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
Target
LWL
Two consecutive points between warning limit and
corresponding control limit
47. Interpreting Control Chart
Two points out of three consecutive points fall
between warning limit and corresponding control
limit.
This could be due to large shift in the process,
equipment, material, method or measurement
system.
48. UCLUCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWLUWL
LCLLCL
TargetTarget
LWLLWL
Two points out of three consecutive points
between warning limit and corresponding
control limit
Interpreting Control Chart
49. A trend of seven points in a row upward or
downward demonstrates nonrandomness.
This happens when
- Gradual deterioration or wear in
equipment.
- Improvement or deterioration in
technique.
- Operator fatigue.
Interpreting Control Chart
50. UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
Target
LWL
Seven consecutive points having
upward trend
Interpreting Control Chart
51. UCLUCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCLLCL
Target
LWL
Seven consecutive points having
downward trend
Interpreting Control Chart
