Business Process Improvement Training Document
7 QC Tools for Problem solving: A nut manufacturing company case
study
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Business Process Improvement Training Document
Introduction to 7 QC Tools
7 QC Tools, also known as 7 Quality Control tools, is a set of graphical techniques
that can be used by an individual who has limited understanding of statistical
principles and who doesn’t wish to undergo a formal statistics training.
The 7 Tools are:
1.
2.
3.
4.
5.
6.
7.
Cause and Effect Diagram
Check Sheet
Control Chart
Histogram
Pareto Chart
Scatter Diagram
Flow chart
Uses of 7 QC Tools
Each of these tools are powerful in their own regard and can be used standalone
depending on the situations of use. When used in context of a problem solving
environment, the 7 QC Tools are arranged in the order of use as:
1.
2.
3.
4.
5.
6.
7.
Flowchart
Histogram
Check sheet
Cause and Effect Diagram
Pareto Chart
Scatter Chart
Control Chart
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Business Process Improvement Training Document
7 QC tools – A Case Study
In December 2013, as part of a consulting project, the Head Trainer and
Consultant of The School of Continuous Improvement used 7 QC Tools to solve a
problem of “High % of defectives in nuts” for a manufacturing company.
This case study highlights how the use of the quality tools enabled quicker business
decision making.
The problem
The company had to make nuts in the specifications provided as 3 mm and 5 mm
as diameter of the nuts. These specifications were provided by the customer. In
the last 1 year, the company had begun making a lot of defectives. The measured
rate of defectives was at about 15% versus the last year comparison of 8%.
The company had suffered a loss of $250,000 in the last 1 year due to these
defectives accounting to a revenue loss of 30% and about 10% clients left the
company to choose another competitor.
Where is the problem?
The starting point for the consultant was to locate the problem. He used a macrolevel flow to understand the various steps in the process.
The consultant mapped the process in a macro-view to gain understanding of the
major sub-process contributing to the state of defectives.
Customer
Supplier
Heat treatment
Molding and
finishing
Shape dimensioning
and packing
The consultant then collected data on defects and reworks through each of the
stages to understand the problem closer. He found that for the sample data, the
Heat treatment process suffered the most with an FTY of 65%, while Molding
and Finishing had yields of 100% each.
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What is contributing to the problem?
Next, the consultant wanted to know the possible causes to the problem. He
brainstormed with a team of process experts that included the process owner and
logically categorized the causes in the Cause and Effect Diagram.
The Cause and Effect Diagram presented a nice view of what causes contribute to
the high percentage of defectives in the Heat Treatment process.
Should there be a data collection mechanism?
The consultant on speaking to the process experts found out that data was not
available for quite a few of the causes. For example, the company didn’t track
which raw materials were they using. The company bought its raw materials from 3
different suppliers and it was important for the consultant to determine if raw
material quality was the cause of fluctuation.
The consultant designed a simple check sheet for this purpose and asked the
process team members to record their data for every nut inspected.
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Business Process Improvement Training Document
A sample check-sheet format has been attached with the same format being used
by the consultant.
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What is the major source of the problem?
The consultant was getting closer to solving the problem of defective nuts. He
used the check sheet to collect data on various problems and with the help of the
check sheet, he was also able to generate a frequency list of the issues that causes
the defects.
He needed a tool to prioritize the defects. He decided to use the Pareto Charts
due to its 80-20 prioritization rule.
The consultant used Minitab as the application to generate the Pareto Charts with
the output as shown below:
100
150
80
60
100
40
50
Issues
0
20
l
l
l
t
r ia
r ia
r ia
en
r
ate
ate
ate
ipm
o
u
f
m
m
m
e
eq
ith
aw
aw
eb
fr
fr
old
ag
gw
o
o
r
n
m
i
s
to
th
rk
es
l in
fs
ng
wo
ria
r dn
f
so
tre
e
f
a
y
s
t
H
ta
Da
ile
ma
fs
ns
i ng
eo
Te
c
d
n
fee
rie
of
pe
e
Ex
Ti m
Frequency
Percent
Cum %
se
eu
104
52.8
52.8
54
27.4
80.2
12
6.1
86.3
10
5.1
91.4
10
5.1
96.4
r
he
Ot
Percent
Frequency
Pareto Chart of Issues
200
0
7
3.6
100.0
The Pareto charts clearly inferred some points:
1. 80% of all defectives due to the Heat treatment process were due to
a. Days of raw material storage before use = 52%
b. Tensile strength of raw material was less = 27%
2. The practitioner knew that if he could work on these two problems, he
could take care of 80% of the defectives.
3. He had a limitation though --- Pareto chart is a qualitative tool although we
collected data as statistical correlation between defects in a nut and the
issues identified need to be validated.
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Business Process Improvement Training Document
Validation of the most obvious
The consultant needed a tool that shows to him graphically the effect of type of
suppliers on diameters of the nuts. Some of the graphical tools at his disposal
were:
a.
b.
c.
d.
e.
Dot Plots
Box Plots
Matrix Plots
Individual Value Plot
Histogram
The consultant decided to use Histogram due to its power to clearly identify if the
data was skewed due to a particular factor, and if the data had multiple modes.
Histogram of Diameter
Normal
Mean
StDev
N
3.0
3.3
0.5444
12
Frequency
2.5
2.0
1.5
1.0
0.5
0.0
2.0
2.5
3.0
3.5
Diameter
4.0
4.5
The histogram plotted for all the diameter observations recorded across the 3
suppliers didn’t give the accurate picture. To make ensure, the consultant
stratified and drew a Histogram again.
Histogram of Diameter
Supplier
A
B
C
2.0
Frequency
1.5
1.0
0.5
0.0
2.50
2.75
3.00
3.25
Diameter
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3.50
3.75
4.00
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Business Process Improvement Training Document
This histogram gave a clear picture to the consultant and he was able to infer that:
1. Supplier A’s raw material quality had been consistently poor. The
management needs to clarify their expectations on raw material from
Supplier A and also invoke a penalty clause in their contract.
2. Supplier B consistently delivers raw material of good quality and the
company should purchase raw materials from them, provided they are not
cost inhibitive.
3. Supplier C meets the necessary standards but not consistently.
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Statistical correlation
The consultant wanted to understand the impact days of inventory had on the
diameter of the nut. He wanted to know if “By storing the raw materials for X
days, does quality of the nut get impacted?”
The consultant decided to use “Scatter Charts”, a tool well known to show
statistical correlation when modified to work as a regression tool.
The consultant plotted the Scatter Chart, which is shown below:
Diameter
6
y = 0.7326x + 1.2157
R² = 0.9468
5
4
Diameter
3
Linear (Diameter)
2
1
0
0
2
4
6
8
The inferences the consultant could draw out of the scatter chart are:
1. 94.7% of variability in diameter of nuts is due to the fact that the raw
material is stored.
2. Statistically, the relationship between Days in Inventory and Diameter of the
Nuts has been validated.
3. The consultant did GEMBA walks and correlated the causality factor.
4. Finally, the consultant used the equation, y = 0.732x + 1.2157 to arrive at a
decision that in order to attain a target of 4 mm in diameter, the raw
materials must be moved out of the inventory within 4 days of receipt.
The consultant outlined the below steps and executed them as mentioned:
1.
2.
3.
4.
Meet with the process expert and discuss plan of action.
Pilot the plan of action
Validate the plan of action
Validate improvements
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Statistically control the improvements
After establishing confidence in the executed solutions, the consultant discussed
with the CEO on the need of sustaining the improvement initiative. The consultant
decided to use Control Charts to establish statistical control on two variables:
1. Days in Inventory
2. Diameter of nuts
The consultant established rational subgroups and calculated a statistically
significant sample size of 3 per subgroup. Data was collected for 10 days and
control charts were plotted as below:
Xbar-R Chart of Days in Inventory
U C L=4.100
Sample Mean
4.00
3.75
_
_
X=3.495
3.50
3.25
3.00
LC L=2.890
1
2
3
4
5
6
7
8
9
10
Sa mple
Sample Range
1.6
U C L=1.522
1.2
0.8
_
R=0.591
0.4
0.0
LC L=0
1
2
3
4
5
6
7
8
9
10
Sa mple
The consultant established that the control charts showed that Days in Inventory
was in control. He went on to plot the Control chart for diameter in nuts shown
below:
Xbar-R Chart of Diameter of Nuts
Sample Mean
4.6
U C L=4.6000
4.4
_
_
X=4.1646
4.2
4.0
3.8
LC L=3.7292
1
2
3
4
5
6
7
8
9
10
Sa mple
U C L=1.096
Sample Range
1.00
0.75
_
R=0.426
0.50
0.25
0.00
LC L=0
1
2
3
4
5
6
7
8
9
10
Sa mple
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Business Process Improvement Training Document
Closing Actions
With the consultant determining that the process was in control, he instituted the
below mechanisms for a long-term sustenance of the improvement actions:
1. Instituted Supplier end quality check of raw materials.
2. Integrated Days in Inventory tracking with ERP mechanism.
3. Empowered line workers to report faults during production and correct.
Operational and Financial Benefits




% defectives decreased from 15% to 2.5% on nut sales.
External Failure Cost reduced from 35% to 4.7%.
Appraisal Cost increased from 2% to 15%.
Estimated annual financial benefits = $310,000
Consultant profile
The consultant for this 1-month project was C. Vishwanathan. He is a Lean Six
Sigma Master Black Belt and has led/ mentored/ implemented 17 business process
improvement initiatives across various sectors in the world. Additionally, he is a
Business process improvement trainer having conducted 82 batches in the niche of
business process improvement.
Write to [email protected] to speak about your business process
improvement need.
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