California State University, Northridge MSE 617 Quality Assurance & Management PROF.LUIS ECHEVERRIA PROJECT QUALITY TREND ANALYSIS Team F Dhruv, Rijul Habibi, Shayaan Kalantari, Mahram Singh, Ravinder Al-Doukhi, Murtadha Submission Date: 30th April 2012
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California State University, Northridge
MSE 617
Quality Assurance & Management
PROF.LUIS ECHEVERRIA
PROJECT
QUALITY TREND ANALYSIS
Team F
Dhruv, Rijul
Habibi, Shayaan
Kalantari, Mahram
Singh, Ravinder
Al-Doukhi, Murtadha
Submission Date: 30th April 2012
Quality Trend Analysis
2
Table of contents:
INTRODUCTION………………………………………………………………………………...3
1.1 Manufacturing Flow…………………………………………………………………………...3
1.2 Guidelines to Trend Analysis……………………………………………….............................4
1.2.1 Data Selection……………………………………………………………………..4
1.2.2 Data Sorting……………………………………………………………………….4
1.2.3 Analysis based on Pareto charts…………………………………………………...4
1.2.4 Corrective Action plan…………………………………………………………….4
2.1 Important facts and Assumptions……………………………………………………………...5
3.1 Defect Trend Analysis…………………………………………………………………….......6
3.1.1 Total number of defects in team F workstations (internal)……………………….6
3.1.2 Charge Backs (External)…………………………………………………………..7
3.2 Defect Analysis on a monthly basis…………………………………………………………...7
3.3 Detailed Charge back analysis………………………………………………………………...9
3.4 Analysis based on Family of defects………………………………………………………...11
3.5 Analysis based on Unit Serial Number………………………………………………………13
3.6 Analysis based on Job number……………………………………………………………….15
3.7 Analysis based on groups…………………………………………………………………….16
4.1 SUMMARY………………………………………………………………………………….18
5.1 CORRECTIVE ACTION PLAN……………………………………………………………19
6.1 CONCLUSION………………………………………………………………………………20
Quality Trend Analysis
3
INTRODUCTION:
QUALITY TREND ANALYSIS refers to the concept of collecting and analyzing information
overtime in order to identify sources of high defects, interpret the performance of the process and
make decisions for the future. Although trend analysis is often used to predict future events, it
could be used to estimate uncertain events in the past.
In our project, we would be using the trend analysis steps to define and analyze defects occurring
in workstations of the team F in order to jot down the corrective action plan so as to avoid the
defects in the future using problem solving techniques such as PDCA. (Plan – Do – Check – Act)
The manufacturing flow here comprises of four teams A, B, C and Team F. The approach of the
project is to execute a period by period analysis for defects occurring at the various work centers
and develop a plan for corrective action.
Being a part of team F, our goal is to perform a detailed trend analysis for all our workstations.
Team F consists of four workstations F1, F2, F3 & F4. The time period for the analysis of data is
from January 2003 to March 2003.
1.1 Manufacturing Flow:
The Manufacturing flow for the entire process is shown in the figure given below. Team F
comprises of F1, F2, F3 & F4 workstations linked to each other such that work center F4
receives input from workstation A1 and C6 of Team A and C respectively and then delivers to
F3, which then delivers to F2. F2 receives input from workstation C5 from Team C and then
finally delivers it to F1. There are internal and external suppliers (V) to the manufacturing
process who provides products and services to the manufacturing flow at different points.
- Machine shop (Internal Supplier)
- Plant out of state (Internal Supplier)
- Paint shop (Internal Supplier)
- External Suppliers
Quality Trend Analysis
4
1.2 Guidelines to Trend Analysis:
1.2.1 Data Selection:
Selection of data based on the period of analysis is taken into consideration. Our main area of
focus in identifying all the defects pertaining to team F contains data selected from Jan 2003 to
March 2003.
1.2.2 Data Sorting:
Once our data is selected we mainly focus our concentration in sorting the data based on the
workstation that is causing a majority of defects and affecting overall performance of team F.
This step is the most important in the entire analysis as the path ahead in analyzing the trends is
based on the selection of the workstation.
1.2.3 Analysis based on Pareto charts:
In order to perform trend analysis in the above step we use Pareto charts which are a great tool
for analyzing process trends.
1.2.4 Corrective Action plan:
After performing detailed analysis and getting results from the above steps, the entire team
would get together for brainstorming to analyze the results of charts and come up with a
corrective action plan to reducing the number of defects and to increase the overall efficiency of
Team F.
B3
C6
C5
B2 B1
C4
C3
C2
A3
C1
A2 F1A1 F2F3F4
Team B
Team C
Team A Team F
External Suppliers
(V)
Machine Shop
(MS)Plant out of State
(OR)
Paint shop
(PS)
B4
Quality Trend Analysis
5
2.1 Important Facts & Assumptions:
1. The data under consideration is not subject to change.
2. The cost of executing the corrective action plan is not considered and therefore it has no
influence on the current conclusion.
3. Type N/A in the charge back column pertains to the defects in the same workstation.
Quality Trend Analysis
6
3.1 Defect Trend Analysis
As a part of our analysis, we are responsible to analyze all the defects identified by team F as
well the ones charged back from teams A, B & C. The data taken into consideration includes the
entire information of each piece of manufacturing from Jan 2003 – March 2003. After our
calculations we find out that the total number of defects identified by team F is 1525.
Further ahead we now need to breakdown the total number of defects in our team to its respective
workstations (F1, F2, F3, and F4).This will help us identify the work center that is causing the maximum
defects and affecting the overall teams. We would be using the Pareto charts for identifying the trends of
the defect within each of the work center.
3.1.1 Total number of defects in team F workstations (internal):
Workstation Total defects % % cumulative
F1 1078 71% 71%
F2 416 27% 98%
F3 10 1% 99%
F4 21 1% 100%
Total 1525 100%
From the above pareto chart we can conclude that within team F, Workstation F1 has the
maximum number of defects totaling to 1078 which is 71% of the overall defects in team F.
1078
416
10 21
0%
20%
40%
60%
80%
100%
120%
0
200
400
600
800
1000
1200
F1 F2 F3 F4
TOTAL DEFECTS
% CUMULATIVE
Team F Defects/Workstation
Quality Trend Analysis
7
3.1.2 Charge backs (External):
Workstation Defects Charged back (external)
Total
F1 1078 1 1079
F2 416 12 428
F3 10 7 17
F4 21 0 21
Total 1525 20 1545
The total numbers of defects observed include charge backs from workstations A1 and C6
(external defects) which are very less in our case. Maximum defects were found at the
workstation F1 with charge back N/A (internal defects). Hence, our entire concentration would
be to work on workstation F1 and plan corrective action to reduce the defects. Once our action
plan has reduced the defects in Workstation F1, then other workstations can be considered.
However they are not a part of our current analysis.
Knowing workstation F1 being the major contributor of defects, we breakdown the entire data of
workstation F1 to strategize our corrective action plan.
3.2 Defect Analysis on a monthly basis:
Months Work Station F1
Workstation F2
Workstation F3
Workstation F4 Total F
JAN 314 212 4 4 534
FEB 370 122 3 4 499
MARCH 394 82 3 13 492
TOTAL 1078 416 10 21 1525
Quality Trend Analysis
8
From the above chart, we observed that workstation F1 contributes to the maximum number of
defects in the month of March 2003.
The above chart shows the monthly trend analysis. We know that workstation F1 is the one with
maximum defects, and is the one which has been contributing to maximum defects in each
month (Jan 2003 – March 2003). However, the numbers of defects observed are minimum in the
month of March 2003 and maximum in Jan 2003. After many brainstorming sessions with the
entire team, a conclusion was drawn in order for us to focus on one particular month to work on.
The month of March 2003 was considered. The outcome of this decision is explained in further
details in the summary. Henceforth, our detailed analysis and breakdown of data will consider
overall results as well as the data from the month of March 2003.
314
370 394
212
122
82
4 3 3 4 4 13
0
50
100
150
200
250
300
350
400
450
JAN FEB MARCH
Work Station F1
Workstation F2
Workstation F3
Workstation F4
534 499 492
0
100
200
300
400
500
600
JAN FEB MARCH
Total F
Monthly breakdown of defects
Overall defects/Month from all
workstations
Quality Trend Analysis
9
3.3 Detailed Charge back Analysis:
We would now perform a detailed analysis of the Charge backs on the workstation F1 to get an
insight on the origin of the defects, which would help us plan for the corrective action plan.
CHARGE BACK JAN FEB MARCH Total % cumulative
F1(N/A) 69 54 106 229 21.24
F2 35 75 86 196 39.42
PS 29 97 28 154 53.71
V 66 8 63 137 66.42
A1 37 24 12 73 73.19
C1 7 36 10 53 78.11
B1 18 16 15 49 82.65
C6 9 9 24 42 86.55
A2 20 2 13 35 89.80
DS 0 11 15 26 92.21
MS 3 20 2 25 94.53
C2 14 3 3 20 96.38
T 1 7 1 9 97.22
F3 0 1 6 7 97.87
B2 1 0 5 6 98.42
C3 2 3 1 6 98.98
A3 2 2 1 5 99.44
B3 1 1 1 3 99.72
C4 0 0 1 1 99.81
PD 0 0 1 1 99.91
F4 0 1 0 1 100.00
TOTAL 314 370 394 1078
Quality Trend Analysis
10
From the above pareto charts we can conclude that Workstation F1 has the maximum of internal
defects N/A that were identified within the team. External defects that were charged back to F1
are negligible. Based on the above result, we breakdown the charge backs for the month of
March.
0
20
40
60
80
100
120F1
(N/A
)
F2 PS V
A1
C1
B1
C6
A2
DS
MS
C2 T F3 B2
C3
A3
B3
C4
PD F4
JAN
FEB
MARCH
0.00
20.00
40.00
60.00
80.00
100.00
120.00
0
50
100
150
200
250
F1(N
/A)
F2 PS V
A1
C1
B1
C6
A2
DS
MS
C2 T F3 B2
C3
A3
B3
C4
PD F4
Total
% cumulative
Monthly charge back analysis by F
Overall charge back – F1
Quality Trend Analysis
11
From the above chart, we found out that internal defects are higher for the month of March too.
This makes it clear that we need to concentrate extensively on defects found in F1 which further
strengthens our decision towards accurate corrective action plan.
3.4 Analysis based on Family of defects:
We further breakdown the data of workstation F1 based on family of defects.
DEFECT CODE JAN FEB MARCH DEFECTS % cumulative
B 98 86 116 300 27.80
E 66 87 61 214 47.64
H 19 30 64 113 58.11
K 10 47 56 113 68.58
C 11 65 22 98 77.66
D 21 23 29 73 84.43
R 60 0 0 60 89.99
X 6 1 23 30 92.77
J 8 5 11 24 95.00
F 2 19 2 23 97.13
M 8 7 5 20 98.98
A 5 0 6 11 100.00
TOTAL 314 370 395 1079
106
86
28
63
12 10 15
24 13 15
2 3 1 6 5 1 1 1 1 1 0
0
20
40
60
80
100
120F1
(N/A
)
F2 PS V
A1
C1
B1
C6
A2
DS
MS
C2 T F3 B2
C3
A3
B3
C4
PD F4
Charge back - March
MARCH
Quality Trend Analysis
12
Based on the above chart, the family of defect B is causing the maximum number of defects viz.
300 in workstation F1. We would now breakdown the family of defect code B to find out which
family code is a major contributor overall as well as in the month of March.
Defect Code Jan Feb March Total % Cumulative
B05 22 18 65 105 35%
B02 17 30 27 74 60%
B01 23 0 14 37 72%
B08 2 23 1 26 81%
B10 9 8 4 21 88%
B04 12 0 0 12 92%
B06 3 5 1 9 95%
B07 5 2 2 9 98%
B03 5 0 2 7 100%
Total 98 86 116 300
300
214
113 113 98
73 60
29 24 23 20 11
0.00
20.00
40.00
60.00
80.00
100.00
120.00
0
50
100
150
200
250
300
350
B E H K C D R X J F M A
DEFECTS
% cumulative
Overall family of defects – F1
Quality Trend Analysis
13
The above chart signifies the contribution of B05 family defect code responsible to cause the
maximum defects overall. It is also the highest in the month of March.
3.5 Analysis based on Unit serial Number:
UNIT S/N
JAN FEB MARCH TOTAL DEFECTS
% Cumulative
165 0 42 133 175 16.23
277 0 56 103 159 30.98
272 65 49 0 114 41.56
275 27 60 27 114 52.13
166 0 22 88 110 62.34
274 15 80 0 95 71.15
65
0
10
20
30
40
50
60
70
B05 B02 B01 B08 B10 B04 B06 B07 B03
March
Jan
Feb
105
74
37
26 21
12 9 9 7
0%
20%
40%
60%
80%
100%
120%
0
20
40
60
80
100
120
B05 B02 B01 B08 B10 B04 B06 B07 B03
Total
% Cumulative
Quality Trend Analysis
14
164 28 61 0 89 79.41
162 83 0 0 83 87.11
163 55 0 0 55 92.21
276 41 0 0 41 96.01
167 0 0 22 22 98.05
278 0 0 13 13 99.26
168 0 0 7 7 99.91
279 0 0 1 1 100.00
TOTAL 314 370 394 1078
From the above charts we conclude that unit number 165 not only has maximum number of
defects overall but also during the 3 month period (Jan 2003-March 2003) with the highest in
March 2003.
133
103
0
27
88
0 0 0 0 0
22 13
7 1
42
56 49
60
22
80
61
0 0 0 0 0 0 0 0 0
65
27
0
15
28
83
55
41
0 0 0 0 0
20
40
60
80
100
120
140
165 277 272 275 166 274 164 162 163 276 167 278 168 279
MARCH
FEB
JAN
175
159
114 114 110 95 89 83
55 41
22 13 7 1
0.00
20.00
40.00
60.00
80.00
100.00
120.00
0
20
40
60
80
100
120
140
160
180
200
165 277 272 275 166 274 164 162 163 276 167 278 168 279
TOTAL DEFECTS
% Cumulative
Monthly Unit S/N – F1
Overall Unit S/N – F1
Quality Trend Analysis
15
3.6 Analysis based on Job number:
Workstation F1 is further analyzed to find which job number results in maximum number of
defects.
JOB NO.
JAN FEB MARCH TOTAL DEFECTS
% Cumulative
F1-07 42 80 12 134 12.47%
F1-10 40 44 35 119 23.53%
F1-19 71 18 15 104 33.21%
F1-13 23 34 29 86 41.21%
F1-18 7 18 59 84 49.02%
F1-14 49 16 13 78 56.28%
F1-20 9 7 39 55 61.40%
F1-12 22 18 5 45 65.58%
F1-11 12 14 18 44 69.67%
F1-01 1 9 33 43 73.67%
F1-09 15 13 12 40 77.40%
F1-08 5 20 11 36 80.74%
F1-05 1 8 21 30 83.53%
F2-02 4 8 15 27 86.05%
F1-06 5 13 7 25 88.37%
F2-01 0 22 0 22 90.42%
F1-03 2 4 13 19 92.19%
F1-17 0 5 7 12 93.30%
F1-02 1 5 4 10 94.23%
F2-13 0 0 8 8 94.98%
F2-17 2 0 6 8 95.72%
F1-04 0 2 5 7 96.37%
F2-09 0 0 7 7 97.02%
F2-16 0 0 7 7 97.67%
F1-15 1 4 1 6 98.23%
F1-16 2 3 1 6 98.79%
F2-04 0 0 4 4 99.16%
F2-07 0 0 3 3 99.44%
F2-05 0 0 2 2 99.63%
F2-10 0 2 0 2 99.81%
F2-19 0 0 1 1 99.91%
F4-02 0 0 1 1 100.00%
TOTAL 314 367 394 1075
Quality Trend Analysis
16
From the above pareto chart, it is seen that working towards Job number F1-07 will help us
reduce the maximum number of defects.
3.7 Analysis based on Groups:
Group Jan Feb March Total defects % cumulative
Operations 243 324 312 879 81.54
External supplier 66 8 63 137 94.25
Engineering 1 18 16 35 97.50
Internal supplier 4 20 3 27 100.00
Total 314 370 394 1078
134
119
104
86 84 78
55
45 44 43 40 36
30 27 25 22 19 12 10 8 8 7 7 7 6 6 4 3 2 2 1 1
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
0
20
40
60
80
100
120
140
160
TOTAL DEFECTS
% Cumulative
Overall analysis of Job number – F1
Quality Trend Analysis
17
From the above Pareto analysis we can conclude that the Operations Group is the highest
potential root cause of total number of defects in the workstation F1 during the period of Jan
2003 – March 2003.
This is the final breakdown of our data from Jan 2003-March 2003 for our defect trend analysis.
Based on our analysis, a proper corrective action plan needs to be planned and executed in order
to reduce the overall defects.
243
66
1 4
324
8 18 20
312
63
16 3
0
50
100
150
200
250
300
350
OPERATIONS EXTERNAL SUPPLIER ENGINEERING INTERNAL SUPPLIER
JAN
FEB
MARCH
879
137
35 27
0.00
20.00
40.00
60.00
80.00
100.00
120.00
0
100
200
300
400
500
600
700
800
900
1000
OPERATIONS EXTERNALSUPPLIER
ENGINEERING INTERNALSUPPLIER
TOTAL DEFECTS
% cumulative
Monthly defect based on groups – F1
Overall defects/group – F1
Quality Trend Analysis
18
4.1 SUMMARY:
A brief summary of our analysis followed by C/A plan:
- Team F constitutes of 1525 defects out of which 1078 are contributed by workstation F1.
Majority of them are kicked in through internal defects. Hence our C/A plan would be mainly
concentrated towards the workstation F1 as well as charge backs to N/A.
- By analyzing monthly trends, maximum numbers of defects were caused in Jan 2003.
However, F1 contributes 80% of defects (394/492) in March 2003 as compared to 59% of
defects (314/534) in Jan 2003. Therefore, team decided to concentrate on March 2003 data.
- Family defect code B was found out to cause the maximum number of overall defects.
Further breaking down the defect code B resulted in family defect code B05 to cause the
maximum number of defects overall. It is also the highest in March 2003.
- Unit S/N 165 accounted to maximum number of defects when the data was broken down
even more.
- On performing a more detailed analysis on the sorted data, we know that the maximum
numbers of defects were caused while performing the job number F1-07. Hence we would
focus on analyzing the respective job number to improve it and to reduce the defects within
F1.
- Further to the analysis, we found out that the Operations group is the one that is causing the
maximum defects. Hence improvement is needed on that group.
Therefore, Focusing on workstation F1 especially for the month of March 2003 with charge
backs N/A (internal defects), family defect code B05, Unit S/N 165, Job No. F1-07 and
group of Operations, Team F would be able to reduce the maximum number of defects.
Quality Trend Analysis
19
5.1 CORRECTIVE ACTION PLAN:
The corrective action plan will be done using the Plan-Do-Check-Act problem solving
technique. Based on the trend analyses, areas of concern are recommended for process
improvement.
1. Since F1 is the one that is causing maximum defects, a thorough inspection team should
be at place to keep internal defects at the minimal. This is very important for team F since
it is the last team in the manufacturing flow and the final output of production maybe
input to customers in the form of finished goods.
2. Placing a very strong quality team (quality engineers, inspectors, etc.) to strengthen the
quality process to achieve ‘zero defects’
3. Pilot test the model to identify any technical issues and make changes as necessary in
order to reduce defects generating from family code B05.
4. Hold brainstorming sessions and organize team meetings with all the members that were
responsible for Job no. F1-07 as maximum defects had occurred while performing the
job.
5. Analyze and refine current tools and processes and ensure that equipments are up to date
with proper calibration thereby reducing machine wear and maintenance issues.
6. Employee training and seminars to be provided to refresh quality control standards and
deliver high quality products.
7. An improved quality plan in place which includes any process, procedure or system
changes requirement and any monitors or controls necessary to prevent recurrence of the
problem.
Once the above corrective actions are implemented to improve the efficiency of team F, we
would continuously monitor the process to make sure there is continuous improvement
according to the PDCA cycle.
Quality Trend Analysis
20
6.1 CONCLUSION:
After reviewing the entire process we conclude that the workstation F1 needs the most
improvement due to high number of defects. The above corrective action plan will improve each
area assessed in workstation F1. Similarly, other workstations like F2, F3 & F4 would be
considered to develop corrective action plan (especially F2 which is the second most
contributing defect workstation) to improve overall efficiency and quality performance of team
F. However, that is not a part of our current analysis.
With potential utilization of all the statistical tools and problem solving techniques we believe
that the ideal quality aim of ‘zero defects’ can be achieved.
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