INTELLIGENCE QUALITY
LEAN SIX
SIGMA
GREEN BELT
Work Book
Lean Six Sigma Green Belt Online Workbook
Name
: ___________________________
Batch
: ___________________________
Organization : ___________________________
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Lean Six Sigma Green Belt Online Workbook
What is required for an organization to be successful?

__________________________________________________

__________________________________________________

__________________________________________________

__________________________________________________

__________________________________________________
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Lean Six Sigma Green Belt Online Workbook
Y = f (Xn) Equation Exercise
Identify the various dependent and independent variables for: ______________
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Lean Six Sigma Green Belt Online Workbook
Which Pond is Safe to Cross?
A) Pond A
B) Pond B
C) Both
D) Unable to decide
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Lean Six Sigma Green Belt Online Workbook
Standard Deviation Calculation
Σ = summation sign
x = individual observations.
xbar = mean of the observations
n = total number of observations
Pond A
x
x-xbar
(x-xbar)2
2
2 – 4 = -2
4
4
4–4=0
0
4
4–4=0
0
5
5–4=1
1
5
5–4=1
1
4
4–4=0
0
Sum
24
Average
4
n
6
6
Standard Deviation = √6 / 6 = √1 = 1
Pond B
x
x-xbar
(x-xbar)2
2
3
4
7
4
Sum
Average
n
Standard Deviation =
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Lean Six Sigma Green Belt Online Workbook
DPMO Calculation Exercise
Let’s inspect the quality of the GEMS Chocolate.
Calculate the DPMO and Sigma value for the sample that we have
collected from the market.
Please take out the GEMS Chocolate packet given to you.
Step 1 :
Identify & Finalise the parameters to inspect
Parameters – Colour, Crack, Shape & Taste
Step 2 :
Inspect each gems in the packet based on the parameters
Step 3 :
Identify the number of defects in gems for each parameter
Step 4 :
Document the findings from your sample in the below table
No. of GEMS chocolates
inside the packet
(Count)
Step 5 :
No. of
Crack
defects
No. of
Colour
defects
No. of
Shape
defects
No. of Taste
Defects
Summarize the findings from the samples of all the participants. Calculate
Defects Per Million Opportunities (DPMO) & Sigma Value
Total Samples
𝐷𝑃𝑀𝑂 =
Total Defects
No. of Opportunities
𝑁𝑜. 𝑜𝑓 𝑑𝑒𝑓𝑒𝑐𝑡𝑠 × 106
(𝑁𝑜. 𝑜𝑓 𝑈𝑛𝑖𝑡𝑠) × (𝑁𝑜. 𝑜𝑓 𝑜𝑝𝑝𝑜𝑟𝑡𝑢𝑛𝑖𝑡𝑖𝑒𝑠)
DPMO =
Sigma Value =
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Lean Six Sigma Green Belt Online Workbook
Sigma Conversion Table
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Lean Six Sigma Green Belt Online Workbook
Kano Model Exercise
Let’s create a Kano Model for: ____________________________________
Identify the Must be, More is Better and WOW factors
Must be (Expected Attributes)
More is better (Performance Attributes)
WOW (Exciting Attributes)
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Lean Six Sigma Green Belt Online Workbook
VOC / VOB to CTQ Exercise
Identify the CTQ for the VOC / VOB given in the table below.
S.No.
VOC / VOB
CTQs
1
“I want the pizza delivered hot”
2
“It takes too long to get the complaint
resolved”
3
“You shipped the product to the
wrong address”
4
"We aren’t able to process
transactions within the time promised
to the customer"
5
"Loan application forms submitted by
loan officers have too many errors"
6
"I had to wait for so long to get an
operator to answer my query"
7
"We haven’t improved our market
share"
8
"Last year, we spent a lot of money to
fix products in warranty"
9
"Time taken to vaccinate the
employees is more than expected"
10
"Significant number of parts has been
rejected in the Final inspection"
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Lean Six Sigma Green Belt Online Workbook
Project Charter Template
Project Title
Business Case
Problem Statement
Goal Statement
Project Scope
Project Team
Project Sponsor
Project Lead
Team Members
Project Schedule
Key Milestones
Start Date
End Date
Project Benefits
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Lean Six Sigma Green Belt Online Workbook
SIPOC Template
Project Title:
Process:
SUPPLIERS
Who are the suppliers of
INPUT
Key inputs required for the
PROCESS
Show the high level process steps
OUTPUT
Key outputs from the
CUSTOMER
Customers who receive the
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Step 8
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Lean Six Sigma Green Belt Online Workbook
Type of Data (Continuous / Discrete)
Identify the type of data for below metrics
S.No
Metrics
1
Diameter of Piston
2
No. of units of Product B sold in a Year
3
No. of Tubes rejected by Go- Nogo Gauge
4
5
6
Type of Data
Out of 100 sheets the numbers that meet the
thickness ( 4  0.9 )
Time taken to process a purchase order
37.81% of your customers are between the
ages of 66 and 70
7
The rejection rate for this process in 10%
8
Population of Bangalore City
9
Life expectancy
10
Runs scored by Virat Kohli
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Lean Six Sigma Green Belt Online Workbook
Data Collection Exercise
Objective: Measure how long a person can hold the breath
Measure: Breath Retention Time
Type of Data: Continuous Data
Frequency: Every Batch
Sampling: 3 Samples from each person
Instrument: Default Stopwatch App in the Mobile
Stratification Factors: Age, Weight, Work Experience, City, State
Data Collection Tool: Google Forms and Sheet
Operational Definition:

Breath In – Hold as far you can – Breath Out

Measure the time you hold your breath

Use the stopwatch in your mobile phone to measure the time
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Lean Six Sigma Green Belt Online Workbook
Types of Variation Exercise
Let’s do a small exercise to understand variation
Step 1 :
Take a piece of paper
Step 2 :
Write letter “a” with your dominant hand five times
Step 3 :
Write letter “a” with your other hand three times
Step 4 :
Write letter “a” with your dominant hand five times
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Lean Six Sigma Green Belt Online Workbook
Xbar – R Control Chart Exercise
The Practical Box Company makes corrugated boxes. As with any
process, there is variation in the strength of each box produced. Box
strength is measured by assessing stiffness. Stiffness is vital, low
stiffness causes the boxes to collapse, while high stiffness increases
production cost. Stiffness level data has been collected and entered
onto the control chart overleaf. A sample of 5 boxes has been assessed
each hour of production.
Use this data and the steps in the tool book to construct and average and range chart.
Step 1
Read and understand the data given in the control chart overleaf
Step 2
Calculate the Mean (Xbar) and Range (R) of the observations for each subgroup.
Complete this step on the control chart overleaf
Step 3a
Calculate the Grand Average – Centre Line for Xbar Chart
𝑋̿ =
∑𝑋̅
=
𝑘
No. of Subgroups (k) = 25
Step 3b
Calculate the Average Range - Centre Line for R Chart
𝑅̅ =
Step 4a
∑𝑅
=
𝑘
Calculate the Control Limits for the Xbar chart
𝑈𝐶𝐿𝑋̅ = 𝑋̿ + (𝐴2 × 𝑅̅ ) =
𝐿𝐶𝐿𝑋̅ = 𝑋̿ − (𝐴2 × 𝑅̅ ) =
Step 4b
Calculate the control limits for the R chart
𝑈𝐶𝐿𝑅 = 𝐷4 × 𝑅̅ =
𝐿𝐶𝐿𝑅 = 𝐷3 × 𝑅̅ =
Weighting Factors
Subgroup / Sample Size (n)
A2
5
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D3
D4
0
2.114
P a g e | 15
Lean Six Sigma Green Belt Online Workbook
̅ − 𝑹 𝑪𝑯𝑨𝑹𝑻
𝑿
Name:
Product / Service
Date
Time
Corrugated Boxes
Batch:
Process
Box Assembly
Specification Limits
05-Jan
06-Jan
Sample
Measurements
07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00
1
2
3
4
5
35
37
41
36
40
40
39
42
38
43
47
41
39
40
41
40
36
37
39
36
43
44
42
40
39
38
39
41
40
37
42
36
40
41
38
43
44
43
39
41
36
38
35
39
37
36
41
40
42
39
42
41
40
38
37
37
38
40
40
37
44
41
40
39
40
39
38
35
36
40
42
43
45
40
39
35
38
37
39
41
35
36
39
38
37
43
42
43
43
39
43
39
41
42
44
39
37
36
35
36
43
40
41
40
39
35
39
36
41
38
41
38
37
37
40
34
39
40
36
37
43
39
41
42
40
21
22
23
24
25
Mean (Xbar) 37.8 40.4 41.6 37.6 41.6 39.0 39.4 42.0 37.0 39.6 39.6 38.4 40.8 37.6 41.8 38.0 37.0 42.0 41.8 36.6
Range ( R )
6
5
8
4
̿=
𝑿
1
5
4
6
5
4
𝑼𝑪𝑳 =
2
3
4
5
6
6
5
3
5
5
6
6
4
4
5
4
18
19
20
𝑳𝑪𝑳 =
7
8
9
10
11
12
13
14
15
16
17
Xbar Chart
45
42.5
40
37.5
35
R Chart
̅=
𝑹
𝑼𝑪𝑳 =
𝑳𝑪𝑳 =
10
5
0
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Lean Six Sigma Green Belt Online Workbook
Table of Control Chart Constants
Control chart constants for X-bar, R, S, Individuals (called "X" or "I" charts), and MR (Moving
Range) Charts.
Subgroup
/ Sample
Size = n
A2
A3
d2
D3
D4
B3
B4
E2
2
1.880
2.659
1.128
0
3.267
0
3.267
2.66
3
1.023
1.954
1.693
0
2.574
0
2.568
1.772
4
0.729
1.628
2.059
0
2.282
0
2.266
1.457
5
0.577
1.427
2.326
0
2.114
0
2.089
1.29
6
0.483
1.287
2.534
0
2.004
0.030
1.970
1.184
7
0.419
1.182
2.704
0.076
1.924
0.118
1.882
1.109
8
0.373
1.099
2.847
0.136
1.864
0.185
1.815
1.054
9
0.337
1.032
2.970
0.184
1.816
0.239
1.761
1.01
10
0.308
0.975
3.078
0.223
1.777
0.284
1.716
0.975
11
0.285
0.927
3.173
0.256
1.744
0.321
1.679
12
0.266
0.886
3.258
0.283
1.717
0.354
1.646
13
0.249
0.850
3.336
0.307
1.693
0.382
1.618
14
0.235
0.817
3.407
0.328
1.672
0.406
1.594
15
0.223
0.789
3.472
0.347
1.653
0.428
1.572
16
0.212
0.763
3.532
0.363
1.637
0.448
1.552
17
0.203
0.739
3.588
0.378
1.622
0.466
1.534
18
0.194
0.718
3.640
0.391
1.608
0.482
1.518
19
0.187
0.698
3.689
0.403
1.597
0.497
1.503
20
0.180
0.680
3.735
0.415
1.585
0.510
1.490
21
0.173
0.663
3.778
0.425
1.575
0.523
1.477
22
0.167
0.647
3.819
0.434
1.566
0.534
1.466
23
0.162
0.633
3.858
0.443
1.557
0.545
1.455
24
0.157
0.619
3.895
0.451
1.548
0.555
1.445
25
0.153
0.606
3.931
0.459
1.541
0.565
1.435
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Lean Six Sigma Green Belt Online Workbook
Types of Control Chart
Type of Data
Count or
Classification
(Attribute Data)
Measurement
(Variable Data)
Count
Classification
Defects
Defectives
Subgroup Size of
1
I-MR
Fixed Sample Size
Variable Sample
Size
Fixed Sample Size
Variable Sample
Size
C Chart
U Chart
NP Chart
P Chart
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Subgroup Size < 8
- 10
Subgroup Size > 8
- 10
X-bar & R
X-bar & S
P a g e | 18
Lean Six Sigma Green Belt Online Workbook
Process Capability Exercise
Now that you have completed both an average and range chart and a histogram for ‘stiffness’
for the Practical Box company, it is necessary to assess the process against specifications. The
control chart showed a reasonably stable process and the histogram showed a normal
distribution. Your task is to calculate percent out of spec, Cp and Cpk and to describe how
well the process is performing. The relevant information from the control chart and the
specification limits are listed below. Use this information and the steps in the tool book to
complete the capability analysis for this process.
Subgroup size (n) = 5 (from control chart)
Grand average (𝑋̿) = 39.39 (from control chart)
Average Range (𝑅̅ ) = 4.96 (from control chart)
Upper specification limit (USL) = 42
Lower specification limit (LSL) = 30
39.39
Step 1
Calculate estimated standard deviation
𝜎̂ =
Step 2
𝑅̅
=
𝑑2
n
d2
4
2.059
5
2.326
6
2.534
Determine the location of the tails for the Process Range.
𝐿𝑒𝑓𝑡 𝑡𝑎𝑖𝑙 = 𝑋̿ − 3𝜎̂ =
𝑅𝑖𝑔ℎ𝑡 𝑡𝑎𝑖𝑙 = 𝑋̿ + 3𝜎̂ =
Step 3
Draw the Specification Limits and Process Range onto the Normal Distribution
diagram above.
Step 4
Calculate and interpret the capability indices. Complete each of the
calculations and explain what each result tells about the process
𝐶𝑝 =
USL − LSL
=
6𝜎̂
𝐶𝑝𝑢 =
USL − 𝑋̿
=
3𝜎̂
𝐶𝑝𝑙 =
𝑋̿ − 𝐿𝑆𝐿
=
3𝜎̂
𝐶𝑝𝑘 = min( 𝐶𝑝𝑢 , 𝐶𝑝𝑙 ) =
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Step 5
Calculate how much data is outside the specification.
𝑍𝑢𝑝𝑝𝑒𝑟 =
USL − 𝑋̿
=
𝜎̂
𝑍𝑙𝑜𝑤𝑒𝑟 =
𝑋̿ − 𝐿𝑆𝐿
=
𝜎̂
Look up the % out of specification in the standard normal distribution table
given in the next page.
Total % out of spec = % above + % below =
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Lean Six Sigma Green Belt Online Workbook
STATISTICAL TABLE: AREAS IN THE TAIL OF THE STANDARD NORMAL
DISTRIBUTION
Table entry for z is the area under the Standard Normal curve to
the right of z
z
0.00
0.01
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
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
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
0.5000
0.4602
0.4207
0.3821
0.3446
0.3085
0.2743
0.2420
0.2119
0.1841
0.1587
0.1357
0.1151
0.0968
0.0808
0.0668
0.0548
0.0446
0.0359
0.0287
0.0228
0.0179
0.0139
0.0107
0.0082
0.0062
0.0047
0.0035
0.0026
0.0019
0.0013
0.4960
0.4562
0.4168
0.3783
0.3409
0.3050
0.2709
0.2389
0.2090
0.1814
0.1562
0.1335
0.1131
0.0951
0.0793
0.0655
0.0537
0.0436
0.0351
0.0281
0.0222
0.0174
0.0136
0.0104
0.0080
0.0060
0.0045
0.0034
0.0025
0.0018
0.0013
0.00097
0.00069
0.02
0.03
0.04
0.05
0.4920
0.4522
0.4129
0.3745
0.3372
0.3015
0.2676
0.2358
0.2061
0.1788
0.1539
0.1314
0.1112
0.0934
0.0778
0.0643
0.0526
0.0427
0.0344
0.0274
0.0217
0.0170
0.0132
0.0102
0.0078
0.0059
0.0044
0.0033
0.0024
0.0018
0.0013
0.4880
0.4483
0.4090
0.3707
0.3336
0.2981
0.2643
0.2327
0.2033
0.1762
0.1515
0.1292
0.1093
0.0918
0.0764
0.0630
0.0516
0.0418
0.0336
0.0268
0.0212
0.0166
0.0129
0.0099
0.0075
0.0057
0.0043
0.0032
0.0023
0.0017
0.0012
0.4840
0.4443
0.4052
0.3669
0.3300
0.2946
0.2611
0.2296
0.2005
0.1736
0.1492
0.1271
0.1075
0.0901
0.0749
0.0618
0.0505
0.0409
0.0329
0.0262
0.0207
0.0162
0.0125
0.0096
0.0073
0.0055
0.0041
0.0031
0.0023
0.0016
0.0012
0.4801
0.4404
0.4013
0.3632
0.3264
0.2912
0.2578
0.2266
0.1977
0.1711
0.1469
0.1251
0.1056
0.0885
0.0735
0.0606
0.0495
0.0401
0.0322
0.0256
0.0202
0.0158
0.0122
0.0094
0.0071
0.0054
0.0040
0.0030
0.0022
0.0016
0.0011
0.00094
0.00090
0.00087
0.00085
0.00066
0.00064
0.00062
0.00060
0.00048
0.00047
0.00045
0.00043
0.00034
0.00033
0.00031
0.00023
0.00022
0.00016
0.06
0.07
0.08
0.09
0.4761
0.4364
0.3974
0.3594
0.3228
0.2877
0.2546
0.2236
0.1949
0.1685
0.1446
0.1230
0.1038
0.0869
0.0721
0.0594
0.0485
0.0392
0.0314
0.0250
0.0197
0.0154
0.0119
0.0091
0.0069
0.0052
0.0039
0.0029
0.0021
0.0015
0.0011
0.4721
0.4325
0.3936
0.3557
0.3192
0.2843
0.2514
0.2206
0.1922
0.1660
0.1423
0.1210
0.1020
0.0853
0.0708
0.0582
0.0475
0.0384
0.0307
0.0244
0.0192
0.0150
0.0116
0.0089
0.0068
0.0051
0.0038
0.0028
0.0021
0.0015
0.0011
0.4681
0.4286
0.3897
0.3520
0.3156
0.2810
0.2483
0.2177
0.1894
0.1635
0.1401
0.1190
0.1003
0.0838
0.0694
0.0571
0.0465
0.0375
0.0301
0.0239
0.0188
0.0146
0.0113
0.0087
0.0066
0.0049
0.0037
0.0027
0.0020
0.0014
0.0010
0.4641
0.4247
0.3859
0.3483
0.3121
0.2776
0.2451
0.2148
0.1867
0.1611
0.1379
0.1170
0.0985
0.0823
0.0681
0.0559
0.0455
0.0367
0.0294
0.0233
0.0183
0.0143
0.0110
0.0084
0.0064
0.0048
0.0036
0.0026
0.0019
0.0014
0.0010
0.00082
0.00079
0.00076
0.00074
0.00071
0.00058
0.00056
0.00054
0.00052
0.00050
0.00042
0.00040
0.00039
0.00038
0.00036
0.00035
0.00030
0.00029
0.00028
0.00027
0.00026
0.00025
0.00024
0.00022
0.00021
0.00020
0.00019
0.00019
0.00018
0.00017
0.00017
0.00015
0.00015
0.00014
0.00014
0.00013
0.00013
0.00012
0.00012
0.00011
0.00010
0.00010
0.00010
0.00009
0.00009
0.00009
0.00008
0.00007
0.00007
0.00007
0.00006
0.00006
0.00006
0.00006
0.00005
0.00005
0.00004
0.00004
0.00004
0.00004
0.00003
0.00003
0.00003
0.00003
0.00003
0.00002
0.00002
0.00002
0.00002
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00000
0.00000
0.00000
0.00000
0.00000
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Lean Six Sigma Green Belt Online Workbook
Measurement System Analysis (MSA) Study Type
Data type
Continuous data
Non Destructive
Gage R&R Study
(Crossed)
Attribute data
Destructive
Attribute
Agreement
Analysis
Gage R&R Study
(Nested)
Sources of Process Variation
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Gage R&R Worksheet
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Gage R & R Decision Criteria
According to AIAG guidelines, if your measurement system's variation is less than 10% of
process's variation, then it is acceptable. To evaluate your process variation, compare the
Total Gage R&R contribution in the %StudyVar (%SV) column in your output with the values
in the table.
%StudyVar (%SV)
Less than 10%
Acceptability
The measurement system is acceptable.
The measurement system is acceptable depending on the
Between 10% and 30% application, the cost of the measurement device, cost of
repair, or other factors.
Greater than 30%
The measurement system is not acceptable and should be
improved.
Attribute Agreement Analysis Decision Criteria
Use kappa statistics to assess the degree of agreement. Kappa values range from –1 to +1.
The higher the value of kappa, the stronger the agreement, as follows:

When Kappa = 1, perfect agreement exists.

When Kappa = 0, agreement is the same as would be expected by chance.

When Kappa < 0, agreement is weaker than expected by chance; this rarely occurs.
The AIAG suggests that a kappa value of at least 0.75 indicates good agreement. However,
larger kappa values, such as 0.90, are preferred.
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Cause & Effect Diagram Template
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FMEA Template
S.No. Process Step
Function
Potential
Failure Mode
Potential Effect S
of Failure on E
Customer
V
Potential
Causes
O Current Process Current Process D
C
Controls
Controls
E
C (Prevention)
(Detection)
T
R
P
N
Actions
Resp.& Target
Recommended
Date
S O D Futur
Actions Taken E C E e
V C T RPN
1
2
3
4
5
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Decision Making
There are two agents Rohit & Mohit, working with problem resolution process. The data
shows the times they take to resolve problems. Their performance is to be judged and one of
them is to be promoted. Data given 12 transactions. Give your judgment.
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Rohit
Mohit
43
39
23
33
43
42
35
28
33
36
43
34
38
39
39
43
35
34
23
20
39
34
34
35
Avg. of Rohit
Avg. of Mohit
35.7
34.7
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Formulating the Null Hypothesis and the Alternative
Hypothesis Statements (H0 and H1)
Question
Hypothesis Statements
A machine produces a particular component to a H0 :
specified mean diameter of 5 mm.
Some not very specific complaints are received
from Service that the components are not to H1 :
specification. So you decide to check whether the
process setting has changed significantly.
H0 :
It is claimed that the battery life manufactured by
H1 :
Company A exceeds 40 hours.
H0 :
Supplier Quality Assurance want to determine the
quality of plastic gear manufactured by Supplier 2
is better than Supplier 1.
H1 :
H0 :
It is claimed that the yield of improved Process B
is greater than old Process A by bringing a change
in an important factor X.
H1 :
Number of defectives observed in a process on H0 :
one of the days is 54 out of 1000 items inspected.
Can we statistically conclude that % of defectives H1 :
is greater than 5%.
A marketing analyst wants to determine whether H0 :
mailed advertisements for a new product result in
a response rate different from the national H1 :
average of 8%.
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Hypothesis Testing using Minitab
The procedure for hypothesis testing is usually similar, irrespective of the test being carried
out. Using the following sequence will generally lead to a satisfactory outcome.
S.No.
Action
Result
1
Define the objective
2
Check whether the Data is normally
distributed
3
Identify the appropriate test
4
Formulate the Null hypothesis and the
Alternative hypothesis (H0 and H1)
5
Is the test one-tailed or two-tailed?
6
Decide on the level of significance α
7
Obtain a sample (or samples) of data.
8
Perform the test in Minitab and Check
p value
9
Decide whether to accept or reject H0
10
Draw a conclusion
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Scatter Plot Decision Criteria
Correlation Coefficient Decision Criteria
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Multivoting Exercise
Procedure for conducting Multivoting Technique:
1) Combine Duplicate items in the list
2) Number (or letter) all items.
3) Decide how many items must be on the final list.
4) Decide how many choices each member will vote for.
5) Each member selects whatever is he or she thinks is most important.
6) Tally the votes.
7) Repeat if required.
Multivoting Example
Project Team conducted meetings which were not always as productive as they might have been. So
team leader called a meeting to identify the reasons for the lack of meeting productivity and to
determine which reasons the team thought most important.
The team leader a Brainstorming session which produced the following list. Now they need to narrows
down the list using Multivoting.
a) No Clear Agenda
b) No Clear Objectives and Directions
c) Vital members missing from meeting
d) Participants arrive unprepared for the meeting
e) Interrupted by visitors
f) Interrupted by phone calls
g) Failing to reach a conclusion
h) People “work” on other tasks during the meeting
i) Start late and run overtime
j) Off-topic conversations
k) Not having action points and a roadmap for follow-up
l) No minutes are taken at the meeting
m) No participation by the attendees
n) Discussions are dominated by a few talkers
o) Wrong attendees are invited
p) Scheduled at the last minute
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Value Added & Non Value Added (Waste) Activity
Exercise
Identify the VA & NVA steps for the manual assembly operation
on a Truck chassis assembly line.
S.No.
Steps
1
Delivering components to the assembly line
2
Walking 25 feet to pick up the component
3
Removing cardboard to expose the components
4
Orienting the component so it can be picked up
5
Picking up bolts for the component
6
Walking 25 feet back to the chassis on the assembly line
7
Positioning the component on the chassis
8
Walking to the power tool
9
Reaching for the power tool
10
Walking and pulling the power tool to the component on the
chassis
11
Pulling the power tool down onto the chassis
12
Placing the bolts in the component
13
Tightening the bolts to the chassis with the power tool
14
Walking 25 feet to pick up the component
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Takt Time Calculation
Takt time is defined as the amount of time available to produce one unit.
Customer Demand: 600 pcs per month
Working Days: 20
Available Production Time: 2 Shift operation, 16 Hrs/day
Takt Time =
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