Blended Lean Six Sigma Black Belt Training –
ABInBev
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Answers to DDC Study “Improve”
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About this Module
• You are the coach for the Green Belt doing the Brazil DDC project.
• He is planning to conduct a DoE• This is his first DoE so he has asked you for help filling
out the DoE planning sheet.
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Where He Is So Far
He indicates that he has completed the form as far as he can go without your help. (DoE Plan DDC.doc).He also indicates he has budget approval for 16 runs.What would you recommend and why?
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Determining the Factors
From the Pareto we know the four leading causes to test those we decided to use the following factors
1. Number of orders
2. Route in minutes
3. Truck loading time
4. Release time
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What Types of Experiment Could We Do?
Stat>DoE>Create Factorial Design>Display Available Designs
Viable designsViable designs
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The Viable Options
• A full factorial design without replication– Advantage
• No alias or confounding– Disadvantage
• No measure of error (saturated model) if all main effects and interactions are significant
• A half faction factorial design with two replicates– Advantage
• The replication provides a measure of error– Disadvantage
• Aliasing may be difficult for an inexperienced Green Belt to understand and fully explain
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Design Choice
Consensus was a full factorial design with the factor levels set at.
1. Number of orders 17 32
2. Route in minutes 56 73
3. Truck loading time 5.5 7.5
4. Release time 3 9
Complete the DoE Matrix
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Create the DoE Matrix
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Analyze the Data
• The experiment was performed and the data saved in DDC DoE.mtw
• Analyze the data and prepare to present the results
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Analyze the DoE
Stat>DoE>Factorial Analyze Factorial Design
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Analyze the DoE
For the initial model include all main effects and interactions
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First Model
ABCAC
ABDC
CDA
ABACDAD
ABCDBCDBDBCDB
121086420
Term
Effect
3.23
A Number of OrdersB Route in MinC Truck Loading in MinD Release Time in Min
Factor Name
Pareto Chart of the Effects(response is Number of Non Delivered Orders, Alpha = 0.05)
Worksheet: DDC DoE.MTWLenth's PSE = 1.25625
Reduce the model
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First Reduction
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Second Model
AC
C
CD
A
AB
AD
BD
BC
D
B
1086420
Term
Standardized Effect
2.57
A Number of OrdersB Route in MinC Truck Loading in MinD Release Time in Min
Factor Name
Pareto Chart of the Standardized Effects(response is Number of Non Delivered Orders, Alpha = 0.05)
Worksheet: DDC DoE.MTW
Reduce the model again
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Final Model
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Final Model
Release Time in Min
Route in Min
1086420
Term
Standardized Effect
2.16
Pareto Chart of the Standardized Effects(response is Number of Non Delivered Orders, Alpha = 0.05)
Worksheet: DDC DoE.MTW
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Check the Residuals
5.02.50.0-2.5-5.0
99
90
50
10
1
Residual
Per
cent
N 16
AD 0.249P-Value 0.703
20151050
2
0
-2
-4
Fitted Value
Resi
dual
3210-1-2-3-4
4.5
3.0
1.5
0.0
Residual
Frequen
cy
16151413121110987654321
2
0
-2
-4
Observation Order
Resi
dual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Number of Non Delivered Orders
Worksheet: DDC DoE.MTW
The residuals are normally distributed with a mean of zero and constant variance. No reason to reject the model.
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Optimize the Performance
Stat>DoE>Analyze Factorial Design>Response Optimizer
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Setup the Optimizer
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Optimizer Output
CurHigh
Low1.0000D
Optimal
d = 1.0000
MinimumNumber o
y = 2.4937
1.0000DesirabilityComposite
3.0
9.0
56.0
73.0Release Route in
[56.0] [3.0]
Non delivered orders are minimized by using the lower route time and lower release time. The model shows we should be able to achieve an average of ~2.5 non delivered orders.
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How Much of the Variation is Explained?
Stat>ANOVA>General Linear Model
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Session Window
Source DF Seq SS Adj SS Adj MS F PRoute in Min 1 537.08 537.08 537.08 99.43 0.000Release Time in Min 1 312.41 312.41 312.41 57.83 0.000Error 13 70.22 70.22 5.40Total 15 919.71
Remove the spaces that are interior to the column and source names.
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Prepare to Graph
Source DF SeqSS AdjSS AdjMS F PRouteinMin 1 537.08 537.08 537.08 99.43 0.000ReleaseTimeiMin 1 312.41 312.41 312.41 57.83 0.000Error 13 70.22 70.22 5.40Total 15 919.71
Your session window should like like this.
Copy this data excluding the total row to the clipboard then paste starting in the title row of the worksheet.
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Create the Graph
Graph>Pie Chart
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Specify the Labels
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Pie Chart
Error7.6%
ReleaseTimeiMin34.0%
RouteinMin58.4%
Pie Chart of Source
Worksheet: DDC DoE.MTW
Over 92% for the change in Non delivered orders is explained by release time and route time.
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Conclusions
The DoE has identified factors that must be controlled to minimize the non-delivered orders which in turn should increase the sales rate.