Del Webinar del mismo nombre, Minitab Inc., aliado de Blackberry&Cross desde 2004 para toda América Central, nos trae estas valiosas ideas sobre como combinar DMAIC con herramientas de Machine Learning. Excelente contenido. Descubra más sobre Minitab en http://minitab.blackberrycross.com

1. The Modern Toolkit
for Process Excellence
Gillian Groom
Minitab

2. © 2019 Minitab, Inc.
What Minitab offers
Powerful statistical software
everyone can use.
Integrated project tools and reporting
to manage Continuous Improvement.
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statistics and Minitab® anytime, anywhere.
Powerful data mining with machine
learning and predictive analytics.
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Services
Training
Public courses or onsite training
matched to your requirements.
Statistical Consulting
Personalized help with your
statistical and analysis challenges
from experts.
Support
Assisting your use of the software,
installation and implementation.
2

3. © 2019 Minitab, Inc.
Throughout her career, Gillian has been applying
statistical analysis to guide informed management
decisions on business opportunities or problems.
Gillian has a Master's Degree in Probability and Statistics
from Sheffield University.
Meet the Presenter:
Gillian Groom
Technical Training Specialist, Minitab
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4. © 2019 Minitab, Inc.
Detergent Improvement Project
A detergent production plant needs
• Stabilize the performance of its new line of eco-
labeled detergents
• Improve the lead times of its new dedicated
production line.
Using a Classic DMAIC Approach to
Manage The Project

5. © 2019 Minitab, Inc.
Define Business Understanding
• Document the project in a project charter
• Determine key stakeholders
• Establish initial goals and benefits
• Define resources– IT, Process Engineer, Blackbelt
• Define the project– Determine and address the cause of lack of stability during washing tests
• Establish metrics that measure the success of the project– Defect reduction of 50%
• List assumptions and risk factors– Biggest risk factor was the ability to get the right data

6. © 2019 Minitab, Inc.
Define Map your processes

7. © 2019 Minitab, Inc.
Average Lead Time: Over 9 hours
Define Value Stream Mapping

8. © 2019 Minitab, Inc.
Measure Data Understanding
• Select the variables and records and aggregate and
clean data– Data from different databases, determine
applicable date range
• Note issues with collection methods for the data
• Verify validity and quality of the data
• Characterize the current status of the variable of interest

9. © 2019 Minitab, Inc.
Measure Gage R&R
Gage R&R results are good:
Within the 10% guideline for measurement
fluctuations compared to overall fluctuations

10. © 2019 Minitab, Inc.
Measure Statistical Summary
Whiteness data of washing sample tests:
Normally distributed
1st Quartile 86.308
Median 88.088
3rd Quartile 89.636
Maximum 93.780
87.696 88.304
87.805 88.369
2.195 2.627
A-Squared 0.20
P-Value 0.877
Mean 88.000
StDev 2.392
Variance 5.722
Skewness -0.121603
Kurtosis -0.193540
N 240
Minimum 81.139
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev
94929088868482
Median
Mean
88.488.288.087.887.6
95% Confidence Intervals
Summary Report for Test results

11. © 2019 Minitab, Inc.
Measure Baseline Capability
Soil removal needs to be as uniform as
possible whatever water hardness, type of
washing machine, wash loads …
Whiteness
capability is poor
often below the
lower spec (85)

12. © 2019 Minitab, Inc.
Measure Baseline Control Chart
Special / Assignable causes impact the
process
These causes need to be identified
Process is not in
control (many out
of control points)
464136312621161161
92
91
90
89
88
87
86
85
84
83
Sample
SampleMean
__
X=88.000
UCL=90.588
LCL=85.412
1
1
1
11
1
1
Xbar Chart of Test results

13. © 2019 Minitab, Inc.
Analyze Modeling/Evaluation
• Explore data and make initial observations about
relationships between variables
• Select modeling technique(s)
• Build model(s)
• Assess model(s)
• Interpret final model
• Discuss model results with key stakeholders
Select modeling
technique(s)
Build model(s)Assess model(s)

14. © 2019 Minitab, Inc.
Analyze Identify Key Input
Use Brainstorming to try to identify key inputs

15. © 2019 Minitab, Inc.
The impact of surfactant concentration on
Whiteness results after washing cycles is
confirmed by data analysis
Analyze Regression

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Surfactants are compounds added to detergents.
They act to attract grease/dirt and repel water
What is a Surfactant?

17. © 2019 Minitab, Inc.
How to Improve Surfactant Concentration
• Database of Surfactant concentration based on different manufacturing conditions available
• Dataset messy
• Lots of missing items
• Outliers
• Complex Interactions
• Regression does not like this type of data
• Machine learning methods, can handle this type of data. It just recognises the patterns in the data

18. © 2019 Minitab, Inc.
Analyze Modeling/Evaluation
A Classification and Regression Tree (CART) has
been used to model surfactant concentration and
understand the best method for manufacturing the
Surfactant for our Eco Labeled Detergent

19. © 2019 Minitab, Inc.
Analyze Modeling/Evaluation
Higher Mixing Speeds and using
equipment CL have a positive impact on
surfactant concentration

20. © 2019 Minitab, Inc.
Improve Model Deployment
• Establish an improvement plan : benchmark based on CL tool
• Implement the changes
• Validate results with new data
• Establish a monitoring and maintenance plan
• Set schedule to verify that model results have not changed
• Update model when changes occur
• Present project results to key stakeholders
• Close out project

21. © 2019 Minitab, Inc.
Improve Capability Comparison
Compare quality performance before
and after improvement

22. © 2019 Minitab, Inc.
Improve Value Stream Mapping
Detergent Production Value Stream Map :
use lean techniques to reduce queuing times
Average Lead Time : Under 7 Hours

23. © 2019 Minitab, Inc.
Control Control chart
Monitor mixing speed so that
adjustments can be made when
Mixing speed reaches low values.

24. © 2019 Minitab, Inc.
Detergent Case Study Conclusions
• Companion provided the framework to manage the project
• In the Measure Phase we used Minitab to
▪ Check data quality
▪ Provide the baseline to measure any improvements
• Companion provided the tools to document results from brainstorming and C&E Analysis

25. © 2019 Minitab, Inc.
• Minitab and Salford Predictive Modeler used in Analyze Phase
▪ Regression modelled the relationship between Surfactant and Brightness
▪ Data available to identify “best” manufacturing process for our Surfactant requirements not suited to regression
▪ CART decision tree quickly identified the manufacturing settings required. Machine learning tools uncovered
relationships that may have been missed otherwise, due to size and complex relationships in the data
• Minitab Analysis used in Control stage of project
Detergent Case Study Conclusions

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