The Modern Toolkit for Process Excellence
Gillian Groom Minitab
What Minitab offers Products
Powerful statistical software everyone can use.
Integrated project tools and reporting to manage Continuous Improvement.
Online learning solution to master statistics and MinitabÂŽ anytime, anywhere.
Powerful data mining with machine learning and predictive analytics.
Services Training
Statistical Consulting
Support
Public courses or onsite training matched to your requirements.
Personalized help with your statistical and analysis challenges from experts.
Assisting your use of the software, installation and implementation.
2 Š 2019 Minitab, Inc.
Meet the Presenter:
Gillian Groom Technical Training Specialist, Minitab
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.
3 Š 2019 Minitab, Inc.
Detergent Improvement Project
A detergent production plant needs
•
Stabilize the performance of its new line of ecolabeled detergents
•
Improve the lead times of its new dedicated production line.
Using a Classic DMAIC Approach to Manage The Project
© 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
© 2019 Minitab, Inc.
Define
Map your processes
Š 2019 Minitab, Inc.
Define
Value Stream Mapping
Average Lead Time: Over 9 hours
Š 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
© 2019 Minitab, Inc.
Measure
Gage R&R
Gage R&R results are good:
Within the 10% guideline for measurement fluctuations compared to overall fluctuations
Š 2019 Minitab, Inc.
Measure
Statistical Summary Summary Report for Test results Anderson-Darling Normality Test A-Squared P-Value Mean StDev Variance Skewness Kurtosis N Minimum 1st Quartile Median 3rd Quartile Maximum
Whiteness data of washing sample tests:
0.20 0.877 88.000 2.392 5.722 -0.121603 -0.193540 240 81.139 86.308 88.088 89.636 93.780
95% Confidence Interval for Mean 82
84
86
88
90
92
87.696
94
88.304
95% Confidence Interval for Median
Normally distributed
87.805
88.369
95% Confidence Interval for StDev 2.195
2.627
95% Confidence Intervals Mean
Median 87.6
87.8
88.0
88.2
88.4
Š 2019 Minitab, Inc.
Measure
Baseline Capability Whiteness capability is poor often below the lower spec (85)
Soil removal needs to be as uniform as possible whatever water hardness, type of washing machine, wash loads ‌
Š 2019 Minitab, Inc.
Measure
Baseline Control Chart Xbar Chart of Test results 92 1
91
Process is not in control (many out of control points)
1
1
Special / Assignable causes impact the process These causes need to be identified
Sample Mean
90
UCL=90.588
89 _ X=88.000
88 87 86 85
1
1
LCL=85.412
1
84
1
83 1
6
11
16
21
26
31
36
41
46
Sample
Š 2019 Minitab, Inc.
Analyze
Modeling/Evaluation Select modeling technique(s)
• Explore data and make initial observations about relationships between variables • Select modeling technique(s) • Build model(s)
• Assess model(s) • Interpret final model Assess model(s)
Build model(s)
• Discuss model results with key stakeholders
© 2019 Minitab, Inc.
Analyze
Identify Key Input
Use Brainstorming to try to identify key inputs
Š 2019 Minitab, Inc.
Analyze
Regression
The impact of surfactant concentration on Whiteness results after washing cycles is confirmed by data analysis
Š 2019 Minitab, Inc.
What is a Surfactant? Surfactants are compounds added to detergents. They act to attract grease/dirt and repel water
Š 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
© 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
Š 2019 Minitab, Inc.
Analyze
Modeling/Evaluation
Higher Mixing Speeds and using equipment CL have a positive impact on surfactant concentration
Š 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
© 2019 Minitab, Inc.
Improve
Capability Comparison
Compare quality performance before and after improvement
Š 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
Š 2019 Minitab, Inc.
Control
Control chart
Monitor mixing speed so that adjustments can be made when Mixing speed reaches low values.
Š 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
© 2019 Minitab, Inc.
Detergent Case Study Conclusions
• 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
© 2019 Minitab, Inc.
Additional Resources: ▪ TRAINING: Enhance your skills with Minitab’s industry training courses ▪ FREE CONSULTATION: Which tools and software are right for you? ▪ ONLINE RESOURCES: ▪ Webinars & Videos ▪ Ebooks ▪ Articles on our blog
Learn more at Minitab.com
October 8-11 Lansdowne Resort & Spa in Leesburg, VA
• Dozens of presentations • Early Bird pricing through June 30 • Get expert advice in The Minitab Lab • Participate in new User-centered Design Studio More information about Insights 2019
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© 2019 Minitab, Inc.
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