Modern tool kit for process excellence, gracias a Minitab Inc.

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

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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

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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

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