Case Study: Using DOE to Reduce Manufacturing Defects
Rick Adams
Lean/Six Sigma Black Belt Albany International
rick.adams@albint.com
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Albany International at a Glance
• Albany International was formed in 1969 with the merger of three major producers of fabrics for the paper and related industries
• There are two major business segments
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Machine Clothing: the world’s leading producer of custom designed fabrics and belts for the paper, nonwovens, and other process industries
Composites: a rapidly growing supplier of highly engineered composite parts for the aerospace and other industries
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Albany International at a Glance
• Albany International has facilities in 10 countries, and approximately 4000 employees
• Wisconsin plants located in Menasha and Kaukauna
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Definition: Design of Experiments (DOE)
• DOE is a method for planning and executing structured experiments that are set up to study more than one factor at a time
• Discover which factors significantly affect the process and which do not
• Learn about interactions among factors
• Build quality into the system and make processes more robust
Definition: Design of Experiments (DOE)
• Process changes are made systematically to force variation into the process so that key input variables are identified
Systematic Continuous Improvement
• DOE is not the solution for every problem
• The appropriate tools are applied based upon
the process problem and opportunity
Continuous improvement
VSM
Theory of Constraints
Kanban
ConWIP
Standard Work
5S
Job Methods
TPM
Quick change
Root Cause Analysis
Visual systems
DOE
DOE
MSA
Machine Clothing Weaving
• Forming and Engineered Fabrics are woven on wide looms at high tension
Problem Statement
• Fabrics with slacks cannot be shipped to the customer, and fabrics with defects must be reworked or rejected
Defect Analysis
• Even though not many fabrics were rejected, slacks were the highest frequency defect
Defect Analysis
• Most slacks were in one product type on similar looms
• Value is always viewed from the customer perspective
Root Cause Analysis
• Root Cause Analysis with process experts indicated a possible reason for the defect occurrence was incorrect set up of the material feed system
RCA
Analyze the Process
• Process map made by the team defined the set up process and start up procedure
Getting Started with the DOE
• The operators identified 12 possible adjustments
• With specific factors, DOE is the appropriate tool
Define Scope, Factors and Factor Levels
• Charter updated with the modified scope • The Process Experts selected 7 factors as priority
• Factor levels are set to boundary conditions
DOE
Definition: Screening Design
• Screening designs are used when there is very little process knowledge and many factors
• Separate significant factors from insignificant factors
• Will not provide the optimum process condition
• The appropriate design must be selected based on time and resources that are available
Definition: DOE Runs
• A Run is an individual combination of conditions
• Each factor will be set at either the low or high level
Design Selection for Screening
• Screening Designs are Fractional Factorial Designs to minimize the number of runs 27‐3 = 16 runs Resolution IV
2 center points
• The 7 selected factors will be studied in 16 runs
• This is a 1/8 fraction of all possible runs
Selection of Responses
• There were three critical responses
o Slacks: defect reduction
o Stops: runnabiltiy and efficiency
o Tension: key process variable for set up
• Consider how precisely the response can be measured and how stable it is over time
o Measurement Systems Analysis (MSA) was used to define some factor settings and study response measurements o What conditions outside the experiment can effect the response? Confirmation of Factors and Factor Levels
• Preliminary tests were run to be sure that all factors and factor levels are realistic
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Use Data Collection Worksheets
• Data collection worksheet keeps data organized
• Run the DOE in randomized Run Order
• Only one person should record responses
Definition: Calculated Main Effects
• A calculated effect is the difference between the average values of runs at low level and runs at high level
DOE
• As the factor is changed from low level to high level, the response increases from 0.39 to 0.54
• The calculated Main Effect = +0.15
Results of Screening Design
• A Pareto Chart is used to compare Calculated Effects
• The model is very complex, so more tests are needed
Characterization Trials
• Learn more about main effects and interactions
• Improve the confidence in the results (R2)
• Adjust factor levels as needed, and set insignificant factors to best level for the process
Characterization Trials
• There are 3 significant factors and 4 constants
• Full Factorial Design to study 3 factors in 8 runs
• All Main Effects and Interactions can be estimated
23 = 8 runs Full Factorial
2 center points
Analyzing the Experiment • The characterization DOE provided a better model
o 2 significant main effects, 1 significant interaction
o The R2 was 98.6%
Interpret Effects Plots
• Main Effects plots can be used to help determine the best process settings
Interpret Effects Plots
• An interaction is when the setting of one factor determines the best setting for another factor
• Significant interactions are very important!
Putting the Results into Practice
• What do we tell the operators?
• Confirmation trials were run to validate the results
Control Plan: Use Set Up Page
• All factors from the DOE are on set up page
• Operators use the settings at start up
• QC Technician checked loom set up daily
Improvement Metrics
• Slacks and related defects significantly reduced so that rejects eliminated, and less re‐work required
• Quicker start up and improved runnability
DOE
Some Final Thoughts
• Use DOE to study many factors at one time
• Find out which factors are significant, which factors are insignificant (Both are important)
• Learn about interactions among factors
• DOE can help to build quality into the system and make processes more robust
• Preparation and discipline are critical
• Use process experts
• Be sure the results make sense!
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