Many companies face a unique dilemma: they’re data rich but information poor. Join Allise Wachs, Ph.D., for statistical concepts and methods that manufacturers can use to optimize products, processes, and decisions

1. Beyond Compliance
Webinar & Podcast Series for Process Manufacturers
Essential Statistical Methods for
Process and Product Optimization
with Allise Wachs, Ph.D.
President, Integral Concepts, Inc.

2. // About SafetyChain
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3. BEYOND COMPLIANCE
Helpful tips
✔ Informal, conversational approach
✔ Ask Questions (Q&A at end)
✔ Only panelists are displayed/heard
✔ Recording link will be shared
✔ Slides will also be shared
✔ Audio issues? Use call-in number

4. About the Presenter
BEYOND COMPLIANCE
Allise Wachs, Ph.D.
President, Integral Concepts, Inc.
● 20 years experience applying statistical methods to optimize
product designs and manufacturing processes
● Areas of expertise include designed experimentation, reliability
analysis, general statistical methods, statistical process
control, measurement system assessment, and stochastic
optimization
● Graduate degrees in Statistics, Industrial & Operations
Engineering

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About Integral Concepts, Inc.
Integral Concepts provides consulting and training services to companies around
the world. They assist companies in optimizing product design and manufacturing
processes to ensure high quality and reliability while minimizing costs.

6. BEYOND COMPLIANCE
● Process and Product Optimization
● Key Statistical Methods
○ Measurement Systems Assessment
○ Statistical Process Control
○ Process Capability Assessment
○ Design of Experiments
○ Reliability Analysis
Agenda

7. BEYOND COMPLIANCE
An act, process, or methodology of making something (such as a design, system,
or decision) as fully perfect, functional, or effective as possible.*
*Merriam-Webster
Optimization

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● Reliable measurement systems (test methods) that measure accurately
and precisely
● Understanding of key input factors that affect process/product
performance
● Consistent and predictable processes (i.e. stability)
● Ability to quickly detect significant changes in key input factors
● High levels of conformance to specifications (i.e. capability)
● Rapid Product Development and Improvement while controlling risks
Optimization Essentials

9. BEYOND COMPLIANCE
Optimization Examples

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● Inadequate measurement systems
● Reliance on inefficient trial-and-error approaches
● Poor understanding of many important aspects of successful Statistical
Process Control implementation (e.g. types of charts used, sampling
approach, sample sizes)
● Unmet assumptions when estimating process capability
● Lack of focus of understanding and minimizing variation
● Inadequate reliability and shelf-life testing
We Commonly Encounter

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Most companies have huge opportunities to optimize
products and processes by:
● Designing and developing products cost effectively
● Reducing waste
● Collecting data in an appropriate way – and using it wisely
● Detect potential manufacturing issues before they blow up
● Basing decisions on the appropriate analysis of data
The Opportunity

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Quantitative methods that depend on MSA:
● Statistical Process Control
● Inspection Activities
● Process Capability Assessment
● Hypothesis Testing
● DOE / Data Modeling
Why Measurement Systems Assessment (MSA)?

13. BEYOND COMPLIANCE
● Important measurement system
characteristics include discrimination,
accuracy, precision (repeatability and
reproducibility), linearity, and stability.
● Techniques exist to assess measurement
systems for each of these important
characteristics.
● Validating measurement systems is an
important prerequisite to relying on data.
What Is MSA?

14. BEYOND COMPLIANCE
• Understand and Consider All Types of Measurement Error (Repeatability,
Reproducibility, Bias, Non-linearity, Instability)
• Ensure Adequate Gage Discrimination
• Select Specimens Wisely for Gage R&R Studies
• Understand, Calculate, and Interpret R&R Metrics Correctly
• Look Beyond the “Pass” or “Fail” Outcomes in a Gage R&R
• Expanded Gage R&R Studies to Include Potential Sources of Variation
• Apply Methods for Non-Replicable Systems as Necessary
• Use Control Charts to Assess the Stability of the Measurement Process
• Compare Systems to each other
Key Aspects of MSA

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● A proactive monitoring system that detects
significant process changes in key
characteristics
● A tool for hearing the “voice of the process”
● Objective criteria for reacting / intervening
● Distinguishes signals from the noise
● A tool to prevent problems by detecting
significant changes quickly
What Is Statistical Process Control

16. Poll
What best describes your current use of SPC
to detect significant process changes?

17. BEYOND COMPLIANCE
● Focus on prevention rather than inspection
● Only processes that are in a state of statistical control (e.g. stable) can
produce predictable outputs
● Monitoring and ensuring process stability eliminates the reliance on
inspection processes (such as acceptance sampling)
Key SPC Principles

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• Management is responsible for quality
• Quality cannot be achieved by inspection
• Statistical methods are required to understand and
control processes by minimizing variation
The Deming Philosophy

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● Control charts tell us when the system has changed, (out of control or
unstable) so we can quickly identify the causes and prevent an issue or make
an improvement
● These can be changes that are still within specification—but are statistically
different than where the process was previously running.
● Note that the appropriate method to assess whether the products will meet
specification consistently is Process Capability Analysis (provided that the
process is stable).
The Purpose of SPC

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Monitors Location
Monitors Variation

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Control Charts Monitor the Current State of the Process

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Cause Scenarios

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1. Focus on Key Characteristics
2. Choose the “Right” Chart
3. Understand Specification Limits vs. Control Limits
4. Employ Effective Sampling Strategy
5. Select the Right Sample Size
6. Automate SPC Where Appropriate
7. Empower Operators
8. Manage the Process & Drive Improvement
Key Aspects of SPC Implementation

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Process Capability assesses the ability of a process to meet product or
service specifications.
What is Process Capability?

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Stability & Capability

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● Process Capability assessment are only informative and predictive
for stable (in control) processes
● Process Stability must be assessed and demonstrated before
capability is assessed
● Process Stability is assessed using statistical process control
charts (SPC)
Before Capability

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Capability Assessments

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• Correct interpretation of capability indices (and understanding their limitations)
• Appropriate handling of non-normal data
• Ensuring evidence of Stability before assessing Capability
Key Aspects of Capability Assessment

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Does Cpk
Distinguish Between These Processes?
Example: Limitations of Capability Indices

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Non-normal Process Capability

33. BEYOND COMPLIANCE
• Determine Target Weight to optimize filling process by balancing costs of
overfilling with risks of non-compliance with company/industry standards
(e.g. unit exceeding MAV)
• Reduce filling process variation to allow a more cost-effective target
weight (closer to label weight)
Application of Process Optimization

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● An Efficient Experimental Approach that
Produces Predictive Models that Describe
Cause and Effect Relationships
● The Effect of Controllable Variables and
their Interactions on Response(s) is
Quantified and Modeled
What is Design Of Experiments?

36. BEYOND COMPLIANCE
• Efficiently Identify which Factors and
Interactions Influence a Characteristic of
Interest and Quantify the Impact
• Identify which Factors Should be Controlled
and the Sensitivity Required
• Develop Mathematical Predictive Models
• Develop Optimal Design and Manufacturing
Parameters (over 1 or more Responses)
Y = f(x1
, x2
, x3
, …)
Design Of Experiments

37. BEYOND COMPLIANCE
• Determining Key Characteristics that should be controlled in the
manufacturing process
• Reducing variation in key performance requirements
• Setting specifications for design parameters (e.g. dimensional tolerances,
material choices, material properties, etc.)
• Setting specifications for machine/process settings (temperatures,
pressures, speeds, cycle time, etc.)
• Determining how much variation in Key Characteristics can be tolerated
before product performance is impacted
Where is DOE Useful?

38. BEYOND COMPLIANCE
• Determining how multiple process variables interact with each other (e.g.
what dependencies exist)
• Finding robust process settings that make performance measures insensitive
to variation in difficult to control process variables
• Solving problems efficiently (while avoiding time-consuming and inefficient
trial-and-error approaches)
• Modeling complex processes so that the relationship between inputs and
outputs is understood
• Optimizing product designs or manufacturing processes (with multiple
requirements)
Where is DOE Useful?

39. BEYOND COMPLIANCE
• Most industrial systems are riddled with interactions, and our experimental
designs must be able to describe and model them!
• Some applications (e.g. chemical) have important 3-factor interactions that
must be understood
• One factor at a time (OFAT) studies are not able to capture interactions
• Some DOE methods (e.g. Taguchi) do not permit effective
understanding/modeling of interactions
Interactions - Key Points

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Design
Space
Factorial Designs (2 Levels)

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Sequential Approach to Experimentation

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Main Effects for Avg. Distortion

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An Interaction Effect for Avg. Distortion

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The predictive model in coded units for Distortion is:
Estimated Distortion = 0.3725 + 0.09 GlassTemp –
0.04771 PackTime + 0.13271 MoldTemp +
0.09896 (GlassTemp)(MoldTemp) –
0.07083 (PackTime)(MoldTemp) –
0.04021 (GlueThick)(PackTime)
The Model for Avg. Distortion

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Illustrative Contour Plot

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Multi-Response Optimization

47. BEYOND COMPLIANCE
Allise Wachs, President
Integral Concepts, Inc.
www.integral-concepts.com
allise@integral-concepts.com
Contact Integral Concepts for Help or More Information!

48. Q&A
BEYOND COMPLIANCE

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