Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, it inhibits the ability to understand and model how multiple variables interact to jointly affect a response. Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.
Design of Experiments has numerous applications, including:
- Fast and Efficient Problem Solving (root cause determination)
- Shortening R&D Efforts
- Optimizing Product Designs
- Optimizing Manufacturing Processes
- Developing Product or Process Specifications
- Improving Quality and/or Reliability
This Blog series will review the key concepts behind Design of Experiments. A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented. Many common types of experiments and their applications are presented. These include experiments appropriate for screening, optimization, mixtures/formulations, etc. Several important techniques in experimental design (such as replication, blocking, and randomization) are introduced. A Case Study involving optimizing a manufacturing process with multiple responses is presented.
Following this blog series regularly will enable you to:
- Learn the basics of a methodology to perform experiments in an optimal fashion
- Review the common types of experimental designs and important techniques
- Develop predictive models to describe the effects that variables have on one or more responses
- Utilize predictive models to develop optimal solutions
Areas Covered in this Blog Series:
- Motivation for Structured Experimentation (DOE)
- DOE Approach / Methodology
- Types of Experimental Designs and their Applications
- DOE Techniques
- Developing Predictive Models
- Using Models to Develop Optimal Solutions
- Case Study
This blog series will be especially useful for those with these or similar positions:
Operations / Production Managers
Quality Assurance Managers
Process or Manufacturing Engineers or Managers
Product Design Personnel
Scientists
Research & Development personnel