Design of experiments (DOE) is a systematic method to determine the relationship between factors affecting a process and the output of that process. In other words, it is used to find cause-and-effect relationships. This information is needed to manage process inputs in order to optimize the output.
An understanding of Design of experiments (DOE) first requires knowledge of some statistical tools and experimentation concepts. Although a Design of experiments DOE can be analyzed in many software programs, it is important for practitioners to understand basic DOE concepts for proper application.
Well-performed Design of experiments (DOE) may provide answers to questions such as:
- What are the key factors in a process?
- At what settings would the process deliver acceptable performance?
- What are the key, main and interaction effects in the process?
- What settings would bring about less variation in the output?
The most commonly used terms in the Design of experiments (DOE) methodology include: controllable and uncontrollable input factors, responses, hypothesis testing, blocking, replication and interaction.
Design of experiments (DOE) is a powerful technique for discovering a set of process or design variables which are most important to the process/product/system and then assisting experimenters to determine at what levels these variables should be set/kept to optimize performance.
Design of experiments (DOE) is having it’s application in Manufacturing, Finance, Services, Research & Development.