| Name | Design For Experiments |
| Abbreviation | DFE |
| Learning Cost | 160 |
| Playing Cost | 500 |
| Suggested Phases | 2,3,4 |
Engineers
| Mechanical Engineer | Industrial Design | System Engineer | Electrical Engineer | Production Engineer | Software Engineer |
| ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Technique and Issue Views
| BusinessNeeds | Stakeholder | Stakeholder Needs | System Requirements | System Structure Architecture |
| ✗ | ✗ | ✗ | ✔ | ✗ |
| System Functional Architecture | Detail Hardware Design | Detail Service Design | Detail Software Design | Manufacturing Operations |
| ✗ | ✔ | ✔ | ✔ | ✔ |
Technique Traits
| Identify Stakeholders | Elicit Needs | Remove Ambiguity | Layman's Terms | Technical Terms | Teamworkings |
| 0 | 1 | 2 | 0 | 3 | 1 |
| Traceability | Prioritizing | Exploring Breadth | Inside the Box | Outside the box | V&V |
| 3 | 1 | 4 | 1 | 1 | 4 |
Verification and Validation
| Analysis | Calculus | Inspection | Demonstration | Test |
| ✗ | ✔ | ✗ | ✗ | ✗ |
Design of experiments, referred to as DOE, is a systematic approach to understanding how process and product parameters affect response variables such as processability, physical properties, or product performance. Unlike quality, mechanical, or process tools, DOE is a purely mathematical tool used to define the importance of specific processing and/or product variables, and how to control them to optimize the system performance while maximizing properties [1][2].
Invented by Ronald A. Fisher in 1935 in his book “The design of experiments” [3], DOE uses statistical methodology to analyze data and predict product property performance under all possible conditions within the limits selected for the experimental design. Next to understanding how a particular variable affects product performance, interactions between different process and product variables are identified. DOE is a technique or procedure to generate the required information with the minimum amount of experimentation [1]. A DoE approach can greatly improve the efficiency in screening for suitable experimental conditions, for example, for cell culture, nutrient complement, factor level, optimization of a process, or robustness testing [4].
Design of experiments is best used to answer questions such as: “What factor contributes most to the problem?” and “what is the most excellent arrangement of aspect values to reduce dissimilarity in response?” [5].
