The purpose of this paper is to address misconceptions about the design of experiments (DoE) usefulness, avoid bad practices and foster processes’ efficiency and products’ quality in a timely and cost-effective manner with this tool.
To revisit and discuss the hindrances to DoE usage as well as bad practices in using this tool supported on the selective literature from Web of Science and Scopus indexed journals.
A set of recommendations and guidelines to mitigate DoE hindrances and avoid common errors or wrong decisions at the planning, running and data analysis phases of DoE are provided.
Errors or wrong decisions in planning, running and analyzing data from statistically designed experiments are always possible so the expected results from DoE usage are not always 100 percent guaranteed.
Novice and intermediate DoE users have another perspective for developing and improving their “test and learn” capability and be successful with DoE. To appropriately plan and run statistically designed experiments not only save the user of DoE from incorrect decisions and depreciation of their technical competencies as they can optimize processes’ efficiency and products’ quality (reliability, durability, performance, robustness, etc.) in a structured, faster and cheaper way at the design and manufacturing stages.
DoE usefulness will be increasingly recognized in industry and academy and, as consequence, better products can be made available for consumers, business performance can improve, and the link between industry and academy can be strengthened.
A supplemental perspective on how to succeed with DoE and foster its usage among managers, engineers and other technical staff is presented.
The authors are thankful to the anonymous reviewers for their suggestions and comments, which contributed to improve this manuscript.
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