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Design of experiments – overcome hindrances and bad practices

Design of experiments – overcome hindrances and bad practices PurposeThe 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.Design/methodology/approachTo 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.FindingsA 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.Research limitations/implicationsErrors 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.Practical implicationsNovice 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.Social implicationsDoE 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.Originality/valueA supplemental perspective on how to succeed with DoE and foster its usage among managers, engineers and other technical staff is presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The TQM Journal Emerald Publishing

Design of experiments – overcome hindrances and bad practices

The TQM Journal , Volume 31 (5): 18 – Oct 10, 2019

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References (71)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1754-2731
DOI
10.1108/TQM-02-2019-0035
Publisher site
See Article on Publisher Site

Abstract

PurposeThe 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.Design/methodology/approachTo 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.FindingsA 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.Research limitations/implicationsErrors 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.Practical implicationsNovice 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.Social implicationsDoE 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.Originality/valueA supplemental perspective on how to succeed with DoE and foster its usage among managers, engineers and other technical staff is presented.

Journal

The TQM JournalEmerald Publishing

Published: Oct 10, 2019

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