Feasibility study for an automated engineering change process.
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| Title: | Feasibility study for an automated engineering change process. |
|---|---|
| Authors: | Sharp, M. E.1 (AUTHOR), Hedberg Jr, T. D.1 (AUTHOR) thedberg@umd.edu, Bernstein, W. Z.1 (AUTHOR), Kwon, S.1,2 (AUTHOR) |
| Source: | International Journal of Production Research. Aug2021, Vol. 59 Issue 16, p4995-5010. 16p. 3 Diagrams, 2 Charts, 3 Graphs. |
| Subjects: | Production engineering, Feasibility studies, Search algorithms, Genetic algorithms, Engineering mathematics |
| Abstract: | Engineering change is a significant cost for projects. While avoiding and mitigating the risk of change is ideal, mistakes and improvements are recognised as more is learned about the decisions made in a design. This paper presents a feasibility and performance analysis of automating engineering change requests to demonstrate the promise for increasing speed, efficiency, and effectiveness of product-lifecycle-wide engineering-change-requests. A comparatively simple case is examined to mimic the reduced set of alterable aspects of a typical change request and to highlight the need of appropriate search algorithms as brute force methods are prohibitively resource intensive. Although such cases may seem trivial for human agents, with the volume of expected change requests in a typical facility, the potential opportunity gain by eliminating or reducing the amount of human effort in low-level changes accumulate into significant returns for the industry on time and money. Herein, the genetic algorithm is selected to demonstrate feasibility with its broad scope of applicability and low barriers to deployment. Future refinement of this or other sophisticated algorithms leveraging the nature of the standard representations and qualities of alterable design features could produce tools with strong implications for process efficiency and industry competitiveness in its projects execution. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 151932917 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Feasibility study for an automated engineering change process. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sharp%2C+M%2E+E%2E%22">Sharp, M. E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hedberg+Jr%2C+T%2E+D%2E%22">Hedberg Jr, T. D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> thedberg@umd.edu</i><br /><searchLink fieldCode="AR" term="%22Bernstein%2C+W%2E+Z%2E%22">Bernstein, W. Z.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kwon%2C+S%2E%22">Kwon, S.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2021, Vol. 59 Issue 16, p4995-5010. 16p. 3 Diagrams, 2 Charts, 3 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Production+engineering%22">Production engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Feasibility+studies%22">Feasibility studies</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+mathematics%22">Engineering mathematics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Engineering change is a significant cost for projects. While avoiding and mitigating the risk of change is ideal, mistakes and improvements are recognised as more is learned about the decisions made in a design. This paper presents a feasibility and performance analysis of automating engineering change requests to demonstrate the promise for increasing speed, efficiency, and effectiveness of product-lifecycle-wide engineering-change-requests. A comparatively simple case is examined to mimic the reduced set of alterable aspects of a typical change request and to highlight the need of appropriate search algorithms as brute force methods are prohibitively resource intensive. Although such cases may seem trivial for human agents, with the volume of expected change requests in a typical facility, the potential opportunity gain by eliminating or reducing the amount of human effort in low-level changes accumulate into significant returns for the industry on time and money. Herein, the genetic algorithm is selected to demonstrate feasibility with its broad scope of applicability and low barriers to deployment. Future refinement of this or other sophisticated algorithms leveraging the nature of the standard representations and qualities of alterable design features could produce tools with strong implications for process efficiency and industry competitiveness in its projects execution. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2021.1893900 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 4995 Subjects: – SubjectFull: Production engineering Type: general – SubjectFull: Feasibility studies Type: general – SubjectFull: Search algorithms Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Engineering mathematics Type: general Titles: – TitleFull: Feasibility study for an automated engineering change process. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharp, M. E. – PersonEntity: Name: NameFull: Hedberg Jr, T. D. – PersonEntity: Name: NameFull: Bernstein, W. Z. – PersonEntity: Name: NameFull: Kwon, S. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 59 – Type: issue Value: 16 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
