Feasibility study for an automated engineering change process

M. E. Sharp, T. D. Hedberg, W. Z. Bernstein and S. Kwon

International Journal of Production Research, 2021, vol. 59, issue 16, 4995-5010

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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1893900 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:59:y:2021:i:16:p:4995-5010

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.1893900

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().