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Group decision support for product lifecycle management

Pasley, Robert C.; MacCarthy, Bart L.

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Assistant Professor in Information Syste


Product Lifecycle Management (PLM) systems support industrial organizations in managing their product portfolios and related data across all phases of the product lifecycle. PLM seeks to enhance an organization's ability to manage its product development activities and facilitate collaboration across organizational functions and between organizations. Effective decision-making is vital for the successful management of products over their lifecycle. However, PLM decision-making is an under-researched area. We argue that decision-making theory and group decision support concepts can be brought to bear to enhance PLM decision-making processes. We present and justify a set of six principles to support decision-making in a PLM context. The paper highlights the need to consider and capture decisions as distinct units of PLM knowledge to support product lifecycle management. We derive a generic information flow and a group decision support structure for PLM decision-making that encapsulates the six principles. Three industrial cases are analyzed to illustrate the application and value of the principles in supporting decision-making. The principles enable PLM decisions to be codified, recorded, and reviewed. Decision-making processes can be reused where appropriate. The principles can support future innovations that may affect PLM, such as ontological and semantic reasoning and Artificial Intelligence.

Journal Article Type Article
Acceptance Date May 31, 2020
Online Publication Date Jun 22, 2020
Publication Date 2021
Deposit Date Jun 23, 2020
Publicly Available Date Jun 23, 2021
Journal International Journal of Production Research
Print ISSN 0020-7543
Electronic ISSN 1366-588X
Publisher Taylor & Francis Open
Peer Reviewed Peer Reviewed
Volume 59
Issue 16
Pages 5050-5067
Keywords Management Science and Operations Research; Strategy and Management; Industrial and Manufacturing Engineering
Public URL
Publisher URL
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 22 June 2020, available online:


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