Abstract
Product Lifecycle Management (PLM) systems support industrial organisations in managing their product portfolios and related data across all phases of the product lifecycle. PLM seeks to enhance an organisation's ability to manage its product development activities and support collaboration across organisational functions and business units, and between organisations. Effective decision-making is vital for the successful management of products over their lifecycle. However, decision-making is an under-researched area in PLM. 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 analysed 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.
Highlights
Product Lifecycle Management (PLM) systems have been developed over the last two decades to enhance an organization’s ability to manage its product portfolios over the product lifecycle (Stark 2018)
We argue that group decision support concepts (DeSanctis et al 2008) can be used to enhance PLM decision-making processes and can assist in defining the processes needed for information gathering, decision structuring, communication, and the recording of planned actions across the product lifecycle
The paper highlights how group decision support concepts can help to address the requirements for the capturing and reuse of PLM decisions and decision-making processes, contributing to the accumulation of organizational knowledge and organizational learning
Summary
Product Lifecycle Management (PLM) systems have been developed over the last two decades to enhance an organization’s ability to manage its product portfolios over the product lifecycle (Stark 2018) Such systems store data and information about products and the processes needed to produce them, and seek to capture and reuse knowledge to support organizations in developing new products, manufacturing them, introducing them to the market, and managing them over their lifecycle (Enríquez et al 2018; Marra et al 2018). PLM review papers such as Meier et al (2017) and Nyffenegger, Rivest and Braesch (2016) and practitioner literature such as Fleming et al (2017) do not show evidence of specific support for decision-making in PLM
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