Group decision support for product lifecycle management

Abstract

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 underresearched 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.

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

“…The rapid development of new-generation information technology has led to the further development of big data acquisition, processing, analysis and application in the full life cycle (FLC) management process of complex product design, manufacturing and service, among which the more typical ones are based on big-data-driven [8], IoT [9], Industrial Internet [10], DT [11], decision theory and group decision making [12], etc. For example, big-data-driven complex product life cycle management emphasizes the characteristics of big data through the integrated application of data and knowledge at all stages of the life cycle to enhance the intelligent decision-making capability of complex product life cycle management.…”