%0 Conference Proceedings
%T A Graph-Based Knowledge Model for Value-Oriented PLM Implementation
%+ University of Twente
%+ Cadmes
%+ Avans University of Applied Sciences
%A Koomen, Bas
%A Klein, Marcel, De
%A Vergouwen, Mayke
%Z Part 2: Information Technology Models and Systems Implementations
%< avec comité de lecture
%@ 978-3-030-94398-1
%( IFIP Advances in Information and Communication Technology
%B 18th IFIP International Conference on Product Lifecycle Management (PLM)
%C Curitiba, Brazil
%Y Osiris Canciglieri Junior
%Y Frédéric Noël
%Y Louis Rivest
%Y Abdelaziz Bouras
%I Springer International Publishing
%3 Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations
%V AICT-640
%N Part II
%P 225-239
%8 2021-07-11
%D 2021
%R 10.1007/978-3-030-94399-8_17
%K PLM
%K Product Lifecycle Management
%K Graph model
%K Knowledge management
%K Implementation
%Z Computer Science [cs]Conference papers
%X Product Lifecycle Management (PLM) has matured over the last two decades. The software and the market have evolved, resulting in a small number of dominant vendors offering an array of functionality in their software platforms. Moreover, PLM software is becoming available in the cloud as a service (Software-as-a-Service, SaaS). These developments impact the Value Added Resellers (VARs) who deliver software and services to the users of the PLM software. The VARs need to focus more on their added value by providing contextual application knowledge for PLM software in specific business environments. The management of this knowledge is a challenge for VARs because this relies strongly on tacit, empirical knowledge that resides in the heads of their staff. This paper describes a methodology that uses Benefit Dependency Networks to capture, manage, and reuse this knowledge. The methodology is based on the literature and then modified with insights from case studies with a PLM VAR. All relevant contextual elements in an extended Benefit Dependency Network of a PLM implementation are registered in a Graph Model, allowing consultants to find answers within a given industry context and configure a customer-specific implementation plan. The paper also describes a software design built on a Graph Database to use this knowledge in an operational VAR environment and build a foundation for future machine learning in implementation knowledge.
%G English
%Z TC 5
%Z WG 5.1
%2 https://inria.hal.science/hal-04195249/document
%2 https://inria.hal.science/hal-04195249/file/526631_1_En_17_Chapter.pdf
%L hal-04195249
%U https://inria.hal.science/hal-04195249
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-PLM
%~ IFIP-WG5-1
%~ IFIP-AICT-640