Application of Big data to configuration management in a PLM context

Abstract

The emergence of information and communication technologies (ICT) in the early 1990s, particularly the Internet, made it easy to produce data and distribute it to the rest of the world. Today's business information systems contain data that is more massive, complex and heterogeneous. The increase in complexity, globalization and collaborative work mean that an industrial project (product design) requires the participation and collaboration of actors who come from several fields and workplaces. Information retrieval is an essential function for any information system. However, the latter is never easy as it always represents a major bottleneck for all organizations. In the environment of complex, heterogeneous and multi-use data, providing all users with easy and simple access to data becomes more difficult due to lack of technical skills or different user perspectives.

PLM (Product Lifecycle Management) being a business strategy that aims to create, manage and share all the definition, manufacturing, maintenance and recycling information of an industrial product, throughout its life cycle. life, from preliminary studies to end of life and Big data, the collection of large data sets that are complex and difficult to analyze by traditional data processing methods.

In this context, this article proposes to establish the main obstacles to the deployment of Big data in PLM systems (the collection of data from the PLM system, the storage and transfer of data in the PLM, the processing of data based on industrialization knowledge and experience, data security and visualization, etc.). The objective is to apply methodologies related to Big data to the management of product configurations (management of product diversity in connection with the development of mass customization (mass customization)).