jane rencontre tarzan The amount and category of digital information in Information System of Product Lifecycle Management (PLM) has been increasing since the last decades. In one hand, innovation in informatics and information systems accelerates the development of production and PLM systems. And in the other hand, technical innovation is the main obstacle to the standardization, accessibility, and reusability of the digital information. As we have to access, retrieve and reuse the product and production knowledge through the whole product lifecycle, which could be rather longer (e.g., aircraft – 50 years) than the expected lifetime of a manufacturing or management software application to interpret data (approximately 3 years), it’s quite important to perform efficient Long Term Knowledge Retention (LTKR) to improve the sustainability of manufacturing and maintenance in PLM.
The enterprises and industries concern about the knowledge they are archiving as mightily as the data they are producing. Because for enterprises and industries, The variety of engineering data types and complexity of the relationships between the information units comprising these data types  is one big challenge for archiving. The reality is that an archive must capture all of the data required to completely define the product, and in some instances, processes . For each type of data, or each type of relationship between the information units, specific methods of archiving have to be used. With the evolution of the data formats, large quantity of types and volumes is increasing, data and its relationships during the engineering process could be more and more complex.
Moreover, for production enterprises and industries, production system could be considered as a dynamical system within the Product Lifecycle. Here “dynamical system” means during the whole Product Lifecycle, system process and production data are both assumed to be available, even some of the design data were produced decades before using technologies, hardware or software that have obsolesced. Therefore, the extensibility and reusability of the digital models and systems are required by the production enterprises and industries for long term. For technologies, hardware and software may obsolesce, yet digital model and system should be able to extended and reused after a long time. However, in reality, there is always a dilemma between the innovation of digital technologies and digital preservation, as well as new technologies may always surprise enterprises and industries by unpredictable fabulous features against the original digital models and systems.
Furthermore, the accuracy of digital data after long term and changing of archival technologies and media is one other challenge. As extending the digital models and systems, preserved data could be available after long time. However, information may lose during the long archiving and extending process. The lack of accuracy of digital information may lead social and economic consequences to enterprises and industries. Besides the data itself, the metadata to describe or to locate the data is required to be semantically rich, for technical as well as organizational reasons. The evolutions of the organizations and people are regular improvements for enterprises, and a person in one position would be changed over time. Thus without enough metadata, the last person in one position will not locate or reuse proper data, for he may not acquire exactly the same knowledge and experiences as his predecessor in this position. When accidents occur, the chains of knowledge connections will be broken, will always occur, and without semantically rich metadata to explain the preserved data the accidents will also make the preserved data unreachable.
Our objective of this work is facing on the LTKR challenges, to develop a methodology and architecture of LTKR for production information systems. We aime to propose better strategy for selection of proper knowledge for long term and provide process of archiving to ensure higher accessibility and reusability of knowledge in order to support enterprises’ needs to address their challenges : knowledge capitalization and reuse, business process reengineering, product diversity, time to market, etc.