An overview of knowledge sharing in new product development

This paper provides an overview of some of the issues in knowledge management related to the sharing of knowledge in new product development. Previous research and concepts reported by international researchers, and examples of the research projects carried out by the authors will be introduced. The paper first provides an overview of the history and importance of innovation and challenges in manufacturing. Then the importance of new product development in the sustainable success of manufacturing enterprises in the globalised business operations is discussed. The formalisation and modelling of product development processes will also be introduced. The concept and different definitions of knowledge management by previous researchers are then introduced, with further discussion on knowledge sharing. At this point, the authors’ research in knowledge sharing is also introduced. Finally, the trend of using social media and Enterprise 2 technologies in knowledge management and sharing is introduced using the recent research projects of the authors as examples

Experts’ knowledge renewal and maintenance actions effectiveness in high-mix low-volume industries, using Bayesian approach

International audienceIncreasing demand diversity have resulted in high-mix low-volume production where success depends on our ability to quickly design and develop new products. This requires sustainable production capacities and efficient equipment utilization which is ensured through appropriate maintenance strategies. At present, these are derived from experts' knowledge, capitalized in FMECA (Failure Mode, Effect and Criticality Analysis) and/or maintenance procedures. (Abu-Samah et al. 2015) found increasing unscheduled breakdowns, failure durations and number of repair actions in each failure as the key challenges while sustaining production capacities in complex production environment. This is an evidence that maintenance based on the historical knowledge is not always effective to cope up with an evolving nature of equipment failure behaviors. Therefore, in this paper, we present an operational methodology based on Bayesian approach and an extended FMECA method to support experts' knowledge renewal and maintenance actions effectiveness. In the proposed methodology, we capitalize and model experts' existing knowledge from FMECA files as an operational Bayesian network (O-BN) to provide real time feedback on poorly executed maintenance actions. The accuracy of O-BN is monitored through drift in maintenance performance measurement (MPM) indicators that results in learning an unsupervised Bayesian network (U-BN) to discover new causal relations from historical data. The structural difference between O-BN and U-BN highlights potential new knowledge which is validated by experts prior to modify existing FMECA and associated maintenance procedures. The proposed methodology is evaluated in a well reputed high-mix low-volume semiconductor production line to demonstrate its ability to dynamically renew experts' knowledge and improve maintenance actions effectiveness