Abstract

Abstract
Innovation networks are seen as an important opportunity for organizational performance by facilitating the creation of new knowledge, not just transferring existing knowledge. Collaboration in innovation between manufacturing companies and research centers is a trend that continues to grow in importance linked to business success. Embedded in the literature on business models in the context of networks organizations, this paper propose key pillars and elements required in order to support the establishing of open business model for innovation networks. Subsequently the use of these elements in practice was verified through empirical research evidence in a case study.

Abstract
To cope with today market challenges and guarantee adequate competitive performances, companies have been decreasing their products life cycles, as well as increasing the number of product varieties and respective services available on their portfolio. Consequently, it has been observed an increasing in complexity in all domains, from product and process development, factory and production planning to factory operation and management. This reality implies that organizations should be able to compile and analyze, in a more agile way, the immense quantity of data generated, as well as apply the suitable tools that, based on this knowledge, will supports stakeholders to take decision envisioning future performance scenarios. Aiming to pursuing this vision was developed a proactive performance management framework, composed by a performance thinking methodology and a performance estimation engine. While the methodology developed is an extension of the Systems Dynamics approach for complex systems' performance management, on the other hand, the performance estimation engine is an innovative IT solution responsible by capturing lagging indicators, as well as estimate future performance behaviors. As main outcome of this research work, it was demonstrated that following a systematic and formal approach, it is possible to identify the feedback loops and respective endogenous and exogenous variables responsible by hindering the systems behavior, in terms of a specific KPI. Moreover, based on this enhanced understanding about manufacturing systems behavior, it was proved to be possible to estimate with high levels of confidence not only the present but also future performance behavior. From the combination of both qualitative and quantitative approaches, it was explored an enhanced learning machine algorithm capable to specify the curve of behavior, characteristic from a specific manufacturing system, and thus estimate future behaviors based on a set of leading indicators. In order to achieve these objectives, both Neural Networks and Unscented Kalman Filter for nonlinear estimation were applied. Important results and conclusions were extracted from an application case performed within a real automotive plant, which demonstrated the feasibility of this research towards a more proactive management approach.

Abstract
The process of establishing the ontology objectives by the stakeholders is fundamental for the ontology success. This process is unstructured by nature, being a continuum from the initial discussion of the purpose to the first agreed conceptual representation. The inherent (inter) subjectivity of the process and their outcomes together with an excessive informality are perhaps the reasons for being overlooked in the literature. This paper proposes an approach integrating competency questions (CQ) and conceptual graphs to the support of domain experts and knowledge specialists in defining the purpose and fundamental conceptual commitments of the ontology to be developed. The approach was experimented and validated with experts in a project in the chemical-pharmaceutical industry.

Abstract

Abstract
Project-based organizations (PBO) are nowadays widespread in almost all the activity sectors. This type of organizations poses complex problems for information and knowledge management due to the fragmentation and lack of uniformity of organizational structures, processes, practices, and technologies. The ineffectiveness of knowledge sharing over time, between project teams, is perhaps the most prominent issue that PBO must deal with. This strongly affects organizational learning, which seems to under-deliver value to PBO. Therefore, relevant knowledge is trapped in an "informational limbo" out of reach, not being capitalized for the organization. This is particularly true in research and development (R&D) institutions, where knowledge sharing can be hindered by conceptual misunderstandings resulting from different disciplines, cultures and ways of working of project participants. This paper addresses such issues by analyzing, in a comprehensive way, how information and knowledge management can better suit project team's needs and at the same time improve organizational learning. An ethnographic study, based on immersed participant observation, is performed at a Portuguese R&D Institute, in order to understand the link between the way information is managed in a project and how people interact and learn by sharing knowledge between projects. Results provide a set of enterprise information management (EIM) recommendations. Findings also suggest that a PBO-wide EIM strategy, balancing knowledge codification and personalization mechanisms, is a feasible solution to overcome the problems of knowledge sharing in PBO. (C) 2014 Published by Elsevier Ltd.