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.

Abstract
The IFIP WG 5.5 Working Conferences on Virtual Enterprises (PRO-VE) has created, in 14 editions, a remarkable scientific and professional community intersecting several disciplines and resulting in a new one: collaborative networks. In this paper we present the results of a retrospective study of the conceptual system evolution of PRO-VE providing an instrument to reflect about the field's past and future. Our approach was socio-semantic trying to devise the influence between the researchers social network and the evolution of the conceptual system. Firstly, we made a terminological analysis of every PRO-VE proceeding resulting in a picture of the main concepts used in each edition and their relative importance. Then, we used social network analysis techniques to conclude about the influence of the researchers on the conceptual system evolution. The results suggest a relatively stable set of concepts influenced by a network of core researchers. However, some marked evolution in the relative importance of the concepts can be identified.

Abstract
In the ambit of Distribute/Agile/Virtual Enterprises, the resources/partners selection process is a critical issue in order to guarantee the success of such enterprises. The selection process is complex in the large sense of the word and for that we advocate the necessity of a broker to perform that task, conveniently assisted by a tool. In order to contribute to its construction, this paper presents the resolution of a resources system selection problem, designated by Dependent or Integral Selection Method without Pre-selection of Transport Resources, with two algorithms, an exact solution algorithm and an approximate one. The results demonstrate that the exact solution algorithm limitations can be covered by the approximate algorithm. With those results, the broker has the knowledge to perform the selection with the most adequate algorithm for each case of the problem (depending of the number of tasks and pre-selected resources) addressed in this paper. This paper brings a contribution to broker performance for the selection process. (C) 2014 The Authors. Published by Elsevier Ltd.