Abstract
The paper firstly reviews the relevant concepts on virtual enterprise operations as well as industrial maintenance processes. Then a virtual enterprise enabling platform is presented. The architecture of the platform and its main modules are briefly introduced. Within this platform, a smart object extension is highlighted. This smart object is used to collect data from remote equipment and pass it to the Virtual Enterprise Management Platform (VEMP) through a gateway. The data collected by the smart object will be aggregated and monitored, using the business intelligence tools of the platform, enabling the implementation of maintenance strategies, rising fault conditions that will trigger a repair business process. In the final part of the paper, it is discussed a business case for a SME with worldwide operations.

Abstract
This paper focuses on process visualization that is applicable to managing a Virtual Factory (VF) business environment. It briefly provides all aspects of implementing the dashboard user interface that is to be used by the VF partners. The dashboard features state-of-The art business intelligence and provides data visualization, user interfaces and menus to support VF partners to execute collaborative processes. With advanced visualizations that produce quality graphics it offers a variety of information visualizations that brings the process data to life with clarity. This data visualization provides critical operational matrices (e.g. KPIs) required to manage virtual factories. Various technical aspects of this dashboard user interface portal are elaborated within the scope of this research such as installation instructions, technical requirements for the users and developers, execution and usage aspects, limitations and future works. The dashboard user interface portal presents different widgets according to the VF requirements that are to be needed to support the visualization and monitoring of various business processes within a VF. The research work highlighted in this paper is conceptualized, developed and validated within the scope of the European Commission NMP priority of the Seventh RTD Framework Programme for the ADVENTURE (ADaptive Virtual ENterprise ManufacTURing Environment) project. Copyright © 2014 SCITEPRESS - Science and Technology Publications.

Abstract
In the recent years, it has been confirmed both by theory and by practice that organisational models need to include networking strategies to cope with the current competitive environment. Different collaboration levels can characterise supply chains, virtual organisations (VO) and business communities; however, managing different networking scenarios is extremely important to allow SMEs to respond to market opportunities, ensuring a quick response, unique products with competitive prices and high product quality. This paper proposes an innovative methodological approach to support collaboration amongst SMEs for customised product design and manufacturing based on the VO concept. The work is based on mapping the methodology with the most important processes characterising the life of a VO and defining the operative practices to be performed within this type of network. This paper presents two case studies in the fashion industry, where the proposed approach for network management was tested and analysed.

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.