Abstract
This research provides mechanisms that facilitate to monitor and manage of Virtual Enterprise (VE) collaborative business processes in an efficient and effective way. First, it shows a self-contained process monitoring tool specification that contains the following main functionalities: events capturing from a workflow engine, business activity monitoring, process analytics and monitoring rules definition and evaluation. An interactive user interface layer in the form of dashboard is then highlighted within the scope of this research with the objective to monitor the VE operational processes. The dashboard will be the integration platform for a set of components that allow the establishment and operation of VE successfully. This platform enables a seamless integration of business processes and provides an endto-end ICT solution among the VE member organizations. The work presented in this paper is developed within the scope of the European Commission NMP priority of the Seventh RTD Framework Programme for the ADVENTURE (ADaptive Virtual ENterprise ManufacTURing Environment) project. © Springer International Publishing Switzerland 2013.

Abstract
In many manufacturing systems human resources are essential in some cognitive intensive tasks while the more repetitive ones are assigned to automatic systems. If on the one hand, automation has a deterministic pace; humans are known by a flexible and variable work manner. Therefore, a reliable description of both hardware and human components is required for designing such manufacturing systems. The purpose of this paper is to investigate the impact of the variable throughput of a manual process in a production flow that contains automatic processes upstream and downstream. With regard to the description of human behavior, two sources of variability were considered: natural and abnormal variability. Natural variability refers to the differences in terms of processing times that can be found among individuals. Organizational aspects such delays in shift changing and breaks along the shift, are referred as abnormal variability, and were also investigated by means of an analytical and simulation models.

Abstract
Controlling, maintaining, and improving quality is a central topic in manufacturing. Total Quality Management (TQM) provides several tools and techniques to deal with quality related topics, which are not always applicable. With the increased use of Information Technology (IT) in manufacturing there is a higher availability of data with great potential of further improvements. At the same time this results in higher requirements for data storage and processing with demanding, time consuming sessions for interpretation. Without suitable tools and techniques knowledge remains hidden in databases. This paper presents a methodology to help analyzing root causes of nonconformities (NCs) through a pattern identification approach. Hereby a methodology of Knowledge Discovery in Databases (KDD) is adapted and used as a quality tool. As the core element of the KDD methodology, the data mining, a well-known statistical measure from the field of economics—the Herfindahl–Hirschman Index (HHI)—is integrated. After presenting the theoretical background a new methodology is proposed and validated through an application case of the automotive industry. Results are obtained and presented in the form of patterns in matrices. They suggest that concentration indices may indicate possible root causes of NCs and invite for further investigations. © Springer International Publishing Switzerland 2013.

Abstract
Due to the increasing globalization process and the current economic situation, the power has shifted from the producer to the costumer, forcing companies to become more aware of the market needs. In order to become more customer-oriented, companies have been enhancing their management capabilities by shifting from a functional oriented approach to a process centered strategy, where core inter-firms processes can be seamlessly monitored and controlled. Since it is not possible to manage a system if its performance cannot be measured continuously during its entire life cycle, it is necessary to explore flexible and agile performance measurement and management systems as they are important tools capable of supporting the achievement of the strategic goals on the operational side. In the recent years several research projects have developed techniques and tools that support the collaboration. However they are restricted to the business level. In order to achieve the goals with the best performance, innovative and appropriate process monitoring and control mechanisms are needed. Consequently, this research provides an innovative solution that facilitates not only the gathering of operational and strategic information but also the assessment of collaborative manufacturing processes behavior following a fuzzy approach. © Springer International Publishing Switzerland 2013.

Abstract
Currently, performance analysis on complex manufacturing systems is performed in an ad hoc way since the main objective is to verify if the strategy designed has been helping companies achieve their targets following a reactive approach. However, more and more companies are performing in competitive markets, forcing them to become more proactive than reactive. This way, a simple approach is no longer suitable for this type of companies, and a stronger and effective interaction between the strategic and operational layers is key. Therefore, this research proposes a framework composed of both qualitative and quantitative methods that allow decision-makers to better understand their production system. Moreover, using key leading indicators as reference, the idea is to provide companies with the ability to anticipate future performance behaviors based not only on the knowledge acquired, but also on a mathematical tool that will synthesize this knowledge and infer future performance behaviors. This paper explores a critical issue for contemporary industrial organizations and sustainability issues concerning energy consumption. In this scope, an important research was performed aiming at modeling and understanding the normal behavior of electricity consumption, as well as the factors affecting energy consumption in the painting line of an automotive plant. © Springer International Publishing Switzerland 2013.

Abstract
Nowadays, business models play a key role in competitiveness. Each industry has specific needs regarding the customization of their business models. Through a personalized business model, organizations can enjoy a customized mapping of all the business activities. In the machinery industry domain and specifically producers of integrated Products and Services, the need for a customized business model has been growing due to the specifications of the industry. The existing business models do not satisfy the capital goods companies’ needs, therefore a study was conducted to analyze and understand companies’ specifications, the existing supporting business frameworks to further proceed with the creation of a new methodology and framework that supports the businesses of this specific industry.