Abstract
Nowadays, it has been observed an increasing awareness and understanding on the subject of sustainable companies and business models, addressing multi-disciplinary approaches that cover not only economical problems, but also social and environmental challenges. Supply chains and especially collaborative networks managers are increasingly aware of these sustainability issues, continuously seeking to meet current human needs while preserving environmental safety. Only this way, focusing on its sustainable growing, it is possible to preserve companies' steadiness. In order to achieve this goal, sustainable networks must ensure that each partner is fully aligned and committed with economic, environmental and social axes that rule the network operational behaviour. Nevertheless, in order to achieve this level of maturity within such complex and turbulent environments, organisations need to improve the quality of their performance assessment approaches, integrating the different sustainability perspectives. To accomplish this, it is critical to establish specific indicators responsible to formalise and evaluate partners' behaviour, according to well-identified objectives, as well as fuse this information in a comprehensive and user-friendly way. This paper presents a new approach, based on a fuzzy logic-based algorithm, for sustainable network performance and risk assessment. © 2016 Inderscience Enterprises Ltd.

Abstract
Since the automotive industry is one of the most competitive markets, key players should be capable of evolving their management strategy from a reactive to proactive approach. This way, it is critical for this type of companies to explore a new performance management approach where an effective interaction between the strategic and operational layers should be achieved. In line with this vision, a framework is presented that helps stakeholders make decisions based on the ability to anticipate future performance behaviours. Using leading indicators as reference, the key idea is to structure and model the existing knowledge within a mathematical tool, in order to project the manufacturing system's behaviour into the future. In order to show the reliability and importance of this framework, this paper presents a research performed at an automotive plant. The aim is to model the factors affecting energy consumption and thus estimate the future behaviour in terms of sustainability issues.

Abstract

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
Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.