Abstract
In mature countries manufacturing is one of the most significant sources of economic development and growth. In those countries, manufacturing transformation systems will be grounded on seamless collaborative environments and will have a high degree of flexibility in production, in terms of product needs (specifications, quality, design), volume, timing, resource efficiency and cost, being able to adapt to customer needs and make use of the entire network chain for value creation. Future transformations systems will be massively collaborative and will be enabled by a network-centric approach, making use of multidimensional data analytics, driven by advanced ICT and the latest available proven manufacturing technologies.

Abstract
In make-to-order (MTO)/engineer-to-order (ETO) business environments multiple customer-oriented projects compete for and share resources through interdependent engineering and production activities. Deep knowledge of critical dimensions that affect performance is key in this context. For this, we propose a set of determinants - workload, complexity, outsourcing, design reuse, project type, and knowledge/experience with technology, that impact performance. These determinants are input to an extended hybrid simulation model using system dynamics (SD), discrete event simulation (DES) and agent-based simulation (ABS) that tackles the needs imposed by activities of very different nature, as the project development and manufacturing/assembly operations. The hybrid model is applied to the case of an advanced manufacturing company. Through Monte Carlo sampling, the influence of different combinations of determinants in the performance variability is assessed. A correlation analysis shows evidence of association between all performance determinants and the project time and cost, while no evidence of association between the design reuse and project type determinants and the manufacturing and assembly time.

Abstract
Despite the growing relevance of customization as a source of competitive advantage, the make-to-order (MTO)/engineer-to-order (ETO) manufacturing strategies have been neglected in the literature. Companies following these strategies deal with simultaneous customer-oriented projects that compete for and share resources, while coordinating interdependent engineering and production activities. It becomes relevant understanding the impact that different development projects and production variables have on the manufacturing system performance. For this, we propose a hybrid multi-dimensional simulation model, using System Dynamics (SD), Discrete Event Simulation (DES) and Agent-based simulation (ABS) for MTO/ETO performance assessment. (C) 2018 The Authors. Published by Elsevier B.V.

Abstract
The context of the fourth industrial revolution brings companies numerous challenges. Agility, flexibility, and responsiveness are necessary characteristics in this business ecosystem, with the deep insertion of digital technologies in manufacturing. Therefore, this work has the objective of developing a model of measurement of the maturity and readiness of the industry 4.0 so that companies can visualize their positioning in this new reality. Qualitative and quantitative methods were used for the development of the model that contemplates 6 (six) dimensions. It was applied in the Industrial Pole of Manaus (PIM), in a multinational manufacturing company. The results obtained are adherent to the reality of the company in relation to the industry 4.0. © 2019, IEOM Society International.

Abstract
The industry faces more and more the challenge of deploying and taking advantage of evidence-based strategic decisions to enhance profit gain. In this research, the possibility of having a fully integrated system composed by a simulator and an IoT platform with the capability of collecting real-time data from the shop floor and returning performance indicators to support decision making is evaluated. The suggested approach involves a Manufacturing Executing System (MES) producing a production schedule, an IoT Platform composed by a message broker and a real-time database, a Simulator including simulation software and a wrapper, and a user application serving as an interface between the user and the IoT Platform and Simulator integrated system. A detailed analysis of the functionalities and integration of the Simulator and the IoT Platform will also be explored. To evaluate the approach, one use case of a production line in the automotive industry is used. The application of the integrated IoT Simulation system permits its validation and consequent future work. © 2019, IEOM Society International.

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.