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
Small and medium-sized enterprises (SMEs) in Europe risk their competitiveness if they fail to embrace digitalization. Indeed, SMEs are aware of the need to digitalize-more than one in two SMEs are concerned that they may lose competitiveness if they do not adopt new digital technologies. However, a key obstacle is related with decision-makers' lack of awareness concerning digital technologies potential and implications. Some decision-makers renounce digital transition simply because they do not understand how it can be incorporated into the business. Take into account this common reality, especially among SMEs, this research project intends to identify the skills and subjects that need to be addressed and suggests the educational methodology and implementation strategy capable of maximizing its success. Therefore, and supported by a focused group research methodology, an innovative training program, oriented to decision-makers, was designed and implemented. The program was conceived based on a self-directed learning methodology, combining both asynchronous lecture/expositive and active training methodologies, strongly based on state-of-the-art knowledge and supported by reference cases and real applications. It is intended that the trainees/participants become familiar with a comprehensive set of concepts, principles, methodologies, and tools, capable of significantly enhancing decision-making capability at both strategic and tactical level. The proposed programme with a multidisciplinary scope explores different thematic chapters (self-contained) as well as cross-cutting thematic disciplines, oriented to the Industry 4.0 and digital transformation paradigm. Topics related with Digital Maturity Assessment, Smart Factories and Flexible Production Systems, Big Data, and Artificial Intelligence for Smarter Decision-Making in Industry and Smart Materials and Products, as well as new production processes for new business models. Each thematic chapter in turn is structured around a variable set of elementary modules and includes examples and case studies to illustrate the selected topics. A teaching-learning methodology centered on an online platform is proposed, having as a central element, a collection of videos complemented by a set of handouts that organize the set of key messages and take-ways associated with each module. In this paper, we present the design and practice of this training course specifically oriented to decision-makers in SME.

Abstract
More and more organizations are seeking to adopt organization and management models oriented to their key processes rather than the traditional functional orientation. However, as organizations are seeking to become more process-oriented, numerous gaps and difficulties are recognized at the level of analysis, modelling, management and improvement of processes. The issues surrounding processes are not properly understood and internalised, leading to increased difficulties in implementation and management by processes. There is thus a clear need for expertise in this area of knowledge. In response to this growing demand, in last year's we identify several universities and engineering schools incorporating specific curricular units in their teaching offer. This paper presents some education courses and specialized programs of the Faculty of Engineering of the University of Porto, specifically oriented to analysis, modelling, management and improvement of processes (engineering and business processes). Firstly, the concept of process and process thinking is presented. It will then present the approach followed in some curricular units incorporated in three Master of Science programs and also provides the design of a specialized program oriented to more experienced participants.

Abstract
Supply chains have become one of the most important strategic themes in the aerospace industry in recent years as globalization and deep technological changes have altered the industry at many levels, creating new dynamics and strategies. In this setting, sustainability at the supply chain level is an emerging research topic, whose contributions aim to support businesses into the future. To do so the development of new products and the response to new industry requirements, while incorporating new materials appears as a path to follow, which require more resilient and agile supply chains, while guaranteeing their sustainability. Such supply chains will be better prepared for the future complex challenges and risks faced by the aerospace companies. Such challenges are addressed in this work, where an integrated framework is proposed to contribute to the resilience and sustainability of aerospace supply chains. Using different analysis methods, the framework addresses four important challenges in the context of aerospace supply chain sustainability: evolution and new trends, performance assessment, supplier selection, and supply chain design and planning. © 2019, Springer Nature Switzerland AG.

Abstract
This paper introduces a new research focus for the problem of flow control. Most of the research until this point in this topic comes in the form of heuristics and flow control protocols, from which we can highlight Kanban and CONWIP. These protocols have as common ground the fact that both impact flow by limiting the amount of WIP (work in process) that circulates through a production route. These limits are not static in a sense that one limit defined for a given period will not suffice for all possible conditions the future may entail. Therefore, we need strategies to find which values for the WIP caps are best (according to an optimization target), given a production system state and a customer demand level. We propose the use of a Reinforcement learning (RL) agent and introduce the problem within the framework of a reinforcement learning problem, showing that for a simulated system it is possible to reduce WIP levels up to 43% without losses in throughput (TH). As an introduction to the flow control problem comparisons between push and pull systems are made resorting to the use of discrete event simulations. We simulated a CONWIP and a push protocol and comparisons are made in terms of cycle-time, throughput and customer lead-time. The work points-out that within the field of industrial management research terms such as cycle-time, customer lead-time, and lead-time are sometimes used interchangeably, which may lead to unnecessary confusion and hindered understanding of the subject matter. Specifically, we show that cycle-time reduction does not lead directly to customer lead-time reduction in a make to order environment.

Abstract
Small and medium-sized enterprises (SMEs) in Europe are conscious that their competitive position depends on their success to embrace digitalisation challenges. However, some decision-makers in companies discard digital transformation because they do not understand how it can be incorporated into their businesses. Therefore, academia, research centres, and technological clusters are responsible for building the infrastructures and providing the support and the training that will progressively change this mindset. This paper aims to report an experience on designing a training program to train the trainers under the digital transformation topic. To define strategies to understand better the companies (and professionals) needs and motivations and the requisites to deliver the training course, the focus group methodology was applied. In this paper, we present a training program methodology and structure that intend to respond to industrial requests and, in this way to accelerate the digital transformation of companies, especially SMEs.