Abstract
Although interest in FinTech businesses has been growing, research about these companies is still scarce. To address this gap, this paper aims to understand the evolution of the FinTechs ecosystem, through a socio-technical system theory and service innovation lense. A case study research methodology was used, in which 6 Brazilian and 5 Portuguese FinTechs were analyzed. Primary data was collected using semi-structured interviews with managers and employees of the startups, while secondary data was obtained through the analysis of reports from consulting firms and public relations materials of the startups. Results show the evolution of FinTech ecosystems from the perspective of socio-technical system theory and service innovation. From the socio-technical system perspective it was possible to understand the roles of social, technological and organizational actors in the evolution of these ecosystems. From the service innovation perspective, it was possible to understand the dynamics of the evolution of the FinTech ecosystems and its results.

Abstract
The topic of Smart Cities involves various aspects of convergence and support to necessarily safe activities, which are dependent on reliable data and reliable sources. The answer could be provided by means of the adoption model of Blockchain technologies for this purpose, applied to the different characteristics of the city from the electronic governance, the contracting of products and services, and the sensing or data collection. The multiple IoT objects and the multiple networks, together with the Blockchain technology, may result in safer and more efficient cities. This paper analyzes and deepens the concept of Smart Cities using Blockchain technology as a platform for safety and reliability of the data. It is intended to reflect critically on this topic, looking through the different aspects involving this concept and the way in which it can implement. It analyzes the Smart Cities and its ecosystems with the generic data models, integrated, with quality data and trusted information.

Abstract
The classical multi-level lot-sizing and scheduling problem formulations for process industries rarely address perishability issues, such as limited shelf lives of intermediate products. In some industries, ignoring this specificity may result in severe losses. In this paper, we start by extending a classical multi-level lot-sizing and scheduling problem formulation (MLGLSP) to incorporate perishability issues. We further demonstrate that with the objective of minimising the total costs (purchasing, inventory and setup), the production plans generated by classical models are often infeasible under a setting with perishable products. The model distinguishes different perishability characteristics of raw materials, intermediates and end products according to various industries. Finally, we provide quantitative insights on the importance of considering perishability for different production settings when solving integrated production planning and scheduling problems.

Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.

Abstract
The concept of industry 4.0 (i4.0) encompasses the integration of different technologies into an autonomous, knowledge- and sensor-based, self-regulating production system. Our objective is to synthesize which are the challenges and opportunities of adopting i4.0 from the perspective of technology provider companies. A single-case research was conducted with ten companies at the Portuguese Production Technologies Cluster. Based on i4.0 technologies – Augmented reality; Additive Manufacturing; Big Data; Cloud Computing; Cyber-Physical Systems; Cybersecurity; Smart Robotics; Simulation; and System Integration – interviewees mentioned that the main adoption challenges are the analysis of data generated, integration of new technologies with available equipment and workforce, and computational limitations. The main opportunities are improvements in: efficiency; flexibility; productivity; cybersecurity; quality of products and services; and decision process due to data analysis. Interviewees have also foreseen changes in company's business model through the integration of internal resources with complementary activities of their partners and other cluster companies. © 2019 Elsevier Inc.

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.