Additive manufacturing (AM) technology has attracted the interest of industrial professionals and researchers in the last years. This interest lies primarily in understanding the trends, benefits, and implications of AM technology on supply chain (SC) and logistics, as it requires reconfiguring the supply chain based on a distributed manufacturing strategy, closer to the consumer market, with shorter lead times and less raw materials. It still is an emerging field, and needs further study. Therefore, a better understanding of main trends will contribute to the dissemination of knowledge about AM technology and its consolidation. This article seeks to investigate the implications of AM, as an advanced manufacturing model, on SC and logistics. A four-step research method was used to develop a systematic literature review and a bibliometric analysis on the AM implications in SC and logistics. The main implications of AM on SC and logistics were classified in seven key issues gathered as result of the literature review. Additionally, bibliometric study allowed understanding researches major trends in this field. The key aspects highlighted and characterized as major implications of AM on SC and logistic are: supply chain complexity reduction; more flexible logistics and inventory management; better spreading and popularization of mass customization; decentralization of manufacturing; greater design freedom and rapid prototyping; increasing of resource efficiency and sustainability, and the need to have clearly defined legal and safety aspects.

This paper aims to review the state-of-the-art in Cyber-Physical Systems (CPS) as a key element to Industry 4.0 and to provide engineering managers with adequate and sufficient knowledge about CPS, highlighting their characteristics, technologies, capabilities, and applications. A three-stage research method was applied to provide an extensive search and systematic literature review (SLR). We identified, selected, and reviewed 218 publications. The convergences in specific areas in which studies are still needed were detailed. We identified 5 characteristics, 7 capabilities, 17 related technologies, and 11 areas of application. Four application areas remain as an opportunity to be explored in future research, such as environmental sustainability; smart and precision agriculture; smart construction, and social sciences. This article is the first to present a wide-ranging review of CPS and bringing together a significant number of publications. Its originality is focused on detailing and analyzing CPS characteristics, technologies, and multidisciplinary applications allowing engineering professionals and managers to identify which technologies a manufacturing equipment should integrate, as well as the level of development of each of them to achieve its full operation in Industry 4.0 environment. The main limitation of this work is the fact that new features, applications, and technologies will certainly appear soon.

The concept of Quality by Design (QbD) has been widely used by the pharmaceutical industry since 2004, changing the focus on inspections to embrace greater control in the manufacturing process. This new approach contributed to lower production costs and higher quality of medicines. This work discusses the first steps towards the implementation of a QbD approach aimed at ensuring the quality of a biomedical device under development. This device will be applied as a non-magnetic metallic foreign body localization system for surgical removal guidance. It should be highlighted that a new approach was used to define the Design Space, based on fuzzy logic rather than the typical statistical techniques.

Since 2004, on the recommendation of the Food and Drug Administration (FDA), the concept of Quality by Design (QbD) has been widely applied by the pharmaceutical industry in drug development. The paradigm for quality assurance has been changed from inspections tests to focusing on increasing control within manufacturing processes. Recently, the methodology of QbD has gained space in the area of development of transducers for biomedical application. In order to advance on the adoption of this approach to the characteristics and needs of this new field, a methodology of representation of a multi-parameter Design Space with fuzzy inference was developed.