Development of traceability solution for furniture components

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIn the contemporary context, characterized by intensified global competition and the constant evolution of the globalization landscape, it becomes imperative for industries, including Small and Medium Enterprises (SMEs), to undertake efforts to enhance their operational processes, often through digital technological adaptation. The present study falls within the scope of the project named “Wood Work 4.0,” which aims to infuse innovation into the wood furniture manufacturing industry through process optimization and the adoption of digital technologies. This project received funding from the European Union Development Fund, in collaboration with the North 2020 Regional Program, and was carried out at the Carpintaria Mofreita company, located in Macedo de Cavaleiros, Portugal. In this regard, this study introduces a software architecture that supports the traceability of projects in the wood furniture industry and simultaneously employs a system to identify and manage material leftovers, aiming for more efficient waste management. For the development of this software architecture, an approach that integrates the Fiware platform, specialized in systems for the Internet of Things (IoT), with an Application Programming Interface (API) specifically created to manage information about users, projects, and associated media files, was adopted. The material leftovers identification system employs image processing techniques to extract geometric characteristics of the materials. Additionally, these data are integrated into the company’s database. In this way, it was possible to develop an architecture that allows not only the capturing of project information but also its effective management. In the case of material leftovers identification, the system was able to establish, with a satisfactory degree of accuracy, the dimensions of the materials, enabling the insertion of these data into the company’s database for resource management and optimization.No contexto contemporâneo, marcado por uma competição global intensificada e pela constante evolução do cenário de globalização, torna-se imperativo para as indústrias, incluindo as Pequenas e Médias Empresas (PMEs), empreender esforços para aprimorar seus processos operacionais, frequentemente pela via da adaptação tecnológica digital. O presente estudo insere-se dentro do escopo do projeto denominado “Wood Work 4.0”, cujo propósito é infundir inovação na indústria de fabricação de móveis de madeira por meio da otimização de processos e da adoção de tecnologias digitais. Este projeto obteve financiamento do Fundo de Desenvolvimento da União Europeia, em colaboração com o programa Regional do Norte 2020 e foi realizado na empresa Carpintaria Mofreita, localizada em Macedo de Cavaleiros, Portugal. Nesse sentido, este estudo introduz uma arquitetura de software que oferece suporte à rastreabilidade de projetos na indústria de móveis de madeira, e simultaneamente emprega um sistema para identificar e gerenciar sobras de material, objetivando uma gestão de resíduos mais eficiente. Para o desenvolvimento dessa arquitetura de software, adotou-se uma abordagem que integra a plataforma Fiware, especializada em sistemas para a Internet das Coisas (IoT), com uma Interface de Programação de Aplicações (API) criada especificamente para gerenciar informações de usuários, projetos, e arquivos de mídia associados. O sistema de identificação de sobras de material emprega técnicas de processamento de imagem para extrair características geométricas dos materiais. Adicionalmente, esses dados são integrados ao banco de dados da empresa. Desta forma, foi possível desenvolver uma arquitetura que permite não só capturar informações de projetos, mas também gerenciá-las de forma eficaz. No caso da identificação de sobras de material, o sistema foi capaz de estabelecer, com um grau de precisão satisfatório, as dimensões dos materiais, possibilitando a inserção desses dados no banco de dados da empresa para gestão e otimização do uso de recursos

Internet-of-things (IOT) - technologies enabling efficient inbound and outbound logistics in engineer-to-order (ETO) manufacturing companies

Confidential until 23. May 202

Business process and technology lessons learned, recommendations and best practices for new adopters

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006."June 2006."Includes bibliographical references (leaves 117-118).This thesis focuses on documenting learnings from a RFID data exchange pilot in the fast moving consumer goods industry. The pilot we studied is a collaborative effort between two of the largest retailers in the world and five of their major suppliers, facilitated by EPCglobal and the MIT Auto-ID labs. Currently, manufacturers and suppliers are building the infrastructure to exchange EPC data to validate standards and proof of concepts for RFID adoption. The outcome of these pilots will essentially set the stage for large scale RFID adoption worldwide. Our thesis attempts to document issues relating to data exchange from business process, organizational and technical perspectives. We have synthesized the findings and consolidated the lessons learned during the pilot in an attempt to form a set of actionable recommendations for new companies looking to start on RFID pilot projects.by Rida Chan [and] Sangeeth Ram.M.Eng.in Logistic

Engineering framework for service-oriented automation systems

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

REAL-TIME LOGISTICS - Case Development of a Shipment Status Display System for a Large Manufacturing Company

As traditional heavy industry businesses transform into global solutions providers, their business models change into project-based and their supply networks expand. Project business faces greater uncertainty within the supply chain than traditional business, thus requires greater need for data exchange within the supply chain. Numerous information systems have provided the organization with a wealth of data. However project management often faces great challenges to utilize it for better visibility on project delivery status, as well as to communicate that to stakeholders. In response to the need for better usage and presentation of transactional project logistical data, a real-time shipment status display system has been developed. The complete system offers an intuitive, up-to-date, fast, and reliable display that is accessible through a wide range of devices. In this thesis, the system is customized to run on public displays. In term of development methodology, spiral axiomatic design approach is adopted to ensure maximum independence of components. The end result is a system comprising of two independent sub-systems: one is for data collection and the second one is for presentation. Modern web technologies such as ASP.NET MVC4, HTML5, and CSS3 have been used to develop the presentation sub-system. The thesis contributes a software artifact that complements information systems that are either too much focused on transactional data or unable to communicate project logistics data to stakeholders. It also demonstrates the use of axiomatic system design in developing modern web platforms.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A Hybrid Indoor Location Positioning System

Indoor location positioning techniques have experienced impressive growth in recent years. A wide range of indoor positioning algorithms has been developed for various applications. In this work a practical indoor location positioning technique is presented which utilizes off-the-shelf smartphones and low-cost Bluetooth Low Energy (BLE) nodes without any further infrastructure. The method includes coarse and fine modes of location positioning. In the coarse mode, the received signal strength (RSS) of the BLE nodes is used for location estimation while in the fine acoustic signals are utilized for accurate positioning. The system can achieve centimeter-level positioning accuracy in its fine mode. To enhance the system’s performance in noisy environments, two digital signal processing (DSP) algorithms of (a) band-pass filtering with audio pattern recognition and (b) linear frequency modulated chirp signal with matched filter are implemented. To increase the system’s robustness in dense multipath environments, a method using data clustering with sliding window is employed. The received signal strength of BLE nodes is used as an auxiliary positioning method to identify the non-line-of-sight (NLoS) propagation paths in the acoustic positioning mode. Experimental measurement results in an indoor area of 10 m2 indicate that the positioning error falls below 6 cm

IoT for Efficient Data Collection from Real World Resources

The Internet of Things is providing new ways of experiencing and reacting to the physical world through the ability of advanced electronic devices that collect data. At the same time, as new application scenarios are envisioned, with the assistance of information generated by sensors, new problems and obstacles will arise. This requires new development to meet business and technical requirements, such as interoperability between heterogeneous devices and confidence (such as validity, security and trust) over smart devices. With the increase of these complex requirements it becomes crucial to develop an infrastructure aimed at tackling such requirements mentioned. IoT middleware – a software layer that bridges the gap between devices and information systems. Thus, this work aims to study the mechanisms and methodology for data collection, devices interoperability and data filtering, closer to the data sources, in order to optimize the collection and pre-analysis of data that can then be used by various applications such as the ones in manufacturing industry