An investigation upon Industry 4.0 implementation: the case of small and medium enterprises and Lean organizations

In recent years, industries have undergone several shifts in their operating and management systems. Alongside to the technological innovation, rapid market changes and high competitiveness; growing customer needs are driving industries to focus on producing highly customized products with even less time to market. In this context, Industry 4.0 is a manufacturing paradigm that promises to have a great impact not only on improving productivity but also on developing new products, services and business models. However, the literature review has shown that research on Industry 4.0 implementation is still characterized by some weaknesses and gaps (e.g., topics such as the implementation of Industry 4.0 in SMEs and its integration with Lean Management approach). Motivated by so, this thesis sought to answer four key questions: (RQ1) What are the challenges and opportunities for SMEs in the Industry 4.0 field? (RQ2) What are the resources and capabilities for Industry 4.0 implementation in SMEs? (RQ3) How can these resources and capabilities be acquired and/or developed and (RQ4) How to integrate Industry 4.0 and Lean Management? To deal with the first research question, a semi-systematic literature review in the Industry 4.0 field was conducted. The main goal is to explore the implementation of Industry 4.0 in SMEs in order to identify common challenges and opportunities for SMEs in the Industry 4.0 era. To face with the second and third research questions, a multiple case study research was conducted to pursue two main aims: (1) to identify the resources and capabilities required to implement Industry 4.0 in Portuguese SMEs. Furthermore, based on mainstream theories such as resource-based view (RBV) and dynamic capability theory, it sought empirical evidence on how SMEs use resources and capabilities to gain sustainable competitive advantage; (2) to shed light on how those SMEs acquire and/or develop the Industry 4.0 resources and capabilities. Finally, this thesis employed a semi-systematic literature review methodology to deal with the fourth research question. As such, it explored the synergistic relationship between Industry 4.0 and Lean Management to identify the main trends in this field of research and, ultimately, the best practices. The analysis and discussion of the best practices revealed a set of potential relationships which provided a more clear understanding of the outcomes of an Industry 4.0-LM integration.Nos últimos anos, as indústrias têm passado por várias mudanças tanto nos seus sistemas operacionais, como de gestão. Juntamente com a inovação tecnológica e alta competitividade; as mudanças nas necessidades dos clientes levaram as indústrias a se concentrarem na produção de produtos altamente personalizados e com tempo de lançamento no mercado cade vez menores. Nesse contexto, a Indústria 4.0 é um paradigma de manufatura que promete ter um grande impacto não só na melhoria da produtividade, mas também no desenvolvimento de novos produtos, serviços e modelos de negócios. No entanto, a revisão da literatura mostrou que a investigação sobre a implementação da Indústria 4.0 ainda é caracterizada por algumas lacunas (por exemplo em tópicos como a implementação da Indústria 4.0 em pequenas e médias empresas (PMEs) e sua integração com a filosofia de gestão Lean Management). Diante disso, esta tese procura responder à quatro questões-chave: (RQ1) Quais são os desafios e oportunidades para as PMEs no campo da Indústria 4.0? (RQ2) Quais são os recursos e capacidades necessários para a implementação da Indústria 4.0 nas PMEs? (RQ3) Como esses recursos e capacidades podem ser adquiridos e/ou desenvolvidos e (RQ4) Como integrar os paradigmas de manufatura, Indústria 4.0 e Lean Management? Para responder à primeira questão de investigação, este trabalho empregou uma revisão semi-sistemática da literatura. O objetivo principal foi explorar a implementação da Indústria 4.0 nas PMEs, a fim de identificar quais são os desafios e oportunidades para as PMEs na era da Indústria 4.0. Para fazer face à segunda e terceira questões de investigação, foi realizado um estudo de caso em 5 PMEs localizadas em Portugal a fim de atingir os seguintes objetivos: (1) identificar os recursos e capacidades necessários para implementar a Indústria 4.0 nas PME portuguesas; (2) esclarecer como essas PMEs adquirem e/ou desenvolvem esses recursos e capacidades. Além disso, com base nas teorias resourcebased view (RBV) e dynamic capabilities, buscar evidências empíricas sobre como as PMEs usam recursos e capacidades para obter vantagem competitiva sustentável. Finalmente, para lidar com a quarta questão de investigação, este estudo explorou a relação sinérgica entre a Indústria 4.0 e a filosofia de gestão Lean Management (LM) para identificar as principais tendências neste campo de investigação e promover as melhores práticas. A análise e discussão das melhores práticas revelaram um conjunto de potenciais relações, o que contribuiu para um entendimento mais claro sobre a integração da Indústria 4.0 com LM

Economic aspects of automation innovations in electronic transportation management systems

This paper presents an analysis of economic aspects of three selected automation innovations in electronic Transportation Management Systems: Maritime Transport Chain solution and Vessel Estimated Time of Arrival solution (related to the maritime transport) and Delivery Planning solution (related to the transport in general). The theoretical background of transportation, Transportation Management Systems, maritime transportation and seaports is provided, focusing on the economic aspects. A literature review has been conducted, in order to identify the research gap and to focus on the economic aspects of the selected automation innovations. A SWOT Analysis of the Maritime Transport Chain solution, Vessel Estimated Time of Arrival solution and Delivery Planning solution (from an internal and external perspective) is presented, adding to the existing research of the economic aspects of automation innovations in the transport sector

Analysis of distributed ledger technologies for industrial manufacturing

In recent years, industrial manufacturing has undergone massive technological changes that embrace digitalization and automation towards the vision of intelligent manufacturing plants. With the aim of maximizing efficiency and profitability in production, an important goal is to enable flexible manufacturing, both, for the customer (desiring more individualized products) and for the manufacturer (to adjust to market demands). Manufacturing-as-a-service can support this through manufacturing plants that are used by different tenants who utilize the machines in the plant, which are offered by different providers. To enable such pay-per-use business models, Distributed Ledger Technology (DLT) is a viable option to establish decentralized trust and traceability. Thus, in this paper, we study potential DLT technologies for efficient and intelligent integration of DLT-based solutions in manufacturing environments. We propose a general framework to adapt DLT in manufacturing, and then we introduce the use case of shared manufacturing, which we utilize to study the communication and computation efficiency of selected DLTs in resource-constrained wireless IoT networks

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.<br/

Blockchain and internet of things for electrical energy decentralization: A review and system architecture

Decentralization in electrical power grids has gained increasing importance, especially in the last two decades, since transmission system operators (TSO), distribution system operators (DSO) and consumers are more aware of energy efficiency and energy sustainability issues. Therefore, globally, due to the introduction of energy production technologies near the consumers, in residential and industrial sectors, new scenarios of decentralized energy production (DEP) are emerging. To guarantee an adequate power management in the electrical power grids, incorporating producers, consumers, and producers-consumers, commonly designated as prosumers together, it is important to adopt intelligent systems and platforms that allow the provision of information on energy consumption and production in real time, as well as for obtaining the price for the sale and purchase of energy. In this research the literature is analysed to identify the appropriate solutions to implement a decentralized electrical power grid based on sensors, blockchain and smart contracts, evaluating the current state of the art and pilot projects already in place. A conceptual model for a power grid model is presented, with renewable energy production, combining Internet of Things (IoT), blockchain and smart contracts.A descentralização nas redes elétricas ganhou importância crescente, especialmente nas últimas duas décadas, uma vez que os operadores da rede de transporte (ORT), operadores da rede de distribuição (ORD) e consumidores estão mais conscientes das questões de eficiência energética e sustentabilidade energética. Globalmente, devido à introdução de tecnologias de produção de energia junto dos consumidores, nos setores residencial e industrial, estão a surgir novos cenários de produção de energia descentralizada. Para garantir uma adequada gestão de energia nas redes elétricas, integrando produtores, consumidores e produtores-consumidores, vulgarmente designados por prosumers, é importante adotar sistemas e plataformas inteligentes que permitam fornecer informações sobre consumo e produção de energia em tempo real, bem como para obter o preço de compra e venda de energia. Nesta pesquisa, a literatura é analisada para identificar as soluções adequadas para implementar uma rede elétrica descentralizada baseada em sensores, blockchain e contratos inteligentes, avaliando o estado da arte atual e projetos piloto já em curso. É apresentado um modelo conceptual para um modelo de rede elétrica, com produção de energia renovável, combinando Internet das Coisas (IoT), blockchain e contratos inteligentes

Blockchain technology research and application: a systematic literature review and future trends

Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently. Blockchain serves as an immutable distributed ledger technology which allows transactions to be carried out credibly in a decentralized environment. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability, security and other issues waiting to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We begin with a summary of the development of blockchain, and then give an overview of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspective of academic research and industry technology. Furthermore, technical challenges and recent developments are also briefly listed. We also looked at the possible future trends of blockchain

Integrating Green Lean Six Sigma and Industry 4.0: A Conceptual Framework

This research aims to propose a framework to integrate Green Lean Six Sigma (GLSS) and Industry 4.0 to improve organizational sustainability. The integration of GLSS and Industry 4.0 is proposed based on theoretical facets of the individual approaches. A generic, conceptual framework of an integrated GLSS-Industry 4.0 approach is then proposed using the application of different tools and techniques of GLSS and Industry 4.0 at different stages of the realization of a project. Both approaches have common facets related to enablers and barriers, and the integrated application of tools and techniques of each approach supplements the common focus of both related to sustainability enhancement. The proposed, conceptual framework provides systematic guidelines from the project selection stage to the sustainment of the solution, with the enumerated application of different techniques and tools at each step of the framework. This research is the first of its kind to propose the integration of GLSS and Industry 4.0 under the umbrella of a unified approach, including a conceptual framework of this integrated GLSS-Industry 4.0 approac

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine