A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

A conceptual framework for smart production planning and control in Industry 4.0

[EN] This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.The research leading to these results received funding from the European Union H2020 Program with grant agreements No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" and No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)"; the "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) project granted by the Valencian Regional Government; and the PAI-12-21 open-access support from the Universitat Politecnica de Valencia.Cañas, H.; Mula, J.; Campuzano-Bolarín, F.; Poler, R. (2022). A conceptual framework for smart production planning and control in Industry 4.0. Computers & Industrial Engineering. 173:1-12. https://doi.org/10.1016/j.cie.2022.10865911217

Industry 4.0 and digitization towards job satisfaction of organizations in Tampico, Tamaulipas, Mexico

La industria 4.0 está relacionada en cada lugar y con el uso y la implementación de nuevas tecnologías para la mejora continua de los procesos administrativos. Como parte del crecimiento de una organización, es importante que día a día se adapte a los cambios tecnológicos que afectan las operaciones de los trabajadores o la seguridad laboral. Algunos de los elementos que abarca esta industria son el uso de equipos autónomos, robótica, simuladores de procesos, impresoras 3D, inteligencia artificial y equipos que comparten información en tiempo real. El objetivo de este artículo consiste en valorar los procesos de digitalización de las organizaciones de la ciudad de Tampico, Tamaulipas (México), desde el punto de vista del usuario, para identificar los factores determinantes de la satisfacción laboral. Se emplea la técnica multivariante de regresión de mínimos cuadrados parciales, considerando como factores de análisis de digitalización y su relación con el modelo de relación y colaboración, habilidades y competencias profesionales, formación digital y procesos de digitalización. Los resultados muestran que el grado de motivación aumenta con el incremento en la digitalización de los procesos y que la formación digital y las competencias profesionales necesitan aumentar gradualmente para tener un impacto positivo en relación con los procesos de digitalización.Industry 4.0 is related in each place and with the use and implementation of new technologies for the continuous improvement of administrative processes. As part of the growth of an organization, it is important that day by day it adapts to technological changes that affect worker operations or job security. Some of the elements that this industry encompasses are the use of autonomous equipment, robotics, process simulators, 3D printers, artificial intelligence and equipment that share information in real time. The objective of this article is to assess the digitization processes of organizations in the city of Tampico, Tamaulipas (Mexico), from the user's point of view, to identify the determining factors of job satisfaction. The multivariate technique of partial least squares regression (or PLS, by Partial Least Squares (or SEM, by Structural Equation Models) is used, considering as digitization analysis factors and their relationship with the relationship and collaboration model, skills and competencies professionals, digital training and digitization processes. The results show that the degree of motivation increases with the increase in the digitization of processes and that digital training and professional competencies need to increase gradually to have a positive impact in relation to the processes of digitization.Universidad Pablo de Olavid

Smart Sensor Architectures for Multimedia Sensing in IoMT

[EN] Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called Interactive, which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%.This work has been supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00), which is partially funded by AEI, FEDER and EU.Silvestre-Blanes, J.; Sempere Paya, VM.; Albero Albero, T. (2020). Smart Sensor Architectures for Multimedia Sensing in IoMT. Sensors. 20(5):1-16. https://doi.org/10.3390/s20051400S116205Bangemann, T., Riedl, M., Thron, M., & Diedrich, C. (2016). Integration of Classical Components Into Industrial Cyber–Physical Systems. Proceedings of the IEEE, 104(5), 947-959. doi:10.1109/jproc.2015.2510981Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017). The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0. IEEE Industrial Electronics Magazine, 11(1), 17-27. doi:10.1109/mie.2017.2649104Salehi, M., & Ejlali, A. (2015). A Hardware Platform for Evaluating Low-Energy Multiprocessor Embedded Systems Based on COTS Devices. IEEE Transactions on Industrial Electronics, 62(2), 1262-1269. doi:10.1109/tie.2014.2352215Alvi, S. A., Afzal, B., Shah, G. A., Atzori, L., & Mahmood, W. (2015). Internet of multimedia things: Vision and challenges. Ad Hoc Networks, 33, 87-111. doi:10.1016/j.adhoc.2015.04.006Jridi, M., Chapel, T., Dorez, V., Le Bougeant, G., & Le Botlan, A. (2018). SoC-Based Edge Computing Gateway in the Context of the Internet of Multimedia Things: Experimental Platform. Journal of Low Power Electronics and Applications, 8(1), 1. doi:10.3390/jlpea8010001Memos, V. A., Psannis, K. E., Ishibashi, Y., Kim, B.-G., & Gupta, B. B. (2018). An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework. Future Generation Computer Systems, 83, 619-628. doi:10.1016/j.future.2017.04.039Chianese, A., Piccialli, F., & Riccio, G. (2015). Designing a Smart Multisensor Framework Based on Beaglebone Black Board. Lecture Notes in Electrical Engineering, 391-397. doi:10.1007/978-3-662-45402-2_60Wang, W., Wang, Q., & Sohraby, K. (2016). Multimedia Sensing as a Service (MSaaS): Exploring Resource Saving Potentials of at Cloud-Edge IoTs and Fogs. IEEE Internet of Things Journal, 1-1. doi:10.1109/jiot.2016.2578722Munir, A., Gordon-Ross, A., & Ranka, S. (2014). Multi-Core Embedded Wireless Sensor Networks: Architecture and Applications. IEEE Transactions on Parallel and Distributed Systems, 25(6), 1553-1562. doi:10.1109/tpds.2013.219Baali, H., Djelouat, H., Amira, A., & Bensaali, F. (2018). Empowering Technology Enabled Care Using IoT and Smart Devices: A Review. IEEE Sensors Journal, 18(5), 1790-1809. doi:10.1109/jsen.2017.2786301Kim, Y. G., Kong, J., & Chung, S. W. (2018). A Survey on Recent OS-Level Energy Management Techniques for Mobile Processing Units. IEEE Transactions on Parallel and Distributed Systems, 29(10), 2388-2401. doi:10.1109/tpds.2018.2822683Chaib Draa, I., Niar, S., Tayeb, J., Grislin, E., & Desertot, M. (2016). Sensing user context and habits for run-time energy optimization. EURASIP Journal on Embedded Systems, 2017(1). doi:10.1186/s13639-016-0036-8Chen, Y.-L., Chang, M.-F., Yu, C.-W., Chen, X.-Z., & Liang, W.-Y. (2018). Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems. Sensors, 18(9), 3068. doi:10.3390/s18093068Tamilselvan, K., & Thangaraj, P. (2020). Pods – A novel intelligent energy efficient and dynamic frequency scalings for multi-core embedded architectures in an IoT environment. Microprocessors and Microsystems, 72, 102907. doi:10.1016/j.micpro.2019.10290

A indústria 4.0 no Brasil : um estudo dos benefícios esperados e tecnologias habilitadoras

A Indústria 4.0 surge com o objetivo de desenvolver fábricas inteligentes, com alto grau de autonomia e flexibilidade, através da adoção de tecnologias digitais de forma integrada nas empresas e suas cadeias de valor. Ao mesmo tempo, a Indústria 4.0 promove benefícios que vão além da performance operacional, como o desenvolvimento de novas ofertas e novos modelos de negócios para as empresas. A Indústria 4.0 é originada na Alemanha, país com alta performance tecnológica, e rapidamente inspira outras iniciativas no mundo inteiro, inclusive em países emergentes como o Brasil. Estes países possuem maiores barreiras para a adoção das tecnologias relacionadas ao conceito, principalmente devido à atual situação tecnológica dos seus parques industriais. Embora a Indústria 4.0 seja um tema crescente na literatura, ainda existem grandes lacunas de estudo sobre a adoção de tecnologias relacionadas ao conceito no contexto de países emergentes, principalmente por se tratar de uma iniciativa recente. Logo, o objetivo desta dissertação é estudar o conceito da Indústria 4.0 no Brasil, de forma a entender quais são os benefícios do conceito para a performance industrial e as tecnologias habilitadoras. O trabalho tem uma abordagem quantitativa, com análises estatísticas aplicadas em dados de pesquisas surveys conduzidas em nível nacional. Os principais resultados obtidos foram: (i) identificação da relação entre as tecnologias e os benefícios esperados do conceito, (ii) identificação de disparidades entre a percepção industrial brasileira e a literatura sobre os benefícios da Indústria 4.0, (iii) identificação da abrangência do conceito da Indústria 4.0, compreendendo elementos que transcendem a manufatura avançada, e (iv) identificação de tecnologias habilitadoras para a implantação do conceito. Sob a perspectiva acadêmica, esta dissertação traz importantes contribuições para o entendimento do conceito e das tecnologias da Indústria 4.0, assim como o impacto destas na performance industrial. Do ponto de vista prático, os resultados auxiliam na compreensão de um tema de alta relevância empresarial, contribuindo com perspectivas para a diretriz estratégica das empresas à Indústria 4.0.Industry 4.0 arises with the goal to develop smart factories, with advanced autonomy and flexibility, through the adoption of digital technologies in an integrated manner in companies and in their value chains. The Industry 4.0enables benefits beyond operational performance, as the development of new offerings and new business models for companies. Industry 4.0 was developed in Germany, a country with high technological performance, and quickly inspires other initiatives in the whole world, in developed and emergent countries such as Brazil. These countries face major barriers for the adoption of technologies related to the concept, mainly due to the current technological level of their industrial sites. Even though Industry 4.0 is a growing field in literature, there are still considerable gaps of studies about the adoption of technologies related to the concept in the context of emergent countries, mostly due to its novelty. Therefore, this dissertation aims to study the concept of Industry 4.0 in Brazil, in order to understand its benefits for industrial performance and its enabling technologies. This study has a quantitative approach, with statistical analysis of data from national surveys. The main outcomes obtained were: (i) the identification of a relation between technologies and the expected benefits of the concept, (ii) the identification of disparities between Brazilian industrial perception and the literature about Industry 4.0 benefits, (iii) the identification of a wide scope of Industry 4.0 concept, comprising elements that transcends smart manufacturing, and (iv) the identification of enabling technologies for the implementation of the concept. Under academic perspective, this dissertation brings important contributions to understand the Industry 4.0 concept and technologies, and its impact on industrial performance. As practical contributions, the results contribute for the understandings of a high relevant theme for companies, contributing with perspectives for their strategical orientation towards Industry 4.0

Génese e dinâmica atual do conceito "Indústria 4.0": uma abordagem bibliométrica

O atual estudo tem por objetivo apresentar uma descrição do panorama da literatura científica sobre o conceito "indústria 4.0". A indústria 4.0, o seu conceito, abrangência e prática tem sido um tema de elevada importância e consequente debate nos mais diferentes meios e áreas. Assim, pela consciência da importância da bibliografia académica, aplicamos um estudo bibliométrico, aos dados recolhidos e selecionados, que tem por vista compreender e obter um perfil como chave da padronização e tendência na inovação em especial aplicada a esta terminologia. O estudo bibliométrico foi levado a cabo para artigos publicados nas bases de dados Scopus e Web of Science até ao ano de 2017, inclusive, com uma chave de pesquisa que conjuga diferentes sinónimos de "industry 4.0" para artigos apresentados em inglês e focados nas áreas económica e social. Mantivemos na nossa base de dados os artigos que faziam corresponder o seu conceito "indústria 4.0" a uma combinação de métodos de produção com tecnologias de informação e comunicação capazes de provocar mudanças no trabalho e/ou na fabricação, produção ou indústria. Os resultados obtidos são fruto das informações bibliográficas recolhidas junto dos artigos, tais como título, resumo, palavras-chave, datação, fonte, autoria, citações e filiação. A metodologia adotada permite a extração de informação quantitativa das redes bibliográficas por forma a detetar tópicos e revelar a dinâmica e evolução da produção científica. Este estudo sumariza assim o atual estado da arte indicando ainda deficiências e potenciais direções de pesquisa.The present study aims to present a description of the panorama of the scientific literature on the concept "industry 4.0". Industry 4.0, its concept, scope and practice has been a subject of great importance and consequent debate in the most different media and areas. Thus, due to the awareness of the importance of the academic bibliography, we applied a bibliometric study to the data collected. We also selected which aims to understand and obtain a profile as a key to the standardization and innovation trend especially applied to this terminology. The bibliometric study was carried out for articles published in the Scopus and Web of Science databases: up to and including the year 2017; with a search key that conjugates different synonyms of "industry 4.0"; presented in English; and focused on the areas economic and social development. We kept in our database the articles that matched its "industry 4.0" concept to a combination of production methods with information and communication technologies capable of bringing about changes in work and/or manufacturing, production or industry. The results obtained are the outcome of the bibliographical information collected in the articles, such as title, abstract, keywords, date, source, authorship, citations and affiliation. The methodology adopted allows the extraction of quantitative information from bibliographic networks in order to detect topics and reveal the dynamics and evolution of scientific production. This study summarizes the current state of the art thus indicating deficiencies and potential research directions

An industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applications

Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7% to 99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress