Framework for Context-Sensitive Dashbords Enabling Decision Support on Production Shop Floor

The advancing digitalization of production means that a large amount of data and information is being collected. Used correctly, these represent a significant competitive advantage. Decision support systems (DSS) can help to provide employees with the right information at the right time. Context-sensitive dashboards in the sense of decision support have the potential to provide employees on the shopfloor with information according to their needs. Within the scope of this work, a framework for the determination of the context-sensitive information needs of the staff on the shopfloor was developed. The goal was to reduce the development and adaptation effort of a context-sensitive application by classifying activities with similar information needs in advance. According to the methodology, the information needs of the employees are first analyzed and activities are summarized in terms of their general information needs. Subsequently, the information needs are weighted in order to prioritize them with regard to the processing and selection of information. The context-sensitive dashboard was then implemented using a user-centric approach to achieve a high level of user acceptance. The developed prototype, including architecture and design, was then tested and evaluated by experts. Three scenarios were compared in which experts were asked to assess the information requirements for employees in production. These results were then compared with the results of the framework. The comparison showed that for two of the three scenarios, the weighting determined in the framework matched the experts' assessments to a high degree. These general scenarios show that it is possible to generate context-sensitive dashboards based on demand using the developed framework. If the activities become more specific, it became apparent that further developments of the framework are necessary to cover the corresponding information needs. For this purpose, an iterative application to further scenarios and subsequent implementation in the framework seems to be purposeful

An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

An end-to-end big data analytics platform for IoT-enabled smart factories : a case study of battery module assembly system for electric vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

Comprehensive analysis of design principles in the context of Industry 4.0

[ES] Los sistemas de producción han evolucionado los últimos años gracias a avances tecnológicos recientes e innovaciones en el proceso de manufactura. El termino Industria 4.0 se ha convertido en prioridad y objeto de estudio para empresas, centros de investigación y universidades, sin existir un consenso generalmente aceptado del término. Como resultado es difícil diseñar e implementar soluciones de Industria 4.0 a nivel académico, científico o empresarial. La contribución de este documento se centra en proporcionar un análisis del significado e implicaciones de Industria 4.0 y exponer de forma detallada 17 principios de diseño fundamentales obtenidos a través de un estudio de mapeo sistemático. Estos principios son eficiencia, integración, flexibilidad, descentralización, personalización, virtualización, seguridad, es holística, orientada a servicios, ubicua, colaborativa, modular, robusta, utiliza información en tiempo real, toma decisiones optimizadas por datos, equilibra la vida laboral y es autónoma e inteligente. A través de estos principios, ingenieros e investigadores están capacitados para investigar e implementar escenarios apropiados de Industria 4.0.[EN] Production systems have evolved in the last years thanks to the recent technological advances and innovations in the manufacturing process. The Industry 4.0 term has become a priority and object of study for companies, research centers and universities, but there is not a generally accepted consensus for the term. As a result, is difficult design and implementation appropriate Industry 4.0 solutions at academic, scientific or business level. The contribution of this paper focuses on providing an analysis of Industry 4.0 meaning and implications and exposes in detail 17 fundamental design principles obtained by a systematic mapping study method. These principles are efficiency, integration, flexibility, decentralization, personalization, virtualization, security, is holistic, ubiquitous, collaborative, modular, robust, use information in real time, makes optimized decisions driven by data, is service-oriented, work life balance and is autonomous and intelligent. With these design principles, engineers and researchers have the capacity to research and implement appropriate Industry 4.0 scenarios.Belman-Lopez, CE.; Jiménez-García, JA.; Hernández-González, S. (2020). Análisis exhaustivo de los principios de diseño en el contexto de Industria 4.0. Revista Iberoamericana de Automática e Informática industrial. 17(4):432-447. https://doi.org/10.4995/riai.2020.12579OJS432447174Ahmad, A., & Babar, M. (2016). 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Model for early detection of non-compliance of process parameters in manufacturing systems

Doktorska disertacija se bavi razvojem konceptualnog modela za rano otkrivanje neusaglašenosti procesnih parametara (RONP) u proizvodnim sistemima. RONP model predstavlja hibridni model baziran na upotrebi fazi ekspertnih sistema i metoda napredne analitike, čiji je razvoj podeljen u sedam faza primenom i prilagođavanjem metodologije proučavanja podataka. Verifikacija modela je urađena u procesnoj industriji za proizvodnju podnih obloga od vinila gde je i eksperimentalno potvrđena njegova primenljivost.The Ph. D. thesis deals with the development of a conceptual model for early detection of non-compliance of process parameters in manufacturing systems. The model represents a hybrid model based on the use of fuzzy expert systems and advanced analytics methods. The development of the model is divided into seven phases by applying and adapting the data minig methodology. The verification of the model was done in the process industry for the production of vinyl flooring, where its applicability was experimentally confirmed