Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is "Smart production". Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions

Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA):Business Process Model Automation in an Industry 4.0 Laboratory

The transformation towards the Industry 4.0 paradigm requires companies to manage large amounts of data. This poses serious challenges with regard to how effectively to handle data and extract value from it. The state-of-the-art research of Enterprise Architecture (EA) provides limited knowledge on addressing this challenge. In this article, the Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA) method is proposed and demonstrated. An abstraction hierarchy is introduced by AMA4EA to support companies to automatically abstract data from enterprise systems to concepts, then to automatically create an EA model. AMA4EA was demonstrated at an Industry 4.0 laboratory. The demonstration showed that AMA4EA could abstract detailed data from the Enterprise Resource Planning (ERP) system and Manufacturing Execution System (MES) to be relevant for a business process model that provided a useful and simplified visualization of production process data. The model communicated the detailed business data in an easily understandable way to stakeholders. AMA4EA is an innovative and novel method that contributes new knowledge to EA research. The demonstration provides sufficient evidence that AMA4EA is useful and applicable in the Industry 4.0 environment

Optical subsystems of metis (multi element telescope for imaging and spectroscopy) on board of the solar orbiter mission

The Lyman- spectral line, at 121.6 nm, is a wavelength of great interest for solar environment exploration and astrophysics. It is an important hydrogen emission line and can give information on the dynamics of heated regions of space like the solar photosphere and corona. Among others, the instrument METIS (Multi Element Telescope for Imaging and Spectroscopy) will be on board the mission Solar Orbiter, an ESA mission in collaboration with NASA, that from the year 2018 will undertake a trip towards the Sun to explore the dynamics of the solar dynamo and its connection with corona and heliosphere. METIS will take images of the corona in the visible range and at Lyman-, studying shape and evolution of the processes expanding from the Sun to the heliosphere. In the CNR-IFN UOS Padova laboratories I employed deposition and characterization facilities to study the characteristics of materials and devices to be used as optical elements at Lyman- wavelength. A morphological characterization was carried on with an atomic force microscope (AFM) and an optical characterization revealed the performances of materials and devices. Variations in performances were related to modications in the experimental conditions and the acquired knowledge was used to optimize the performances of the nal product. Annealing is an approach never fully explored before to increment the optical quality of magnesium uoride thin lms, and consequently the reectivity of VUV optical elements. I conducted a study of the phenomena involved in the process and applied the procedure to the realization of improved mirrors for this spectral region. This work will present all the experimental steps that led to the realization of the nal devices and describe the characteristics of this novel annealing approach

The Industry 4.0 Journey: Start the Learning Journey with the Reference Architecture Model Industry 4.0

The wave of the fourth industrial revolution (Industry 4.0) is breaking on manufacturing companies. In manufacturing, one of the buzzwords of the moment is "Smart production". Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. Data and information from manufacturing operations are however not used by most manufacturing companies and this impedes organizational learning. To address this problem, the authors applied in a Smart Production Laboratory the International Electrotechnical Commission (IEC) Reference Architecture Model Industry 4.0 (RAMI4.0) standard for Smart production. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment

Understanding Enterprise Architecture with Topic Modeling:Preliminary Research based on Journal Articles

The next 3 years will be more important than the last 50 due to the digital transformation across industries. Enterprise Architecture (EA), the discipline that should lead enterprise responses to disruptive forces, is far from ready to drive the next wave of change. The state of the art in the discipline is not clear and the understanding among researchers and practitioners is not aligned. To address these problems, we developed a topic model to help structure the field and enable EA to evolve coherently. In this preliminary study, we present the 360 identified topics in EA literature and their evolution over time. Our study supports and combines the findings from previous research and provides both a deeper analysis and more detailed findings