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The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. [ABSTRACT FROM AUTHOR]

The growing awareness of climate change is constantly moving the attention of designers and policymakers from typical and current scenarios to future layouts. This new approach introduces a degree of uncertainty that should be accounted in the building design process. The multitude of possible scenarios suggests considering dynamic system that can adapt themselves to unpredicted operating conditions. The aim of this study is to test a new approach and a new flexibility metric analysing the behaviour of a high-performance dynamic internal curtain in current and future scenarios. The first part of the paper focuses on the optimization of the dynamic system in the current scenario; this preliminary analysis represents the traditional design approach and is considered the baseline for all the comparisons. Hence, defining a matrix of likely scenarios, this paper explores the behaviour of three different selected control strategies – always off (i.e., no curtain), fixed control, optimized control – in the different scenarios considering possible variations of i) climate, ii) urban context, iii) internal loads, and iv) building use. The main outcomes of the research are, on the one hand, the comparisons of the control strategies and the benefits of the dynamic system and, on the other hand, the definition of a new metric that can properly describe the flexibility of the building systems with reference to the future scenarios analysed. This metric is based on six classes – which describe the statistical distribution of the energy consumptions of all the scenarios simulated in the matrix – and an index called Energy Flexibility Index (EFI) that quantifies the flexibility of the technology considered. The case study analysed highlights how implementing a dynamic shading system, considering the same building use, can increase the EFI of nearly 21% for non-optimized control strategy and up to 23% when optimized. While changing the building use in residential reduces these values respectively, to 6% and 7%. [ABSTRACT FROM AUTHOR]