The concept of a digital twin has been used in some industries where an accurate digital model of the equipment can be used for predictive maintenance. The use of a digital twin for performance is critical, and for capital-intensive equipment such as jet engines it proved to be successful in terms of cost savings and reliability improvements. In this paper, we aim to study the expansion of the digital twin in including building life cycle management and explore the benefits and shortcomings of such implementation. In four rounds of experimentation, more than 25,000 sensor reading instances were collected, analyzed, and utilized to create and test a limited digital twin of an office building facade element. This is performed to point out the method of implementation, highlight the benefits gained from digital twin, and to uncover some of the technical shortcomings of the current Internet of Things systems for this purpose. INDEX TERMS Building information modeling, digital twin, life cycle management, Internet of Things, wireless sensor network.

The COVID-19 pandemic has caused a surge of demand for medical supplies and spare parts, which has put pressure on the manufacturing sector. As a result, 3D printing communities and companies are currently operating to ease the breakdown in the medical supply chain. If no parts are available, 3D printing can potentially be used to produce time-critical parts on demand such as nasal swabs, face shields, respirators, and spares for ventilators. A structured search using online sources and feedback from key experts in the 3D printing area was applied to highlight critical issues and to suggest potential solutions. The prescribed outcomes were estimated in terms of cost and productivity at a small and large scale. This study analyzes the number and costs of parts that can be manufactured with a single machine within 24 h. It extrapolates this potential with the number of identical 3D printers in the world to estimate the global potential that can help practitioners, frontline workers, and those most vulnerable during the pandemic. It also proposes alternative 3D printing processes and materials that can be applicable. This new unregulated supply chain has also opened new questions concerning medical certification and Intellectual property rights (IPR). There is also a pressing need to develop new standards for 3D printing of medical parts for the current pandemic, and to ensure better national resilience.

The use of Internet of Things (IoT) sensors and devices is on a sharp rise with the help of infrastructures like 5G cellular networks. However, with this swift expansion come major challenges such as management of the vast data collected by these IoT devices. In the paper, we propose a method which takes advantage of fuzzy similarity to significantly simplify the big data analysis and management for human operators and machines. A use case in the area of smart buildings was utilized to illustrate its potential application. A comparison is made between the ideal situation and the data collected from an office room. The considered environmental factors in this research are temperature, humidity, and workplace lighting and then the collected data was utilized to make triangular fuzzy numbers and after that, we compare them with an efficient fuzzy similarity measure. In addition to data summarization and abstraction, this method also protects the main information from inaccuracy. The advantage of the fuzzy controlling system is its aptitude to deal with nonlinearities and uncertainties.

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This paper aims to determine whether additive manufacturing (AM) always simplifies the supply chain. The advent of AM as a final-parts production method can radically impact supply chains. Due to AM's inherent characteristics that suit customised production and complex geometries, utilization of this technology continues to expand into various industries (e.g. aviation, defence, automobile, medicine). Some of the crucial areas that AM can contribute to are cost reduction and simplification of organizations' supply chains. An objective examination of the entire supply chain rather than merely focusing on production cost is important when studying the impact of switch-over from conventional to additive manufacturing. Supply chain complexity is caused by the proliferation of products, processes, suppliers, and markets, resulting in additional costs and decreased company profit. Therefore, to clearly illustrate the benefits and shortcomings of a switch-over to AM, it is necessary to investigate this transition in depth. In this paper, we analysed supply chain complexity before and after the implementation of AM in three case companies from distinct industries by conducting interviews or utilizing publicly available information. Our findings underline the simplification of supply chain in one of the cases, after the switch to AM, while it resulted in slightly higher complexity in another case. In the third case, the impact of switching to AM on the supply chain complexity is dependent on several variables. We contribute to the literature by elucidating on the common belief that AM simplifies the supply chain. We found that the implementation of AM is not a silver bullet to reduce the complexity of every supply chain.