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The challenges posed by unsustainable practices in today's economy underscore the urgent need for a transition toward a circular economy (CE) and a holistic supply chain (SC) perspective. Benchmarking plays a pivotal role in managing circular SCs, offering a metric to gauge progress. However, the lack of consensus on the optimal benchmarking approach hampers effective implementation of circular business practices. To address this gap, we conducted a systematic review of the literature, identifying 29 pertinent publications. The analysis revealed 30 unique attributes and sub-attributes for benchmarking circularity, which were clustered into five main attributes. The main attributes are goals, subjects, key performance indicators (KPIs), data sources, and evaluation methods, while the sub-attributes are organised as features of the main attributes and depicted as a feature model. Drawing from selected publications, we illustrated each feature with examples. Our model offers a comprehensive benchmarking reference for circularity and will be a valuable tool for managers in the transition toward circularity. Supply chains seeking to benchmark their transition to circularity can apply the reference model to ensure that their benchmarking strategy is consistent with state-of-the-art knowledge. By providing a generic circularity benchmarking approach that is valid for diverse economic sectors, our findings contribute to theoretical efforts to address the lack of generic frameworks for CE.

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations.

The traditional data management systems prove inadequate to handle the volume, velocity, and variety of the data within farm business processes. Smart farming technologies offer advanced data management systems as a practical solution to these challenges. However, data is complex and originates from many sources; hence many aspects of data must be considered during the data management design of smart farming systems. This study proposes a reference architecture for data management in smart farming, developed through domain analysis and architecture modeling approaches. The domain analysis provides insights into the common and variant features and modules of the smart farming system, resulting in a blueprint representing family features across various smart farming domains. The effectiveness of the proposed reference architecture has been evaluated through two case studies, demonstrating its efficacy in designing data management systems for smart farming. The study found that the percentage of reused modules in the case studies, compared to the provided reference architecture, was 82.6%. The outcomes of this research will pave the way for further exploration in smart farming, particularly addressing data management issues within smart farming systems.

Microservice architecture (MSA) is an architectural style that is designed to support the modular development of software systems within a particular domain. It is characterized by the use of small, independently deployable services, which can be developed and deployed autonomously. The benefits of MSA include improved scalability, fault-tolerance, and ease of deployment and maintenance. However, developing MSA for a specific domain can be challenging and requires a thorough consideration of various concerns such as service boundaries, communication protocols, and security. To support the easy development and guidance of an MSA for practitioners, we present a reference architecture design for MSA. The reference architecture has been designed after a comprehensive domain analysis of MSA in the literature and the MSAs of key vendors. This analysis has been used to identify best practices and common patterns that can be applied to the development of MSA. Additionally, relevant architecture viewpoints have been selected to model the corresponding architecture views, providing a clear and comprehensive understanding of the architecture. To validate the proposed reference architecture, a multi-case study research approach has been used. In this approach, two industrial case studies have been used to demonstrate the practical applicability of the proposed reference architecture. The results of these case studies have shown that the reference architecture can be used to effectively guide the development of MSA in real-world scenarios.