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The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.

The purpose of this study is to propose a new mapping tool called Circular Value Stream Mapping (C-VSM) that combines Circular Economy principles with Lean tools to enhance sustainability performance in operations.

To develop the C-VSM tool, the researchers conducted a literature review and a focus group. The tool was then applied to two real case studies in the agri-food sector, specifically analyzing an artichoke and olive oil producer, to assess its validity and effectiveness.

The study introduces the Circular Resource Box (CRB) as a key innovation in the C-VSM tool. This visual representation effectively captures resource circularity and how resources and wastes are managed, making it easy to identify circularity in the production process. By combining qualitative and quantitative information with this visual representation, companies can identify improvement opportunities aligned with the CE.

The research is limited in scope as it focuses on the application of the C-VSM tool in the agri-food sector. Further research could explore its applicability in other industries and settings to understand its broader impact.

The C-VSM tool provides practical benefits to companies seeking to transition from linear to circular production processes. It enables practitioners to identify opportunities to reduce environmental impacts and optimize production operations in line with CE.

The introduction of the C-VSM tool is a novel approach that bridges the gap between Lean Manufacturing and CE concepts, advancing the understanding of how CE thinking can be effectively implemented in operations.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This study aims to analyze lean wastes for the poultry sector in Turkey and link lean tools to this study, focusing on identifying each lean waste that affects poultry production and proposing solutions for preventing these lean wastes in the sector. The proposed solutions aim to improve processes by suggesting different lean tools and their applications for the poultry sector.

The study consists of two different applications. First, the waste relationship matrix (WRM) was created to reveal the relationship between seven lean wastes and their importance order. Then, after determining lean tools for eliminating lean wastes, the optimum weight ranking and consistency ratio of the most suitable lean tools were calculated for these wastes and ranked with the best-worst method (BWM).

Results showed that overproduction is the most critical waste that impacts other wastes, followed by defect waste. Due to the nature of the sector, these wastes not only result in economic loss for the company but also in food waste and loss and issues related to animal welfare. Furthermore, the Kaizen approach and 5S implementation are the methods to eliminate these wastes. Detailed discussion on the link between lean tools and lean wastes is provided for the poultry sector.

This is the first study that theoretically and empirically identifies the potential lean waste affecting the poultry sector and provides lean tools for eliminating these wastes. Sector-specific explanations and discussions are presented in the study to show the applicability of lean approaches in the poultry sector to eliminate waste. In addition, this study is the first to integrate the WRM and BWM.

Overcrowding continues to afflict emergency departments (EDs), and its attendant consequences are becoming increasingly severe. The burden of the COVID-19 pandemic is further escalating the situation worldwide. One of the most critical questions is how to adequately quantify what constitutes overcrowding and determine implications for operations management in improving service efficiency. This paper aims to discuss the aforementioned.

The authors propose the time and class complexity measures for ED service systems, taking into account important patient-level and system characteristics. Using an extensive data set from a Canadian ED, the authors investigate the performance of complexity-based measures in predicting service delays.

The authors find that the complexity measure is potentially more important than some well-known crowding metrics. In particular, EDs can improve service efficiency by managing the level of complexity within a desirable interval. Furthermore, complexity exposes how the interplay between demand-side behavioral changes and supply-side responses affects operational performance. Moreover, the results suggest that arrival patterns—the number of patients of each class arriving per time and times between events (arrivals and service completions)—increase the risk of service delays more than the demand volume.

This paper is the first to provide an extensive investigation into the application of the complexity-based measure for ED crowding. The study demonstrates potential values to be gained in ED service systems if complexity measure is incorporated into their operations management decisions.