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This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

The study focuses on drug safety regulation capture, reveals the inner mechanism and evolutionary characteristics of drug safety regulation capture and provides suggestions for effective regulation by pharmacovigilance.

The article introduces prospect theory into the game strategy analysis of drug safety events, constructs a benefit perception matrix based on psychological perception and analyzes the risk selection strategies and constraints on stable outcomes for both drug companies and drug regulatory authorities. Moreover, simulation was used to analyze the choice of results of different parameters on the game strategy.

The results found that the system does not have a stable equilibrium strategy under the role of cognitive psychology. The risk transfer coefficient, penalty cost, risk loss, regulatory benefit, regulatory success probability and risk discount coefficient directly acted in the direction of system evolution toward the system stable strategy. There is a critical effect on the behavioral strategies of drug manufacturers and drug supervisors, which exceeds a certain intensity before the behavioral strategies in repeated games tend to stabilize.

In this article, the authors constructed the perceived benefit matrix through the prospect value function to analyze the behavioral evolution game strategies of drug companies and FDA in the regulatory process, and to evaluate the evolution law of each factor.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

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.

Delay and disruption claims involve a complex process that often result in disputes, unnecessary expenses and time loss on construction projects. This study aims to review and synthesize the contributions of previous research undertaken in this area and propose future directions for improving the process of delay and disruption claims.

This study adopted a holistic systematic review of literature following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A total of 230 articles were shortlisted related to delay and disruption claims in construction using Scopus and Web of Science databases.

Six research themes were identified and critically reviewed including delay analysis, disruption analysis, claim management, contract administration, dispute resolution and delay and disruption information and records. The systematic review showed that there is a dearth of research on managing the wide-ranging information required for delay and disruption claims, ensuring the transparency and uniformity in delay and disruption claims’ information and adopting an end-user’s centred research approach for resolving the problems in the process of delay and disruption claims.

Complexities in delay and disruption claims are real-world problems faced by industry practitioners. The findings will help the research community and industry practitioners to prioritize their energies toward information management of delay and disruption claims.

This study contributes to the body of knowledge in delay and disruption claims by identifying the need for conducting more research on its information requirements and management. Subsequently, it provides an insight on the use of modern technologies such as drones, building information modeling, radio frequency identifiers, blockchain, Bigdata and machine learning, as tools for more structured and efficient attainment of required information in a transparent and consistent manner. It also recommends greater use of design science research approach for delay and disruption claims. This will help to ensure delay and disruption claims are the least complex and less dispute-prone process.

Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

Research shows peer health education programs on university campuses can support students in pursuing sustainable health-related behavior changes. However, few programs deliver peer health education through a nondiet, weight-inclusive framework. Research shows that health educators who challenge the status quo of diet culture and weight-focused health interventions may face unique challenges when sharing this perspective with others. Thus, the purpose of this study was to examine the experiences of peer educators who provided critical health education by introducing a nondiet, weight-inclusive approach to health.

Five health coaches from a university health coaching program at a mid-sized southwestern university participated in a semi-structured interview. The data were analyzed through interpretative phenomenological analysis.

Peer educators faced numerous challenges when introducing nondiet, weight-inclusive approaches such as lacking credibility as a peer to challenge weight-centric messages, feeling conflicted about honoring clients’ autonomy when clients are resistant to a weight-inclusive approach and feeling uncomfortable when discussing client vulnerabilities. Peer educators also identified several strategies that helped them navigate these challenges such as being intentional with social media, using motivational interviewing to unpack clients’ concerns about weight, and seeking group supervision.

Given the reality that health coaches will face challenges sharing weight-inclusive health approaches, educators and supervisors should explicitly incorporate strategies and training methods to help peer health coaches prepare for and cope with such challenges. More research is also needed to examine effective ways to introduce weight-inclusive approaches to college students.

Evaluating the implementation of environmental management plans (EMPs) in highway construction projects is essential to avoid climate change. Public evaluations can help ensure that the EMP is implemented correctly and efficiently. To allow public evaluation of EMP implementations, this study aims to investigate performance indicators (PIs) for assessing EMP implementation in highway construction projects. To that end, the study objectives are to compare the critical PIs between environment auditors (EAs) and environment officers (EOs) and among the main project stakeholders (i.e. clients, contractors and consultants), create components for the critical PIs and assess the efficiency of the components.

The paper identified 39 PIs from interviews with environmental professionals and a systematic literature review. Then a questionnaire survey was developed based on the PIs and sent to EAs and EOs. The data were analyzed via mean score ranking, normalization, agreement analysis, factor analysis and fuzzy synthetic evaluation (FSE).

The analyses revealed 21 critical PIs for assessing EMP implementation in highway construction projects. Also, the critical PIs can be grouped into four components: ecological, pollution, public safety and ecological. Finally, the overall importance of the critical PIs from the FSE is between important and very important.

To the best of the authors’ knowledge, this paper is the first-of-its-kind study on the critical PIs for assessing EMP implementation in highway construction projects.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.