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Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

The purpose of the paper is to present research trends in the food supply chain in the context of changes in food systems due to globalization, urbanization, environmental concerns, technological changes and changes in food consumption patterns in the world.

The present investigation was performed by bibliometric analysis using the VOSviewer software, visualization software developed by Nees and Waltman (2020). In this work we performed co-citation, bibliographic coupling and keyword evolution analyses.

The results show that research in the food supply chain is rapidly changing and growing. By applying co-citation analysis, The authors found that the intellectual structure of the food supply chain has evolved around six clusters, namely, (a) collaboration and integration in the supply chain (b) sustainable supply chain management, (c) food supply chain management (FSCM), (d) models for decision-making in the food supply chain, (e) risk management in the supply chain and (g) quality and food logistics in the supply chain. However, based on bibliographic coupling analysis, The authors find that new or emerging research niches are moving toward food supply market access, innovation and technology, food waste management and halal FSCM. Nevertheless, the authors found that the existing research in each of the thematic clusters is not exhaustive.

The limitation of the research is that the analysis mainly relates only to the bibliometric approach and only one database, namely, Scopus. Broader inclusion of databases and deeper application of content analysis could expand the results of this research.

There are limited studies that have examined research trends in food supply chains in both developed and developing countries using bibliometric analysis. The present investigation is novel in identifying the thematic research clusters in the food supply chain, emerging issues and likely future research directions. This is important given the dynamics, consumer demand for quality food, technological changes and environmental sustainability issues in food systems.

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.

As avatars are increasingly becoming popular, both scholars and businesses are acknowledging the vast potential that avatars hold for the future. Despite this growing interest in avatars, no review articles have attempted to provide a comprehensive overview of avatar literature and its implications for consumers. The present review addresses this gap using the combination of descriptive analysis (for corpus performance), bibliometric analysis (for corpus performance and emerging themes), and thematic analysis (for emerging themes and implications as well as future research opportunities). We conducted a review of 47 Scopus-indexed articles from 34 journals between year 2006 and 2023. By examining the corpus performance of avatar literature, the emerging themes, and future research opportunities, this review offers scholars a comprehensive overview of the subject matter.

The purpose of this study is to investigate the influence of IFRS convergence on annual report readability in an emerging market context, with an emphasis on the contents of management discussion and analysis (MD&A), notes to the accounts (Notes) and the whole annual report.

The study performs firm-fixed effect regression on a sample of 143 Indian listed companies over a period spanning from 2012 to 2021 to examine the influence of IFRS convergence on readability. This assessment primarily focuses on broader spectrums of readability dimensions, namely annual report length and complexity, wherein complexity is measured using the Gunning Fog, Flesch Reading ease and Flesch-Kincaid grade index.

As Indian firms shift to IFRS reporting, the findings suggest that annual reports have become significantly lengthier and more complex, causing deterioration in readability. The Notes section, in particular, exhibits the most significant increase in length and complexity, followed by the entire annual report and MD&A section. Furthermore, the findings also indicate that the complexity of the Notes section is instrumental in the observed complexity growth of the whole annual report in the post-IFRS period.

The current study employs readability indices rather than directly taking into consideration the opinions of actual users of annual reports to determine readability. As a result, the study does not provide direct evidence on how information in annual reports affects users' readability.

The findings provide insightful information to managers and policymakers about the difficulties stakeholders may encounter while reading IFRS-based annual reports, which ultimately impact their investment decisions. Thus, there is an important managerial implication from this, depending upon the severity of complexity corporations participate in while complying with IFRS in the post-IFRS period.

Analyzing the influence of exogenous information shock, such as IFRS convergence, on readability is critical, particularly for emerging markets like India, where a lack of financial literacy and weaker enforcement already have detrimental effects on the capital market. In light of this, the current study provides a comprehensive examination of the impact of IFRS convergence on annual report readability and contributes to the growing IFRS literature in the less explored emerging market context.

Since the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).

An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.

The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.

There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.