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Purpose: This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies. Design/methodology/approach: For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness). Findings: The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness. Originality/value: This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy's performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies. [ABSTRACT FROM AUTHOR]

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings. [ABSTRACT FROM AUTHOR]

Aims Background Methods Results Discussion Conclusion Implications for nursing and nursing policy To investigate the effect of ward operational efficiency on nursing workload and identify the factors that influence nursing workload.It remains unclear how and to what extent ward operational efficiency can influence nursing workload.A prospective observational study was conducted from July 1, 2022 to June 30, 2023, in one tertiary general hospital in China. Purposive and convenience sampling was used, and 266 470 patients from 66 wards and 52 nurses from 13 wards were recruited. The relationships between operational efficiency and nursing workload and the predictors of nursing workload were analyzed. The STROBE guidelines were followed.The operational characteristics vary by the type of wards. Nursing workloads were positively correlated with case mix index (CMI), rate of level 4 surgery, the number of patients transferred in and out, the number of deaths, total bed days, and the number of emergency admissions and critical illnesses (γs: 0.35–0.56, p < 0.05). And the CMI, rate of level 4 surgery, average bed occupancy rate, number of critically ill patients, and total bed days were the predictors of nursing workload (R2 = 57.3%, p < 0.05).This study is the first to discuss the relationship between operational efficiency and nursing workload on the ward level and offers valuable insights into the nursing workload.The operational efficiency of wards affects the nursing workload and needs to be considered both in the measurement of nursing activities and in the sizing of the nursing staff.The study findings provide a full understanding of the relationship between ward operation and nurse staffing, which is helpful for nursing managers to formulate scientific nurse staffing policies. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real‐time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. Practitioner Points: Digital representation of assets and processes in the drinking water treatment networkEarly fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution networkReduction on monitoring time and incident response for target assets by means of Digital TwinsImprovement in visualization, prediction, and proactive measures for asset management and water quality controlContribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations [ABSTRACT FROM AUTHOR]

This article proposes a comprehensive framework for enhancing information exchange in ship maintenance through the integration of Digital Twins (DTs) and the Internet of Things (IoT). The maritime industry faces significant challenges in maintaining ships due to issues like data silos, delayed information flow, and insufficient real-time updates. By leveraging advanced technologies such as DTs and IoT, this framework aims to optimize maintenance processes, improve decision-making, and increase the operational efficiency of maritime vessels. Digital Twins create virtual replicas of physical assets, allowing for continuous monitoring, simulation, and prediction of ship conditions. Meanwhile, IoT devices enable real-time data collection and transmission from various ship components, facilitating a seamless flow of information. This integrated approach enhances predictive maintenance capabilities, reduces downtime, and improves resource allocation. The article will delve into the architecture of the proposed framework, implementation steps, and potential challenges, supported by case studies that demonstrate its practical application and benefits. By addressing these aspects, the framework aims to provide a robust solution for modernizing ship maintenance operations and ensuring the longevity and reliability of maritime assets. [ABSTRACT FROM AUTHOR]

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase. [ABSTRACT FROM AUTHOR]

This research explores the impact of debt management and big data technology utilization in management decision-making on corporate financial performance with a focus on operational efficiency and sustainable business strategy effectiveness as intervening variables. It aims to delve into how a firm's debt management strategies and its incorporation of big data into decision-making processes influence its financial outcomes. This study scrutinizes the company's approach to managing its debt structure and levels while the use of big data technology in decision-making reflects the integration of advanced technology in strategic planning by examining debt management. The study further investigates how operational efficiency and the effectiveness of sustainable business strategies act as intermediaries in this relationship indicating the role of a firm's internal processes and corporate strategy in driving financial success. Moreover, the research considers mediating variables such as industry type and market competition level which could affect the relationship between the independent and dependent variables. This study aims to test these relationships providing insights into the influence of debt management and big data technology on financial performance using statistical data analysis. Expected outcomes include a deeper understanding of the strategic implications of these factors for achieving sustainable and improved financial performance in a complex business landscape. Emphasizing the importance of efficient debt management and the strategic application of big data technology, the study highlights the necessity for firms to adopt wise financial resource management and innovative decision-making approaches to navigate the challenges of the modern business environment effectively. [ABSTRACT FROM AUTHOR]

For small and medium-sized enterprises (SMEs) seeking to adhere to sustainable standards and gain a sustained competitive edge, green marketing and cleaner production are increasingly imperative. Green marketing has grown a lot in popularity in the present market, which makes it possible to rebrand and repackage existing products. This study looks at how green marketing in Algerian industrial SMEs might help achieve sustainability through cleaner production. Data analysis was performed using Smart PLS 4 softwareV.4.1.0.8 and structural equation modeling. With a mediation effect of 47.4%—higher than the direct impact of cleaner production on sustainability—the study's conclusion is that green marketing strongly mediates the relationship between cleaner production and sustainability. This suggests that by combining green marketing with cleaner production methods, SMEs can reap significant profits. In accordance with their financial and environmental goals, SMEs can effectively incorporate cleaner production methods and green marketing with the help of the research's practical recommendations. [ABSTRACT FROM AUTHOR]

Objective: This study aims to design a lean distribution model tailored for the electric power distribution network in Tehran, integrating global best practices and expert insights to address the specific challenges and opportunities within this sector. Methodology: The study employed a mixed-method approach, combining qualitative and quantitative techniques. A comprehensive literature review was conducted to identify key factors influencing lean distribution. Expert interviews with managers and industry specialists in Tehran's electric power distribution sector provided additional insights. The factors were structured and categorized using Interpretive Structural Modeling (ISM) and MicMac analysis to establish their interrelationships and influence. Findings: The study identified 16 critical factors influencing lean distribution, with "World-Class Lean Distribution in the Electric Power Industry" emerging as the most influential. Other key factors included "Optimal Distribution of Reactive Power in Power Systems," "Estimation of Load Uncertainty," "Reduction of Loss Levels," and "Use of Fixed and Switchable Capacitors." The findings highlight the importance of infrastructure, ISO 9000 implementation, technological integration, internal capacity, and resilience. Conclusion: The research provides a comprehensive framework for implementing lean distribution practices in the electric power industry, emphasizing the need for global standards, technological advancements, and resilience. The findings suggest that adopting these practices can lead to improved reliability, reduced operational costs, and enhanced customer satisfaction, contributing to sustainable development and energy security. [ABSTRACT FROM AUTHOR]

This research investigates digital transformation strategies to improve the efficiency and competitiveness of ABC Education Foundation in Depok, West Java. Using Sunil Gupta's methodology, this study conducted internal and external SWOT analysis, value chain analysis, business benchmarking analysis, and education technology trend analysis. The findings show that improving information system integration, operational efficiency, and the role of the IT department are key in achieving digital transformation goals. The results also present a digital transformation roadmap based on the stages of value definition, rollout, and improvement. The implications of the digital transformation recommendations can be seen in changes to the business model and organizational infrastructure. [ABSTRACT FROM AUTHOR]

In the evolving landscape of airport management, leveraging public data emerges as a transformative strategy to enhance operational efficiency and customer satisfaction. This paper explores the integration of data analytics in airport operations, illustrating through case studies how airports can utilise open data to optimise performance, improve passenger experience and streamline security processes. We highlight the significant benefits of adopting data-driven approaches, including increased operational transparency, improved decision making and enhanced service quality. Our findings underscore the pivotal role of public data in driving innovation and sustainability in airport management, offering actionable insights for airport authorities and stakeholders. This paper serves as a stimulus on how to benefit from the use of open data. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this research was to examine the impact of organizational agility on the efficiency of enterprises. Design/methodology/approach: The research was conducted using a survey among 720 respondents from Poland in February 2023. Opinions on the impact of organizational agility on various aspects of enterprise efficiency were analyzed, such as image improvement, profitability, market position, competitiveness, building trust and relationships with customers, promoting social activities and ethical and sustainable operation. Findings: Organizational agility has a multi-faceted impact on the functioning of enterprises, including improving the image, increasing profitability, strengthening the market position, increasing competitiveness, building trust, promoting social activities and sustainable and ethical operation. The research results indicate that the majority of respondents agree with the statement that organizational agility contributes to improving the company's image and increasing its profitability. Research limitations/implications: The research is limited to respondents from Poland, which may affect the generalizability of the results to other countries and regions. Further research is needed across industries and regions to gain a more comprehensive understanding of the impact of organizational agility on business performance. Practical implications: Research findings can help managers identify key areas where organizational agility can deliver the greatest benefits. This may include management strategies, employee development, customer relationship management and promoting social activities and sustainability. Social implications: Organizational agility helps build trust and relationships with customers and promotes social performance and sustainability. Agile companies engage in activities for sustainable development and compliance with high ethical standards, which positively affects their image and relations with stakeholders. Originality/value: The research provides new information on the impact of organizational agility on various aspects of the functioning of enterprises in Poland. They emphasize the importance of organizational agility as a key factor influencing competitiveness, profitability and building lasting relationships with customers and investors. [ABSTRACT FROM AUTHOR]

The aim of this research is to examine the influence of company size, managerial ownership, and Sustainability Report on the financial performance of banking companies in Indonesia, with operational efficiency as an intervening variable. The population for this study comprises banking companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2022. Data collection was conducted using purposive sampling, resulting in a sample of 13 companies and a total of 65 observations. The analysis technique employed is panel data regression. Microsoft Excel and Eviews 13 were used for data processing. The research findings indicate that, partially, company size and managerial ownership have a positive effect on operational efficiency. However, the Sustainability Report does not significantly affect operational efficiency. Furthermore, company size and operational efficiency are empirically proven to have a positive impact on financial performance. Meanwhile, managerial ownership and Sustainability Report do not significantly influence financial performance. Company size and managerial ownership, mediated by operational efficiency, positively impact financial performance, while Sustainability Report which mediated by operational efficiency, does not significantly affect financial performance. [ABSTRACT FROM AUTHOR]

Theoretical and empirical evidence point to the ability of political leaders to manipulate economic policies and leverage local firms to elevate their political careers. Despite this, there is limited understanding of how these career incentives impact the operational dynamics of the firms involved. This empirical study delves into this gap, revealing that city leaders with fewer promotional incentives are more inclined to mobilize state-owned enterprises (SOEs) within their jurisdiction to pursue sustainable development, as indicated by heightened corporate operational efficiency. Our analysis further indicates that the career prospects of city leaders significantly influence the operational efficiency of SOEs by driving a shift in focus from rapid growth to sustainable development and firms' adoption of disruptive technologies. We posit that this increase in operational efficiency not only benefits the SOEs but also generates unique value for stakeholders, resulting in elevated market capitalization and reduced stock price crash risk. Our findings carry direct relevance to the ongoing discourse on political incentives and contribute to operations management research, shedding light on the intricate ways in which the political environment can impact the operational performance of firms. [ABSTRACT FROM AUTHOR]

This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of advanced ML models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Momentum LSTM (MLSTM), on a dataset of 21,644 operational records. These models were trained to predict a critical operational parameter, FlowRate, which is essential for operational integrity and efficiency. Our results demonstrate substantial improvements in predictive accuracy: the LSTM model achieved an R2 score of 0.9720, the BiLSTM model reached 0.9725, and the MLSTM model topped at 0.9726, all with exceptionally low Mean Absolute Errors (MAEs) around 0.0090 for LSTM and 0.0089 for BiLSTM and MLSTM. These high R2 values indicate that our models can explain over 97% of the variance in the dataset, reflecting significant predictive accuracy. Such performance underscores the potential of integrating ML with SCADA systems for real-time applications in the oil and gas industry. This study quantifies ML's integration benefits and sets the stage for further advancements in autonomous well-monitoring systems. [ABSTRACT FROM AUTHOR]

Sustainable manufacturing with the Internet of Things (IoT) reduces environmental impacts, conserves natural resources, saves energy, and improves worker, community, and consumer safety while maintaining economic viability. IoT's network of sensors and intelligent devices collects and analyzes data throughout the production lifecycle, enabling organizations to fulfil sustainability objectives and adopt more efficient, less wasteful operations. Waste management and reduction measures are the focus of sustainable manufacturing research. Improvements are needed to simplify waste management and reduce production waste. Thus, in this study, we introduce an innovative machine learning technology called "EcoEfficientNet", developed to tackle this problem. Our study addresses the issue of waste in manufacturing processes. EcoEfficientNet employs sophisticated deep learning algorithms to analyze complex production data, allowing it to identify significant patterns and determine specific areas where waste can be significantly minimized. EcoEfficientNet's approach to waste reduction in manufacturing processes revolves around three main strategies: data-driven analysis, optimization recommendations, and adaptable learning for continual enhancement. EcoEfficientNet's success lies in its capacity for perpetual learning, enabling it to adapt to novel data and evolve alongside production settings. An extensive case study of a particular manufacturing process is carried out to assess the efficiency of EcoEfficientNet and provide helpful perspectives into the model's effectiveness. By incorporating this method into the manufacturing process, organizations have seen a decrease in waste generation of up to 30%, demonstrating the applicability and efficacy of machine learning in improving sustainable manufacturing processes. The key to EcoEfficientNet's success is its ability to engage in continuous learning, allowing it to adjust to new data and develop in tandem with operational environments. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology is revolutionizing traditional assembly processes, offering intuitive and interactive guidance that significantly enhances operational efficiency and accuracy. This study investigates the impact of AR on the assembly of Turtlebots, a complex task representative of industrial applications. Through a comparative analysis involving traditional paper manuals, modified paper manuals, and AR-based manuals, the benefits of AR integration are quantitatively assessed. Participants utilizing AR-based manuals completed the Turtlebot assembly 21.72% faster than those using traditional paper manuals, with a notable reduction in assembly time from an average of 03:00:40 to 02:21:26. Furthermore, the incidence of assembly errors significantly decreased, with AR manual users making an average of 2.25 errors compared to 5 by paper manual users. These findings underscore the potential of AR to expedite complex assembly tasks and enhance the accuracy of these processes. The study highlights the novel application of AR in improving both the speed and quality of assembly in an industrial context, demonstrating AR’s role as a pivotal technology for the future of manufacturing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Technology Economics is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the digital age, integrated ERP solutions are the best way to efficiently manage an organization's business and resources, as they contain a number of specific modules for accounting, human resources, production, sales and purchasing. This system is designed to provide efficiency and transparency to all operations performed by organizations. The aim of this paper is to explore how ERP systems are a reliable solution in the context of corporate reporting. The modules of this system facilitate the collection, storage, and analysis of an organization's financial and operational data, building a solid foundation for accurate and correct reporting of results, especially its performance. The case study in this paper is summarized as a quantitative analysis based on a questionnaire, which aims to highlight the advantages and challenges of using an ERP system in the context of financial and operational reporting of organizations. The conclusion of the paper emphasizes the importance of using ERP systems in the corporate reporting of organizations, highlighting the main benefits they bring in terms of operational efficiency, data transparency and correct decision making.

Effective customer service and operational excellence are critical components of corporate success, particularly in today's changing business world. This paper examines the crucial role that inventory management plays in controlling and improving customer service and operational efficiency in businesses. Companies that efficiently manage inventory levels may ensure the timely fulfillment of client orders, minimize stockpiles, and increase efficiency. Furthermore, efficient inventory management contributes to boosting operating efficiency, lowering logistical costs, and increasing profitability. This study extensively reviews the literature and case studies to explore the best strategies utilized in inventory management to attain these objectives. It also investigates the influence of inventory management on performance and offers useful insights for companies looking to use inventory management as a strategic strategy to gain a sustained competitive advantage.

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The breakthrough of articulated tractors has led to a significant increase in productivity and a cost reduction of agricultural operations. The performance of the tractor implement system depends on the understanding of the tractors’ energy parameters. Fertilizer seeders operate between 6 and 9 km h-1, while seed drills allow higher speeds. Increasing sowing speed improves operational efficiency. However, it is important to adequately set the machinery, to optimize the energy parameters, considering the balance between productivity and sustainability. This study evaluated the energy and operating parameters of a 398kW articulated agricultural tractor in a sowing operation at different speeds. The experiment was conducted in a complete randomized block design. Five theoretical speeds were chosen for the sowing operation (6, 7, 8, 9 and 10 km h-1), with seven replications (35 units). The tractor operating parameters were measured: operational speed, slippage, engine speed, hourly fuel consumption, engine thermal efficiency, specific fuel consumption, drawbar force, drawbar efficiency, fuel consumption per area and field operating capacity. The data were subjected to normality test, when significant, variance analysis. Results showed high field capacity in the highest speed and low fuel consumption per area at the highest speed. [ABSTRACT FROM AUTHOR]

[Objective] Considering the strategic guideline of “Ocean Power”, a scientific measurement of the operational efficiency of China’s sea-related listed companies and support for sea-related listed companies to be better and stronger are of great significance to consolidate the micro foundation of the development of the marine economy and promote the expansion of the space for the development of the blue economy. [Methods] This study used the explainable boosting machine (EBM) model based on Pearson correlation coefficient, combined with the three-hierarchy meta-frontier framework, to scientifically measure the operational efficiency of China’s sea-related listed companies during the period of 2014-2022, and further decomposed the inefficiency loss to explore its internal and external influencing factors. [Results] The study showed that: (1) Overall, the operational efficiency of Chinese sea-related listed companies was on an upward trend and showed obvious stage characteristics. (2) From the perspective of industry-ownership heterogeneity, the operational efficiency of emerging sea-related companies was leading in the early stages, but the operational efficiency of traditional sea-related companies showed a significant trend of subsequent growth; the operational efficiency of private sea-related companies was at the forefront, and basically formed a pattern of development in which private enterprises were leading and state-owned enterprises were following. (3) From the perspective of inefficiency decomposition, management inefficiency loss was the key reason affecting the improvement of operational efficiency of sea-related listed companies, followed by industrial heterogeneity efficiency loss and ownership heterogeneity efficiency loss. (4) Based on the analysis of influencing factors, the management inefficiency loss of sea-related listed companies was significantly negatively correlated with enterprise size and positively correlated with business solvency, while the level of regional economic development significantly and negatively affected their technical inefficiency loss. [Conclusion] During the 14th Five Year Plan period, we should further optimize the layout of the marine industry, improve the market-oriented development environment, and enhance the management level of the enterprises, to explore the effective path to promoting the operational efficiency of sea-related listed companies.

Abstract Background During the prolonged COVID-19 pandemic, hospitals became focal points for normalised prevention and control. In this study, we investigated the feasibility of an inpatient bed reservation system for cancer patients that was developed in the department?s public WeChat account. We also explored its role in improving operational efficiency and nursing quality management, as well as in optimising nursing workforce deployment. Methods We utilised WeChat to facilitate communication between cancer patients and health care professionals. Furthermore, we collected data on admissions, discharges, average number of hospitalisation days, bed utilisation rate, and the number of bed days occupied by hospitalised patients through the hospital information system and nurses? working hours and competency levels through the nurse scheduling system. The average nursing hours per patient per day were calculated. Through the inpatient bed reservation system, the number of accepted admissions, denied admissions, and cancelled admissions from the reservation system were collected. The impact of the bed reservation system on the department?s operational efficiency was analysed by comparing the number of hospitalisation discharges before and after reservations, as well as the average hospitalisation and bed utilisation rates. By comparing nurses? working hours per month and average nursing hours per patient per day, the system?s impact on nurses? working hours and nursing quality indicators was analysed. Results The average hospitalisation length, bed utilisation rate, and nurses? working hours were significantly lower, and the average number of nursing hours per patient per day was significantly higher after the implementation of the reservation system. The full-cycle bed information management model for cancer patients did not affect the number of discharged patients. Conclusion Patients? ability to reserve bed types from home in advance using the department?s official WeChat-based inpatient bed reservation system allowed nurses to prepare for their work ahead of time. This in turn improved the operational efficiency of the department and nursing quality, and it optimised the deployment of the nursing workforce

This research endeavours to unveil the nuanced impact of organisational and operational models in higher education by conducting a comparative study of two institutions: the pseudonymous Old Orchard College and Seafarer University. The purpose is to discern the implications of adopting proven business strategies on institutional performance, growth, and student outcomes. The study employs a comprehensive methodology, integrating four years’ worth of personal observations, interviews with current and former employees, and rigorous data mining and analysis of government public domain databases. The comparative analysis delves into the organisational structures, academic focuses, and operational approaches of Old Orchard College and Seafarer University. The design embraces a multi-faceted approach to distill the complexities of organisational dynamics in higher education. The findings illuminate a stark dichotomy between the two institutions. Old Orchard College, undergoing an academic renaissance, is grappling with a shift in executive leadership and restructuring of academic responsibilities. In contrast, Seafarer University emerges as a model of efficiency, marked by its status as the fastest-growing state-supported university. The dialogue between tradition and innovation, and the transformative power of efficient organisational models, become focal points of discovery. This research contributes to the existing body of literature by providing an in-depth exploration of the impact of organisational paradigms on higher education institutions. The originality lies in the comparative nature of the study, which dissects the interplay between traditional and modern approaches. The inclusion of personal observations and data analysis adds a layer to the exploration, offering a holistic perspective on organisational dynamics in higher education.

Studying public-private partnership practices in textbook publishing and distribution in Nepal offers insights into effective strategies for improving education quality through collaboration between government and private sectors. The objective of the study is to assess the effectiveness of public-private partnerships (PPPs) in enhancing education quality through improved textbook publishing and distribution mechanisms. The methodology employed in this study integrates descriptive and explanatory research approaches. It utilizes a structured questionnaire comprising 40 items rated on a 5-point Likert scale to evaluate different dimensions of PPPs concerning school textbook publication and distribution in Nepal. In this study, representatives from private organizations involved in textbook publishing and distribution in Nepal were interviewed. Sampling is conducted through random selection from a pool of 390 private organizations, aiming to ensure representation across various sectors. The model developed from this analysis had a strong explanatory power, with the identified independent variables explaining up to 48% of the variability in improving education quality through PPPs. The study concludes that emphasizing transparency, accountability, and effective communication within public-private partnerships significantly contributes to enhancing education quality through improved textbook publishing and distribution mechanisms, supported by strong correlations between these factors and overall education quality, as revealed by advanced statistical methods.

The academic early warning mechanism is an important measure for improving the quality of talent cultivation in local universities. However, the current mechanism is lacking in flexibility, systematicity, and synergy, which reduces its effectiveness and relevance. This paper examines the problems of the current academic early warning mechanism at a local university using a combination of literature review and descriptive research. Under the background of "Three All-round Education", focused optimization suggestions are offered, and an 'academic early warning mechanism for college students' is built with subject synergy, cycle development, and three-dimensional support. The study suggests optimization strategies based on the concept of "Three All-round Education". It also develops an academic early warning system for college students that includes' subject synergy,' 'cycle development,' and 'three-dimensional support.' The study found that by strengthening the synergistic efficacy of the main body of education, basing it on the whole cycle of academic development, and strengthening the three-dimensional support system, an all-member, all-process, all-around academic early warning mechanism can be effectively constructed, which will help colleges to strengthen the construction of academic atmosphere. This mechanism can assist colleges in strengthening the foundation of academic culture and improving the quality of talent development. This study applies the concept of "Three All-round Education" to explore the optimization path of college students' academic early warning mechanisms. The study enriches the theory of education management and provides important theoretical contributions and practical guidance for improving the college academic management system and optimizing teaching quality.

In the banking industry, data accuracy and integrity are paramount for ensuring regulatory compliance, operational efficiency, and risk management. However, manual data validation processes often lead to delays, errors, and inefficiencies, posing challenges for financial institutions. To address these issues, many banks are turning to automation to streamline data validation processes and improve accuracy. Using data automation, banks optimize time-consuming manual tasks such as data entry, validation, and retrieval by creating automated software processes that execute these tasks quickly and accurately. This case study examines how a hypothetical bank uses automation to improve data validation processes, focusing on Albanian Identity Numbers (AINs). By automating validation tasks, the bank enhances accuracy, compliance, and efficiency, mitigating the limitations of manual processes. The study outlines the implementation steps, discusses results, and highlights the importance of automating data validation in the banking sector.

The establishment of an evaluation model for bus operation efficiency is crucial in determining the bus routes along the rail transit line that require optimization, laying the theoretical foundation for the integration of the two networks. Firstly, a qualitative and quantitative method was used to select bus routes that have a high correlation with the rail transit system and are of research significance. Secondly, an evaluation system was constructed based on the rationality of the bus network itself and the synergy between the two networks. Finally, the Pareto optimal cross-efficiency model was employed to determine the operation efficiency of the bus routes along the rail transit line. Taking the example of Line 1 in Nanchang, 25 bus routes along the line were identified and empirically analyzed. The results indicate that the Pareto optimal cross-efficiency models score is more readily accepted by all DMUs (decision-making units). The data analysis reveales that the bus network in Nanchang exhibites good self-rationality. However, in terms of synergy between the two networks, there is room for improvement by optimizing route directions and adjusting route duplication coefficients to enhance their collaborative nature. Finally, this paper is based on the Pareto cross-efficiency mean values of both factors, the 25 bus routes were categorized into four cases, providing practical and valuable insights for the managers and decision-makers of the public transportation system. [ABSTRACT FROM AUTHOR]

Park-and-ride (P&R) facilities, when combined with the advantages of urban rail transit, have the potential to alleviate congestion problems and promote multimodal transportation. However, some facilities face operational and utilization issues in terms of efficiency and capacity, which may not fully meet users' needs. Therefore, it is essential to conduct research that addresses challenges related to user parking and facility operation. A critical step in this direction is to study relevant indicators that comprehensively measure the operational efficiency and capacity of the facilities, and to find effective methods to tackle problems in P&R operation. This study examined and selected data indicators for P&R facilities around targeted stations, taking those around Nanjing urban rail transit stations as an example. Data envelopment analysis was employed to establish a P&R operational efficiency and capacity evaluation model. This model was enriched with a comprehensive evaluation of multiple factors that affect P&R, with these factors significantly influencing the recorded evaluation results. By identifying operational issues in P&R facilities around Nanjing urban rail transit stations, this study proposed optimization solutions to enhance their operational efficiency and capacity. The findings can serve as a reference for the coordinated development of P&R facilities and urban rail transit. [ABSTRACT FROM AUTHOR]

The increasing use of imaging services and continuous technological advancement require regular optimisation of the hospital's radiology department. Assessing stakeholders to find realistic challenges is an essential step towards achieving the desired quadruple aim (reducing costs, increasing quality of care and improving patient and clinician experience) in healthcare. The statistical gaps explored and the evidence in the literature justify the need for continuous and rapid assessment, identification of challenges, identification of trends and subsequent search for solutions in each radiology department. We have found an average increase of 7.3 per cent annually in reported computed tomography, magnetic resonance imaging and interventional radiology services over the last 10 years, but the number of radiologists has only increased by 2 per cent per year. In Germany, the statistics show a continuous increase in examinations per radiologist with a compound annual growth rate of 4.68 per cent. This article presents a pathway for planning, evaluating and subsequently improving individual radiology departments using a transdisciplinary approach to innovation. The transdisciplinary innovation approach was applied with 36 young innovation fellows with multidisciplinary knowledge from different global locations (Germany, India, China, UAE and USA). As part of our transdisciplinary approach, a qualitative approach was used to collect challenges during hospital visits. We identified over 100 key challenges and 10 healthcare trends from the clinical area. We developed templates for mapping the status quo, trends and challenges in radiology for each radiology department as a tool for assessment. We believe that the proposed transdisciplinary methodology, the resulting radiology model, the template(s) and the proposed digital infrastructure will be useful in the future mapping and optimisation of radiology departments. [ABSTRACT FROM AUTHOR]

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature's focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems. [ABSTRACT FROM AUTHOR]

Aims/Background: In an era where patient-centred care is paramount, effectively managing and analyzing hospital complaints is crucial for improving service quality and patient satisfaction. This study examines hospital complaints to enhance management practices by differentiating between surgery-related and non-surgery-related grievances. By identifying patterns in complaint types and outcomes, we aim to inform targeted quality improvement strategies that address specific patient concerns and boost operational efficiency. Methods: The study utilized data from an internal complaint management system over one year. Complaints were categorized as either surgery-related or non-surgery-related. Descriptive statistics and cross-tabulation analysis were employed to examine the data. The sample comprised 132 complaints, with 67 being surgery-related and 65 non-surgery-related. Results: The analysis revealed that surgery-related complaints frequently involved issues with 'Patient Communication' and 'Surgical Error', while non-surgery-related complaints were primarily about the 'Medical Treatment Process'. The Surgery Department received the highest number of complaints, indicating a critical area for intervention. Additionally, the correlation between complaint types and outcomes provided insights into potential areas for improvement. Conclusion: The findings highlight the need for targeted communication training and procedural enhancements in surgical departments. Non-surgical departments should focus on improving treatment protocols and transparency. These strategies can reduce complaints and improve patient satisfaction. Future research should develop and test interventions based on these insights to further enhance healthcare quality. [ABSTRACT FROM AUTHOR]

This study examines the impact of artificial intelligence (AI) on the accuracy and efficiency of modern accounting systems through a qualitative literature review. Sources discussing the implementation and effects of AI in accounting were selected and analyzed. The findings reveal that AI significantly improves accuracy and operational efficiency by automating tasks and optimizing processing times. However, it faces challenges such as complex data interpretation and information security issues. The discussion underscores the need for continuous training and improvements in security policies to handle these challenges. In conclusion, although AI transforms accounting positively, its success will depend on proactive adaptation to new technologies and collaboration among professionals in the field. This analysis provides a foundation for future research and underscores the importance of proactive strategies to maximize the benefits of AI in accounting. [ABSTRACT FROM AUTHOR]

The tourism sector is one of the economic sectors most affected by the COVID-19 pandemic. Accordingly, this study aims to explore the impact of this pandemic on the operational and financial performance of tourism-listed companies on the Egyptian Exchange. For that purpose, the efficiency of these companies is evaluated using DEAW and SBM-DEA models, besides investigating their determinants from internal and external resources using dynamic panel data regression. The empirical results exhibited that company resources, such as operating expenses and employee costs, negatively impact their financial and operational efficiency. While total assets did not affect the financial efficiency of companies. Also, operational and financial efficiency are positively affected by the total operating income and sales revenues during this pandemic. Interestingly, the results showed that the COVID-19 pandemic does not impact both types of efficiency for companies operating in this sector. The study provided some managerial insights and empirical evidence that may help companies, policymakers, and investors to confront the repercussions of this pandemic and aftermaths. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aimed to develop a resilient benchmarking system for small hotels in Ukraine, designed to ensure their survival and growth amid global disruptions and local crises. Given the severe challenges associated with the COVID-19 pandemic and military actions, the resilience of the regional tourism business is particularly relevant. The methods used in this study, including factor and cluster analysis, taxonomy, and dendrograms, enabled the development of development programs for two clusters of hotels: those located in areas with increased military risk and those in relatively safe territories. The taxonomic analysis revealed significant differences in managerial practices and operational efficiency, largely determined by the geographic location of the hotels. Hotels in active combat zones experienced a 40% reduction in tourist flow and financial instability, while hotels in safe areas demonstrated a 30% higher level of customer satisfaction. The application of advanced security systems and modern marketing techniques led to a 40% reduction in incidents. [ABSTRACT FROM AUTHOR]

In Older to improve the operational efficiency of Chinese textile and clothing enterprises, 54 textile and clothing listed enterprises in China from 2018 to 20 were suspected as the research objects. Based on the data envelopment analysis ( DEA ) theory, the etteuation index system of operational efficiency in the digital transformation context was constructed, and DEA related models were used to analyze the operational efficiency of enterprises from both static and dynamic perspectives. The static analysis results show that the average comprehensive efficiency of the sample enterprises from 2018 to 2022 is between 0. 700 and 0. 800, and there is still a large room for improvement in the future; The number of enterprises with comprehensive efficiency is 8-14, indicating that the operational efficiency of most enterprises is in an invalid state of DEA. The number of companies with increasing returns to scale increased from 4 to 11, indicating that the number of enterprises with decreasing input-output ratio increased. The results of dynamic analysis show that the average value of total factor productivity index is greater than 1. The number of enterprises with total factor productivity index greater than 1 accounted for 61. 1%, indicating that the operation efficiency of most enterprises was in a state of progress. The technical progress decreased continuously from 1. 019 to 0. 968, indicating that the technological progress rate of enterprises decreased. In the future, enterprises should pay attention to the investment of digital talents and technologies, improve the operation system and improve the operation efficiency. [ABSTRACT FROM AUTHOR]

Based on the practical needs of cross-border trade between Yunnan Province, China, and Southeast Asian countries, this paper studies the impact of blockchain technology usage on operational performance. Owing to immutability and traceability, the implementation of a blockchain-based platform solves several existing problems of cross-border trade. We conducted a field experiment to verify the effect of blockchain technology on the operational efficiency of cross-border trade companies. The results show that operational, agency, and time costs significantly decrease. Blockchain usage reduces the intermediary agencies of cross-border trade and settlement payment for traders, also improves the timeliness of business processing and the efficiency of cross-departmental business collaboration. This study provides new insights on the interface of blockchain technology and supply-chain operation management. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today, the concept of "smart ports", which are designed to enhance operational efficiency and competitiveness, has emerged as a key paradigm in the port and maritime industry. As an integral component of maritime transportation networks, container terminals in ports play a crucial role in facilitating international trade and stimulating economic growth. Accordingly, the Korean government has steadily pursued policies and investments to improve the technological capabilities of ports, focusing on container terminals. However, there is still a lack of comprehensive research that thoroughly analyzes the operational efficiency of container terminals in domestic ports undergoing smart port technology implementation. Therefore, this study analyzes the operational efficiency of 20 container terminals across five major domestic ports, which are forerunners in smart port transformation, as declared by the Korean government. This study employs a Principal Component Analysis (PCA)–Data Envelopment Analysis (DEA) approach to increase the effectiveness of the analysis and addresses efficiency in two aspects: static and dynamic. The findings indicate that container terminals under Ulsan Port and Busan Port (New) have the best operational efficiency. In particular, Ulsan Port demonstrates significant improvement in terms of efficiency changes. The conclusion emphasizes the pivotal role of technological progress in achieving higher efficiency among container terminals. In addition to providing policymakers and port managers with useful insights into smart port transformation, this study is expected to make theoretical contributions to expanding the perspectives of port efficiency research by simultaneously encompassing static and dynamic analyses. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of R&D intensity on operational efficiency and strategic alliances in medium-sized companies. Specifically, it seeks to understand how these variables interact to influence a firm's commitment to research and development activities, ultimately affecting their innovation and market performance. A cross-sectional design was employed, with a sample of 230 participants drawn from medium-sized companies. The sample size was determined using the Morgan and Krejcie table. Data were collected through structured questionnaires assessing R&D intensity, operational efficiency, and strategic alliances. Pearson correlation analysis was conducted to examine the relationships between the dependent variable (R&D intensity) and each independent variable (operational efficiency and strategic alliances). Linear regression analysis was performed to explore the combined effect of the independent variables on R&D intensity. All analyses were conducted using SPSS version 27. Pearson correlation coefficients indicated significant positive relationships between R&D intensity and operational efficiency (r = 0.53, p = 0.001), and between R&D intensity and strategic alliances (r = 0.47, p = 0.002). The regression analysis showed that operational efficiency and strategic alliances together explain 40% of the variance in R&D intensity (R² = 0.40, F(2, 227) = 19.25, p = 0.000). Multivariate regression results confirmed that both operational efficiency (B = 0.07, β = 0.42, p = 0.001) and strategic alliances (B = 1.10, β = 0.35, p = 0.000) are significant predictors of R&D intensity. The study concludes that operational efficiency and strategic alliances significantly enhance R&D intensity in medium-sized companies. These findings suggest that improving operational processes and fostering strategic partnerships are critical for increasing a firm's investment in research and development. The results are consistent with previous research and provide valuable insights for both academia and industry practitioners. Future research should consider longitudinal designs and explore additional variables to further understand these relationships. [ABSTRACT FROM AUTHOR]

This study aims to explore the adoption and implementation of emerging technologies in Small and Medium-sized Enterprises (SMEs). By understanding the motivations, challenges, benefits, and future plans of SME founders, this research provides insights into how these businesses leverage technology to enhance their competitiveness and operational efficiency. The study employed a qualitative research design, utilizing semi-structured interviews with 25 founders of SMEs across various industries. Participants were selected using purposive sampling to ensure diverse representation. Data collection continued until theoretical saturation was achieved. The interview data were transcribed and analyzed using NVivo software, following a thematic approach to identify key themes and insights related to technology adoption and implementation in SMEs. The study identified several motivations for technology adoption, including competitive advantage, cost efficiency, customer demand, innovation drive, and growth opportunities. Key challenges faced by SMEs included financial constraints, technical difficulties, resistance to change, regulatory and compliance issues, knowledge gaps, vendor dependence, and time constraints. The benefits realized from technology adoption encompassed improved operational efficiency, enhanced customer satisfaction, revenue growth, better data analytics, increased employee productivity, and greater market adaptability. Future plans of SMEs included continued investment in technology, scaling up implementation, focusing on employee training and development, forming strategic partnerships, enhancing cybersecurity measures, and improving customer engagement. The adoption and implementation of emerging technologies present significant opportunities for SMEs to enhance their competitiveness and operational efficiency. However, SMEs must navigate various challenges to realize these benefits. By adopting a strategic approach and leveraging external partnerships, SMEs can successfully implement new technologies and drive sustainable growth. This study provides valuable insights into the experiences of SME founders, informing both practice and policy in the context of technological advancement in small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

Objective: This study aims to explore the implementation and impact of management and technical standards within fitness clubs. Methodology: Employing a qualitative research design, the study involved semi-structured interviews with 15 participants, including university professors in sports management, managers and experts from the licensing commission of fitness clubs, and standards specialists. The thematic analysis was used to interpret the data, focusing on the motivations for standard adoption, the processes of implementing these standards, and the perceived impacts on organizational performance and stakeholder engagement. Findings: The study reveals that fitness clubs adopt management and technical standards primarily to improve operational efficiency, ensure compliance with regulations, and enhance service quality. Challenges in standardization include aligning internal processes with external requirements and managing stakeholder expectations. Successful standardization efforts lead to improved operational efficiency, better stakeholder management, and enhanced competitive advantage. The research also highlights the importance of voluntary environmental programs and ISO certifications in signaling commitment to quality and sustainability. Conclusion: The adoption of management and technical standards in fitness clubs plays a crucial role in enhancing operational efficiency, regulatory compliance, and stakeholder satisfaction. Despite the challenges faced during implementation, the benefits of standardization are clear, suggesting that fitness clubs should continue to engage in these practices. Future research should aim to broaden the geographic and demographic scope of participants and incorporate quantitative measures to further validate the findings. [ABSTRACT FROM AUTHOR]