Operational Performance Research Articles

Characterizing Performance Tradeoffs from Air Traffic Management Services for Advanced Air Mobility

The emergence of Advanced Air Mobility (AAM) has called for novel Air Traffic Management (ATM) solutions to enable the projected high number of operations, particularly in already dense and complex metroplex airspace. Understanding the multidimensional performance impacts of developing air traffic rules, procedures, and services is key to guiding this new traffic management system design. This paper presents a simulation framework to provide a quantitative assessment of the operational performance of ATM services in support of AAM. The framework embeds several modules for high-fidelity modeling of urban air traffic flows based on actual data, ATM service provision, stochastic simulation, and performance quantification. Two key services are analyzed: airspace geofencing and strategic deconfliction. We evaluate the use of these services in a scenario of air mobility operations associated with daily urban commuting in the Sao Paulo metropolitan region. Monte Carlo experiments are performed to simulate the execution of aircraft trajectories managed under different ATM service configurations, allowing for the characterization of operational performance tradeoffs, particularly in terms of safety and efficiency. The work contributes to quantifying the value of novel ATM services for integrating AAM operations in dense metroplex airspace, supporting their design and implementation.

Examining the Psychometric Impact of Targeted and Random Double‐Scoring in Mixed‐Format Assessments

AbstractDouble‐scoring constructed‐response items is a common but costly practice in mixed‐format assessments. This study explored the impacts of Targeted Double‐Scoring (TDS) and random double‐scoring procedures on the quality of psychometric outcomes, including student achievement estimates, person fit, and student classifications under various conditions that reflect operational performance assessments. Using a simulation study, our results suggest no notable advantages for TDS over the random double‐scoring approach across various psychometric outcomes, regardless of conditions related to student misfit, rater misfit, and rater severity. This study holds significant implications for mixed‐format assessments, offering insights into a comprehensive evaluation of double‐scoring methods. We recommend that researchers consider these findings when considering among double‐scoring procedures.

How does data factor utilization stimulate corporate total factor productivity: A discussion of the productivity paradox

Stimulating total factor productivity through the utilization of data factors is a crucial concern for companies operating in the digital era. This study employs data from A-share listed firms from 2011 to 2022 to examine the impact and mechanisms of data utilization on corporate total factor productivity. The findings reveal that effective data utilization can significantly improve corporate total factor productivity, but no evidence supports the productivity dilemma. The stimulating effects are achieved by alleviating investment inefficiency and resource redundancy. Further analysis verifies that firms` financial performance and life cycle can lead to heterogeneous effects. Specifically, the positive impact is more pronounced in firms with lower operational performance and in periods of maturity and recession.

Analysis of the influence of different support types on the operation performance of bidirectional shaft tubular pump

Abstract Because of the particularity of the pump’s bidirectional operation, and the difference of the flow channels on both sides of the pump. The device has a mature impeller and guide vane combination when it is running forward. And when the device runs in reverse, the support on the shaft side acts as the rear guide vane of the impeller, which has great influence on the performance of the device. However, due to the few operating conditions in reverse operation, the importance of support is often ignored. In this paper, the influence of different support types on the bidirectional flow pattern and performance of the device is analyzed by numerical simulation. The results indicate that by optimizing the variation trend of the edge arc radius of the supporting baffle section, it is possible to effectively control the section with poor flow distribution, thereby reducing hydraulic losses. The support adopts three partitions arranged at an interval of 120°, which has little difference in performance during forward operation compared with other schemes. But during reverse operation, it reduces the impact and friction losses of water flow. And it fully utilizes the direct guide vane function of recovering vorticity, resulting in a significant increase in efficiency. At the same time, the accuracy of numerical simulation results is verified by model tests. The stability of positive and negative operating pressure pulsation supporting the inlet and outlet under the optimal scheme is analyzed.

High-fidelity model development of CO2 booster refrigeration systems in supermarkets using field measurements

Supermarket refrigeration systems adopting traditional refrigerants with high global warming potential (GWP) have impacts on global warming for indirect and direct greenhouse gases (GHG) emissions. CO2 is a popular low-GWP alternative. The transcritical operation of CO2 systems worsens their energy performance, but provides recoverable heat as a heat source to reduce gas consumption. To evaluate operation performance, data-driven models, trained by historical data, are weak in implementation with datasets outside the scope of training data; in contrast, theoretical models have better extrapolation ability to calculate all operation conditions of CO2 systems at supermarket. Existing theoretical modeling approaches often lack validation against the limited public-access data, which reduces model reliability for further applications, and adopt oversimplified inference methods for unmeasured variables, which increases the risks of breaking thermodynamic laws and lowering model accuracy. This study therefore develops a steady-state theoretical model for CO2 booster refrigeration systems validated against field measurements from three UK supermarkets. The available measurements are utilized to the best level to ensure model accuracy and physical interpretability. Proposed methods to infer missing variables in CO2 systems include condenser outlet temperature, evaporating temperature, compressor isentropic efficiency and compressor mass flow rate. Results show that proposed inference methods enhance the abilities of the proposed modelling approach to ensure data integrity, avoid breaking thermodynamic laws, and improve model accuracy by reflecting real-time actual values of unmeasured variables rather than rough assumptions. The proposed modeling approach provides satisfactory accuracy validated using high-resolution measurements across the whole year from three real supermarkets.

Hybrid Nanofluid Flow in Chamber Containing Heated and Concentrated Internal Source Under the Effectiveness of Applied Magnetic field

Extensive applications of buoyancy-induced flow owing to temperature and concentration gradients have been found exclusively in diversified industrial and technological processes, such as heat exchange systems, solar collectors, heat sinks, and power plants. Additionally, installation of internal sources in confined configurations is essential in operation performance of many devices relevant to transformers, radiators, air cooling engines, fuel cells, semiconductor instruments and so forth. Furthermore, in recent decades, hybridized nanoparticles have been added to different setups to increase their efficiency. Therefore, the premier motive of current work is to examine mass and heat transport mechanisms in water-based fluids encompassed in a corrugated rectangular chamber with the induction of copper (Cu) and alumina (Al2O3) in a hybrid manner. A horizontally oriented magnetic field is assumed, along with the adjustment of a uniformly heated and concentrated source. The thermal conductivity relation proposed by Maxwell is obliged to scrutinize the performance of hybridized nanoparticles in controlling heat exchange in the domain. In this study, a power-law viscosity model is manifested along with utilization of other conservation laws and model structuring in the form of dimensionless expressions is performed by accomplishsing variables. Finite element simulations are executed by utilizing COMSOL Multiphysics 6.0 to solve developed governing equations system. The quantities of interest, such as average heat and mass fluxex, along with presentation of percentage change versus sundry factors, through graphical and tabular display. From a thorough examination, it is inferred that magnetic field plays an effective significance in controlling the excessive thermosolutal gradients. In addition, inudction of hybridized nanoparticles will positively affect thermal exchange in the base liquid compared to the situation when nanoparticles are not added. It is also deduced that average Nusselt and Sherwood numbers decreased up to 3% and 2.4% respectively for hydromagnetic situation (Ha = 0) in comparison to hydrodynamic case (Ha ≠ 0). Decrement in coefficients representing thermosolutal exchange reach up to 55% and 66% approximately against increment in number of corrugations. Induction of hybrid nanoparticles resulted in an increase in the coefficients of thermal and solutal transfer up to 14% and 22%, respectively, compared to the scenario where the base fluid contained no particles. For shear thinning aspects of non-Newtonain liquid, Nusselt and Sherwood numbers exceeds in comparison to Newtonian case and contrary aspects are observed for shear thickening materials.

A Novel Algal–Algal Microbial Fuel Cell for Enhanced Chemical Oxygen Demand Removal

To enhance the removal of COD (Chemical Oxygen Demand) by microalgae, this study constructed a novel microalgae–microalgae microbial fuel cell system (AA-MFC). It investigated the coupling relationship between the COD treatment efficiency at the anode and the production of high-value microalgal products at the cathode, as well as explored the effects of different initial inoculum densities and light–dark cycles. The experiment first measured the operational performance of the newly constructed AA-MFC in open-circuit and closed-circuit modes, demonstrating that this novel AA-MFC could start up rapidly within 32 h and operate stably. The results showed that the AA-MFC enhanced the removal of COD and the growth of microalgae biomass at the anode while maintaining stable power generation. When the initial inoculation density of the anode was 1.2 × 108 cell/cm2 and the light–dark cycle time was 18:6 h, the AA-MFC had the most obvious promoting effect on the COD removal of the anode. Compared with normal culture conditions, the COD removal rate increased by 26.0% to 96.1%. These results indicate that the AA-MFC can not only effectively remove pollutants, but also promote the accumulation of high-value microalgae biomass.

Experimental and numerical studies on the propagation paths of gear root cracks

Gear bending fatigue failure has a significant impact on the operational performance of advanced machinery, such as electric vehicles and wind turbines, potentially leading to failures and catastrophic consequences for transmission systems. Several methods are currently used to predict gear crack propagation; however, a comprehensive comparison of their accuracy and efficiency in predicting crack paths is lacking. In this study, the propagation path of root cracks in commonly used automotive gears was investigated using finite element (FE) analysis and experimental testing. Three mixed crack path prediction criteria were integrated to predict the gear root crack propagation using the commercial software ABAQUS by user-defined Python script. The impacts of gear crack parameters, including the gear initial crack length, on gear root crack propagation behaviour were analysed. The simulations were verified by the gear bending fatigue test using a single tooth bending test device. The results indicate that the simulation outcomes align with the experimental tests, demonstrating that all three criteria are effective in predicting gear root crack propagation behaviours.

Creep-to-rupture of T91 steel in static liquid lead-bismuth eutectic: Effects of cyclic temperature and oxygen environment

The creep-to-rupture behavior of T91 steel in static liquid lead–bismuth eutectic (LBE) is investigated, focusing on the impacts of cyclic temperature and oxygen condition. The results indicate that thermal cycling, oxygen deficiency, and high applied stress levels significantly accelerate creep deformation and reduce the creep-to-rupture lifetime of T91 steel in LBE. Surface oxide scales on T91 steel and their self-healing mechanisms play a crucial role in enhancing the creep resistance by isolating the LBE contact and delaying crack initiation and propagation. However, the integrity of this oxide scale is compromised under cyclic thermal conditions and low oxygen levels, leading to premature failure. Microstructural examinations reveal the evolution of oxide scale damage and self-healing mechanisms. The findings suggest that oxide scale failure mechanisms should be considered when designing the long-term operational performance of advanced LBE-based reactors.

The mediating role of supply chain responsiveness in the relationship between supply chain integration and operational performance

The mediating role of supply chain responsiveness in the relationship between supply chain integration and operational performance

Mitigating data inaccuracy and supply chain challenges in Western Romania's automotive industry

The purpose of this paper was to emphasize the importance of data accuracy within internal logistics systems and their extended influence on supply chains in automotive industry through 6-month multiple case studies conducted on 3 first tier original equipment manufacturers (OEM) based in Western Romania. The study investigates the most common causes of data inaccuracies among automotive suppliers and their approaches to reduce consequential supply chain issues and be more agile. Data collection and analysis revealed that main issues arise due to ordering quantities mismatching actual customer demand, a wide range of order lot sizes, lead times and delivery reliability concerns and the reluctance to shift away from mainstream cost-effectiveness and towards strategic added value thinking. These issues sourced significant other related operational challenges such as excessive inventory, short-term stockouts and subsequent express shipping services or product-related inconveniences (quality and capacity levels, contracted volumes and dedicated lines). The paper sources different logistics and supply chain strategies used by the 3 OEMs, their features and operational performance, as well as their overall effectiveness, which can be applied by other automotive industry suppliers to improve own results. Introducing more reliable real-time data collection tools and performance metrics has started hauling more focus towards solving these prevalent issues with some ongoing improvement projects showing up to 25% better results. For one of the 3 OEMs introducing a new warehouse management system has already sourced an overall quality increase (5 percentage points) due to a 60% higher utilization of its production equipment.

중국 중장비 제조기업의 친환경 공급망관리가 기업성과에 미치는 영향에 대한 연구

Purpose: The purpose of this study is to empirically analyze the impact of green supply chain management (Green SCM) on corporate performance in China's heavy machinery manufacturing. Research design, data, and methodology: This study employed a survey to collect data from key Chinese companies in heavy machinery manufacturing. The questionnaire was developed through a pilot study and in-depth interviews, and the survey was conducted from April to June 2024. Data analysis was performed using SPSS 24.0 and AMOS 24.0. Results: Analysis revealed that internal environmental management, eco-design, and investment recovery inf luenced both environmental and operational performance positively. Eco-design had the greatest impact on environmental performance, while internal environmental management and investment recovery significantly contributed to operational performance. The total explained variance was 72.871%, indicating meaningful correlations between variables. Conclusions: This study demonstrates that Green SCM enables companies in the heavy machinery industry to fulfill their environmental responsibilities while improving operational efficiency. The findings highlight the importance of strengthening eco-design and internal environmental management practices for sustainable business development.

Optimization of Solar PV System Efficiency in Bangladesh

This paper presents a comprehensive review and analysis of Sarishabari Engreen Solar Plant Ltd., a 3.3 MW grid-connected solar photovoltaic (PV) system located in Sarishabari, Jamalpur, Bangladesh. The study evaluates the plant's economic and operational performance, revealing a competitive payback period of 10.1 years and a levelized cost of energy (LCOE) of 0.11 USD/kWh. These metrics highlight the plant's financial viability, largely due to the low cost of public land used for construction. However, profitability may be challenged if similar projects require significant investments in private land acquisition. Key areas for improvement identified include optimizing the tilt angle and integrating smart automation systems. Additionally, the potential for hybrid renewable energy systems combining solar and wind power is discussed. The paper also provides actionable recommendations for future renewable projects, emphasizing the importance of advanced technologies, and supportive policies. These insights aim to inform the optimization of existing solar PV systems and guide the development of future renewable energy projects in Bangladesh, contributing to the country's sustainable energy goals.

Evaluation of Financial Performance with the Balanced Scorecard Approach in Retail Companies

This research assesses the financial performance of retail enterprises utilizing the Balanced Scorecard (BSC) methodology. The study used a quantitative methodology, gathering data from 75 retail firms via a Likert-scale questionnaire (1 to 5). The analysis use SPSS version 26, concentrating on four key Balanced Scorecard perspectives: financial, customer, internal business processes, and learning and growth. The findings indicate substantial positive relationships across the customer, internal processes, and learning and growth perspectives with financial success, with the customer viewpoint serving as the most robust predictor. The results indicate that retail firms can improve financial performance by prioritizing customer satisfaction, operational efficiency, and ongoing innovation. These insights offer significant recommendations for retail managers seeking to synchronize strategic objectives with operational performance.

The impact of administrative efficiency on SME Growth and Sustainability

enterprises (SMEs). This review examines the relationship between efficient administrative processes and the long-term success of SMEs, emphasizing how streamlined operations, resource management, and decision-making can enhance business performance. SMEs often struggle with limited resources, resulting in operational bottlenecks and inefficiencies that hinder their capacity to scale and sustain growth. By improving administrative efficiency through the adoption of technology, organizational restructuring, and optimized processes, SMEs can achieve greater productivity, reduce costs, and make informed decisions that promote sustainability. The review employs a case study approach, analyzing SMEs across different sectors—manufacturing, retail, and services to demonstrate the tangible benefits of administrative efficiency. The research includes qualitative interviews, operational data analysis, and performance evaluations before and after implementing efficiency-enhancing measures. Key findings highlight improvements in areas such as time management, cost reduction, and customer satisfaction, all contributing to accelerated growth and competitive advantage. Additionally, the study explores how administrative efficiency supports sustainable practices, such as resource optimization and risk management, leading to long-term business viability. Despite these advantages, challenges such as resistance to change, lack of skilled personnel, and financial constraints pose barriers to achieving administrative efficiency in SMEs. The review concludes by offering practical recommendations for overcoming these challenges, including investing in digital tools, employee training, and organizational restructuring. Ultimately, the research underscores the importance of administrative efficiency as a critical factor for SME growth and sustainability, providing insights for policymakers, business owners, and managers aiming to enhance the operational performance of small businesses in a competitive market.

Exploring the Potential Synergies between Industry 5.0 and Green Lean Six Sigma for Sustainable Performance: A New Dimension of Operational Excellence

Today's companies are adopting a sustainable strategy, often in a reactive manner, to respond to customer demands, stakeholder concerns, and compliance with regulatory requirements, while seeking to proactively maintain a strong standing in the future market. This article explores the synergies between Industry 5.0 technologies and Green Lean Six Sigma (GLSS), highlighting how their integration can catalyse sustainable transformations in manufacturing. The study combines a literature review with empirical survey conducted across companies in various industrial sectors. The objective is to identify and examine potential synergies between Industry 5.0 technologies and Green Lean Six Sigma tools to improve the environmental impact of manufacturing companies. The results highlight the importance of Industry 5.0 technologies in boosting the efficiency of GLSS, thereby promoting more sustainable manufacturing practices. Key areas of synergy include sustainable process optimization, energy efficiency, sustainable personalization, data analytics for sustainability, and sustainable innovation. This article emphasizes the mutual benefits of integrating Industry 5.0 technologies with the Green Lean Six Sigma methodological framework. This combination reduces the consumption of materials and resources, improves energy efficiency, and stimulates innovation in favor of eco-responsible production. It calls for an integrated approach, considering technology, human resources, and processes, to enable manufacturing companies to achieve optimized operational performance while contributing to environmental sustainability.

Influence of Expected Loan Loss on Operational Performance of Commercial Banks in Kenya

Purpose: The main objective of this study was to examine influence of credit risk stress testing on operational performance of commercial Kenya. The theoretical framework was based on credit risk theory. Methodology/Approach: The study used all commercial banks which are 42 it total. The adopted mixed research comprising of causal and longitudinal research designs. The study used secondary data from operational statements of banks. Descriptive applied involved skewness, kurtosis, mean and standard deviation. Multiple regression analysis was also applied. Data was presented using tables. Findings: The results revealed that expected loan loss lead to performance of commercial Banks as shown by a positive and significant coefficient of in the regression model in a fixed effect model. Implications: The study suggested that operational institutions implement a stringent policy that escalates the loss in the event of default, thereby restricting loan approvals to clients who can guarantee they will repay the loan in full.

Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency

Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.

Authenticity and high performance: Nonmarket social, public and media strategies of foreign SMEs during a black swan event

AbstractNonmarket (NM) strategy is an emerging concept, similar to the notion of NM social, public and media strategy (SPMS). Previous literature has predominantly focused on larger corporations, often overlooking smaller enterprises. Additionally, much of the existing research has been centred on developed markets, leaving a gap in qualitative research. The current analysis seeks to elucidate how foreign small and medium‐sized enterprises (FSMEs) in transitional markets, such as Shanghai, adopt specific NM SPMS to mitigate the liability of foreignness (LOF) during a pandemic. The study utilizes a CGT approach, employing semi‐structured interviews with 27 participants, including foreign entrepreneurs and digital marketing specialists. Additionally, qualitative data was analysed comprehensively using Atlas.ti 9 software. The qualitative methodology implementation resulted in the creation of the authenticity and high performance framework from where specific strategies and general implications are presented. The study highlights two principal themes: (1) The need for increased flexibility and speed in social media message communication and (2) Customized FSME strategies through digital marketing—a never‐going‐back market digitalization. The second theme is subdivided into (a) Tailored public relations for FSMEs. (b) An increased need for internal cohesion—fostering a stable mindset and positive rapport among staff. (c) An increased need for trialling new target customers. (d) Creating a relatable story—showing a particular vulnerability can be viral and (e) Maintaining a strong reputation for operational performance.

Balancing plant safety and efficiency through innovative engineering practices in oil and gas operations

In the oil and gas industry, balancing plant safety with operational efficiency is a critical challenge that requires innovative engineering practices. This paper explores how advanced engineering techniques can enhance both safety and efficiency in oil and gas operations. By integrating cutting-edge technologies and methodologies, companies can achieve a harmonious balance between protecting personnel, equipment, and the environment, while also optimizing productivity and resource utilization. Key innovative engineering practices discussed include the implementation of real-time monitoring systems and predictive analytics to enhance safety and operational efficiency. Real-time monitoring, powered by Internet of Things (IoT) sensors, allows for continuous tracking of plant conditions, detecting anomalies before they escalate into significant issues. Predictive analytics, utilizing machine learning algorithms, further aids in forecasting potential failures and scheduling maintenance proactively, thereby minimizing downtime and operational disruptions. The paper also examines the role of risk-based approaches and safety management systems in improving plant safety. Techniques such as quantitative risk assessment (QRA) and safety integrity level (SIL) analysis are pivotal in identifying potential hazards and designing mitigation strategies. These practices ensure that safety measures are proportionate to the actual risk, avoiding over-engineering while addressing critical safety concerns. Moreover, the integration of automation and digital twin technology is discussed as a means to enhance both safety and efficiency. Automation reduces human error and improves precision in operations, while digital twins provide a virtual replica of physical assets, enabling simulation and optimization of plant operations in a risk-free environment. Case studies illustrate successful implementations of these practices, highlighting their impact on reducing incidents and improving operational performance. The paper concludes by emphasizing the need for ongoing innovation and collaboration between engineers, safety experts, and operational managers to continuously advance safety standards and operational efficiency in oil and gas operations.