Operating Performance Assessment Research Articles

Assessment of Operational, Environmental and Social Performance of Container Ports in Türkiye

ABSTRACT Purpose: This study aims to reveal the sustainability orientations of container port facilities operating in Turkey through sustainability reports and to evaluate the performance of their operational, environmental and social dimensions both separately and in an integrated manner (Environmental, Social, Operational-ESO). Methodology: Sustainability orientations of container port facilities were subjected to qualitative assessment through examination of web pages and sustainability reports. The data obtained through document scanning regarding operational, environmental, and social performance indicators revealed by researching the relevant literature were analyzed using the MULTIMOORA method. The Rank Position Method was used in the performance ranking of port facilities. Findings: The results show that 18% of the container service port facilities publish independent sustainability reports. There are deficiencies in the environmental and especially social performance indicators taken into account in the sustainability reports. Among the port facilities examined, Mersin International Port, which has the highest operational performance, is also ranked as the facility with the lowest integrated performance (ESO). The port facility with the highest integrated performance (ESO) was Socar. Originality: The study contributes to filling the gap in the literature regarding the evaluation of environmental, social and operational performance of container ports in Turkey with the MULTIMOORA method. More importantly, the integrated examination of relevant performance dimensions represents the originality of this study.

Assessment of operator performance during dynamically guided osteotomy and root-end resection in endodontic microsurgery: a cross-sectional in vitro study

ObjectivesTo compare in vitro the magnitude of deviations, task-focused performance, and perspective on instrumentation of two operators with different levels of experience using computer-assisted dynamic navigation (C-ADN) systems during osteotomy/root-end resection (RER) in models mimicking thick bone layers overlying the apices. MethodsEight models with 15 tooth replicas each were assigned equally to two operators (experienced/less experienced). Pre- and post-operative cone-beam computed tomography images were superimposed using a C-ADN software to determine discrepancies between the planned and real trajectory of the trephine bur. Task-focused operator performance and subjective aspects derived from operator's perspective related to C-ADN were also analyzed. Statistical analysis was completed through parametric methods and Chi-square/fisher exact tests. ResultsPearson correlation tests showed positive correlations between the distance from buccal cortical plate to root apex regarding deviation at apex, trephination depth, and visuomotor coordination time. The less experienced operator had significantly greater values of visuomotor coordination time, deviation at buccal entry point and apex, trephination depth, and angular deviation, as well as a significantly higher frequency of apex location failures, poor RER quality, and mishaps in comparison with those of the experienced operator, thus leading to a significantly higher task-focused performance for the experienced operator. Both operators improved the visuomotor task through diverse attempts, acknowledged enhanced situation awareness, surgical procedure quality, and patient safety associated to the use of C-ADN, and reported a positive impact on the developing and maintaining navigational skills. Concerns regarding perceived workload, mental demand, and usability difficulties were reported only by the less experienced operator. ConclusionsDynamically guided osteotomy and RER prepared with trephine burs entail a subset of visuomotor skills requiring a special learning which may vary depending on the operator's experience and the procedure complexity as determined by dentoalveolar anatomy. Likewise, the perceived usability about C-ADN systems increases insofar the complexity of the user interface decreases and upsurge the earlier experience with these systems. Statement of clinical relevanceThis study shows that both the technical characteristics involved in the function of C-ADN systems and the subjective operators' perceptions are critical for the accuracy of task-focused performance during endodontic microsurgery.

Enhancing Operational Performance through Digitalization and Industry 4.0: A Comprehensive Model for Data Reliability and OEE Optimization

In today's industrial context, three key elements are guiding the course of small and medium-sized enterprises (SMEs) towards improved productivity, efficient operations, and sustainable growth. The introduction of Industry 4.0 signifies a groundbreaking shift, integrating state-of-the-art technologies into manufacturing processes and propelling industries towards heightened efficiency and competitiveness. This article deals with the crucial role of productivity measurement in SMEs and examines the impact of data reliability on operational performance assessment. It explores the strategic use of Industry 4.0 tools to enhance data reliability in processes like production, quality, and maintenance. The research focuses on designing a comprehensive model for data collection, reliability, and utilization, ultimately aiming to improve Overall Equipment Effectiveness (OEE) within SMEs. By showcasing the synergistic integration of Industry 4.0 advancements, the article provides practical insights for SME stakeholders to optimize operational performance. The proposed model contributes to the understanding and implementation of efficient methodologies for data management, fostering sustainable improvements using calculation of OEE within SMEs. The case study was conducted in a plastics manufacturing SME that produces components for various industries. These findings can be enhanced and improved through additional case studies to refine the proposed model.

진로체험지원센터 이용이 학생의 진로체험활동 참여에 미치는 영향 분석

This study empirically analyzed the effectiveness of Career Experience Support Centers, which are operated nationwide. Established to support students' participation in career experience activities within their communities, 223 centers were in operation as of June 2022. The primary function of these centers is to support schools in facilitating career experience activities by identifying career experience sites within the community and connecting them with schools. This study aimed to verify the effective operation of these Career Experience Support Centers by examining whether their use by schools influences students' participation in career experience activities. Data for the analysis were derived from responses of third-grade in middle school students to the 'Survey on the Status of Career Education in Primary and Secondary Schools,' conducted by the Korea Research Institute for Vocational Education and Training (KRIVET). Based on survey responses from students, parents, career guidance teachers, school administrators, and homeroom teachers, school- and student-level variables were identified. To determine whether the use of Career Experience Support Centers by schools and the satisfaction with these centers influence students' participation in career experience activities, a multilevel logistic regression analysis was conducted. The analysis revealed that the use of Career Experience Support Centers by schools has a statistically significant effect on students' participation in career experience activities. Additionally, satisfaction with the centers positively impacts students' participation. It was found that the likelihood of students engaging in career experience activities increases by 1.4 times with the use of the centers and by 1.7 times with high satisfaction. Based on the analysis results, this study suggests enhancing the status and functions of Career Experience Support Centers and calls for an objective assessment of these centers' operational performance.

UNC Charlotte Autonomous Shuttle Pilot Study: An Assessment of Operational Performance, Reliability, and Challenges

This paper presents the findings from an autonomous shuttle pilot program conducted at the University of North Carolina at Charlotte between June and December 2023 as part of the North Carolina Department of Transportation’s Connected Autonomous Shuttle Supporting Innovation (CASSI) initiative. The shuttle completed 825 trips, transporting 565 passengers along a 2.2-mile mixed-traffic campus route. The study evaluates the shuttle’s operational performance, reliability, and challenges using data from onboard sensors, system logs, and operator reports. Key analyses are divided into four areas: service reliability, which assesses autonomy disengagements caused by signal loss, technical issues, and environmental factors; service robustness, focusing on the shuttle’s ability to maintain operations under adverse conditions; performance metrics, including average speed, autonomy percentage, and battery usage; and service usage, which examines the number of trips and passengers to gauge efficiency. Signal loss and battery-related issues were the primary causes of service interruptions, while environmental factors like weather and vegetation also affected shuttle performance. Recommendations include enhancing vehicle-to-infrastructure communication and optimizing battery management.

A benthic mesocosm system for long‐term multi‐factorial experiments applying predicted warming and realistic microplastic pollution scenarios

AbstractPredicting anthropogenic impacts on benthic marine ecosystems is of great importance for conservation. Climate change models have indicated that increasing seawater temperatures will drive shifts in the distribution of benthic organisms due to species‐specific thermal tolerances. When combined with other stressors such as pollutants, interactive effects may lead to even greater impacts. Microplastics (MP), as a marine pollutant, have been shown to elicit responses in organisms but often at concentrations far greater than experienced in the environment and with short‐term exposure times. Assessing long‐term interactive effects of MP pollution and ocean warming on benthic marine organisms has not been previously addressed. A unique mesocosm facility was constructed on the island of Helgoland, in the southern North Sea, to explore the combined impacts of these two factors. The multi‐factorial experimental system is composed of 16 independent benthic mesocosms, utilizing novel features and methods for the continuous generation of climate change and MP exposure scenarios, while retaining natural conditions for other environmental parameters. We provide a description of the system design and methods, followed by an operational performance assessment during a 10‐month exposure experiment with European flat oysters (Ostrea edulis), evaluated on the accuracy of exposure scenario control and the degree of realism achieved. We demonstrate the novel application of kinetic modeling for generating environmentally relevant MP exposure conditions (+ 25 MP L−1), and highlight the mesocosm systems suitability for studying chronic effects of MP pollution and ocean warming on benthic marine ecosystems through its real‐world application.

Enhancing operational performance assessment of structures with seismic response modification devices: The role of observability and symmetry analysis under limited sensor deployment

AbstractTo manage structural responses under various external forces, the increasing incorporation of seismic isolation and supplementary damping systems in modern civil engineering necessitates post‐installation performance assessments. The challenge of accurately inferring system information from these complex dynamical structures, especially with limited sensor deployment, could be significant. From the perspective of solving inverse problems, this challenge hinges on constructing an input‐output mapping that assures unique solutions, achievable through theoretical observability or symmetry analysis. We introduce a unified algorithm framework designed to accommodate various definitions of Lie derivatives, specifically for observability and symmetry analysis in dynamic systems with affine, non‐affine, and unknown inputs—capabilities not fully achieved in previous studies. We demonstrate its application across typical dynamic scenarios, including both linear and nonlinear examples. We also present a numerical example featuring complex isolation systems with limited sensor layouts, illustrating how uniform convergence can be achieved in estimating all system states when an observable input‐output mapping is utilized. Furthermore, an experimental example employing shaking table tests showcases the potential complications that arise when an unobservable input‐output mapping is used.

WP6.14 - Automated analysis of operative video in surgical training: Scoping review

Abstract Aims There is an increasing availability of operative video for use in surgical training. Emerging technologies can now assess video footage and automatically generate metrics that could be harnessed to improve the assessment of operative performance. This scoping review explores the current application of automated video analytics in surgical training. Methods PubMed, Scopus, Web of Science and the Cochrane database were searched to identify studies which applied automated video analytics to trainee-performed operations. Search terms included ‘trainee’, ‘video analytics’ and ‘education’. Two reviewers independently screened and extracted data on the methods of analysis, metrics generated and application to training. The Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Review (PRISMA-ScR) guidelines were followed. Results Thirteen studies were identified. Computer vision tracking was the most common method of video analysis. Metrics were generated for processes (e.g. movement of instruments) and outcomes (e.g. intraoperative phase duration). Two studies generated metrics to assess critical safety elements (e.g. critical view of safety in laparoscopic cholecystectomy). Automated metrics were able to differentiate between different skill levels (e.g. consultant vs. trainee) and correlated with traditional methods of assessment. There was a lack of longitudinal application to training and only one qualitative study reported the experience of trainees using automated video analytics. Conclusion Automated video analytics is an emerging field which provides an objective assessment of operative performance. Gathering evidence to demonstrate the impact of long-term use on training outcomes is a priority for future research.

Automated analysis of operative video in surgical training: scoping review.

There is increasing availability of operative video for use in surgical training. Emerging technologies can now assess video footage and automatically generate metrics that could be harnessed to improve the assessment of operative performance. However, a comprehensive understanding of which technology features are most impactful in surgical training is lacking. The aim of this scoping review was to explore the current use of automated video analytics in surgical training. PubMed, Scopus, the Web of Science, and the Cochrane database were searched, to 29 September 2023, following PRISMA extension for scoping reviews (PRISMA-ScR) guidelines. Search terms included 'trainee', 'video analytics', and 'education'. Articles were screened independently by two reviewers to identify studies that applied automated video analytics to trainee-performed operations. Data on the methods of analysis, metrics generated, and application to training were extracted. Of the 6736 articles screened, 13 studies were identified. Computer vision tracking was the common method of video analysis. Metrics were described for processes (for example movement of instruments), outcomes (for example intraoperative phase duration), and critical safety elements (for example critical view of safety in laparoscopic cholecystectomy). Automated metrics were able to differentiate between skill levels (for example consultant versus trainee) and correlated with traditional methods of assessment. There was a lack of longitudinal application to training and only one qualitative study reported the experience of trainees using automated video analytics. The performance metrics generated from automated video analysis are varied and encompass several domains. Validation of analysis techniques and the metrics generated are a priority for future research, after which evidence demonstrating the impact on training can be established.

Task reliability index for operator performance and failure probability assessment in control room simulators

Main control room simulators are an important information source for improving the empirical basis of human reliability analysis (HRA). Both quantitative and qualitative observations provide valuable insight on the operators’ ability to successfully perform their tasks. However, relevant portions of this evidence are not used in typical approaches for estimation of human error probabilities (HEP), which are based on failure counts. With small sample sizes and the operator performance levels representative for nuclear power plants, very little information enters the probability estimates. This paper presents a novel approach to formalize the collection and use of reliability-relevant evidence from simulators. The methodology treats task reliability with the plant-centred orientation characteristic of the systems or Probabilistic Safety Assessment (PSA) perspective as well as a human-centred view of task performance based in human factors engineering. These are combined to form a Task reliability index (TRI) able to distinguish finer performance variations and consider indications of potential failures. Bayesian updating of HEP distributions is suggested by translating the TRI data into pseudo-failures and trials, preserving the convenient coupling of Beta-Binomial distributions in the Bayesian analysis. The approach is demonstrated using data from the International HRA Empirical Study and is shown to produce results that are comparable to those obtained in the empirical study, which were based on comprehensive expert analyses.

Assessment of Operational Performance of Unsignalized Intersection using Microsimulation: A Case Study of Intersection at Pepsicola, Kathmandu

Assessment of Operational Performance of Unsignalized Intersection using Microsimulation: A Case Study of Intersection at Pepsicola, Kathmandu

Assessment of operational performance and service quality in inland waterway transport: A case study of Warri Southwest, Nigeria

Purpose: This study aims to evaluate the operational performance and service quality of inland waterway transport in Warri Southwest, Nigeria, by examining the factors that influence them. Methodology: The study employs a quantitative research approach, using a structured questionnaire to collect data from 180 respondents. The data is analyzed using regression analysis and a structural equation model (SEM) with the help of PLS Software Version 13.0. Results: The findings indicate that cargo clearance, infrastructure quality, and storage facilities significantly influence the performance of agent operation in inland waterway transport (p=0.000). Staff performance also directly impacts the provision of logistical services, which affects customer service. However, jetty operations do not promote operational performance (p=0.075). Additionally, staff performance does not significantly influence the association between jetty operation and performance and the relationship between cargo clearance and infrastructure level (p=0.136, 0.388). Theoretical contribution: This study contributes to the literature on inland waterway transport by providing empirical evidence on the factors influencing operational performance and service quality. The findings can help inform the development of policies and strategies to improve the efficiency and effectiveness of inland waterway transport in Nigeria and other similar contexts. Practical implications: The study's recommendations include improving the quality of infrastructure, cargo clearance processes, and storage facilities to enhance operational performance. Additionally, investing in staff training and development can improve the provision of logistical services and, in turn, customer service. The study also suggests that the government and relevant stakeholders should prioritize the development of inland waterway transport to unlock its potential for socio-economic development, particularly in rural and hard-to-reach areas.

Operational Performance Evaluation of Iron and Steel Industry in China under the Background of Digital Transformation – An Application of Data Envelopment Analysis

This paper aims to study the operating performance of iron and steel enterprises under the background of digital transformation, using the DEA-Malmquist model to carry out static and dynamic analysis of the data from 2018 to 2022, which is divided into four parts. Generally speaking, most of the selected companies have good operational performance, but there is still room for improvement. This paper aims to establish a more comprehensive and objective evaluation system that aligns with the actual development of the steel industry, in order to fulfill the operational performance assessment needs of listed steel enterprises within the context of digitalization.

A spatiotemporal synergetic operating performance assessment based on reconstructed correlation matrix and dissimilarity analytics for plant-wide hot strip mill process

Operating performance assessment (OPA) of process industries plays a significant role in improving the product quality and pursuing the best economic benefits. With the continuous improvement of industrial intelligence, massive data has been collected. Variable time-delay issue is brought by the different spatial and temporal distributions of production units, which affects the actual causal relationship of collected data and assessment accuracy. Therefore, a spatiotemporal synergetic operating performance assessment (SSOPA) method is proposed in this paper. First, a reconstructed correlation matrix is carefully crafted in both time and space, leveraging the intricate multi-correlated spatiotemporal-delay (MCSD) parameters. To precisely quantify the multi-correlated relationships, a mixed gray relation analysis (MGRA) is employed. Subsequently, a predatory search-based genetic algorithm (PSGA) is utilized to meticulously search for the optimal MCSD parameters. Once these parameters are determined, an OPA model is formulated for each subsystem, drawing upon aligned process data and a distributed dissimilarity analysis (DISSIM). Finally, based on the subsystem-level OPA, a comprehensive assessment of the global operating performance level is conducted. The feasibility and effectiveness of the proposed method are verified by the plant-wide hot strip mill process (PHSMP).

Sustainability and operational performance assessment of supermarket air-conditioning architectures using secondary fluids and slurries

Secondary loop architectures and phase change material (PCM) slurries could reduce the environmental impact of refrigeration and air-conditioning systems by lowering significantly the amount of primary refrigerants. However, there is a barrier to their industrial development due to the lack of studies on their behaviour in operational scenarios. The present work analyses the behaviour of different secondary loop systems in an industrial context, here the air conditioning of a supermarket in France. For this purpose, a generic approach, developed in previous work, is proposed to assess their potential of adoption. This modelling approach is able to describe the sustainability and operational performance (energy, environmental, economic, social) of refrigeration systems. After validation of the model on real architecture and components, five standard or new architectures were tested: centralized direct expansion system; secondary loop system with ethylene glycol water; secondary loop system with ice slurries, TBPB hydrate slurries or CO2 hydrate slurries. The main results of this study show that new secondary systems using hydrate slurries have better cooling energy performance than direct expansion or classical brine / ice slurry secondary systems. Moreover, even including pumping power, the life cycle climate performance of secondary loop systems is much better than that of direct expansion systems. However, the total cost of ownership of direct expansion systems is lower than that of secondary systems, but with little differences for smaller pipes. Finally, trade-offs between several performances can be proposed. For example, some architectures with suitable pipe diameters could meet TCO/LCCP-based and CAPEX/pumping-based trade-offs.

Adaptation Entropy Regularization Actor–Critic for Process Operation Performance Assessment

Adaptation Entropy Regularization Actor–Critic for Process Operation Performance Assessment

Performance in Irish Selection and Future Performance in Surgical Training

Selection processes for surgical training should aim to identify candidates who will become competent independent practitioners and should aspire to high standards of reliability and validity. To determine the association between measured candidate factors at the time of an Irish selection and assessment outcomes in surgical training, examined via rate of progression to Higher Specialist Training (HST), attrition rates, and performance as assessed through a multimodal framework of workplace-based and simulation-based assessments. This retrospective observational cohort study included data from all successful applicants to the Royal College of Surgeons in Ireland (RCSI) national Core Surgical Training (CST) program. Participants included all trainees recruited to dedicated postgraduate surgical training from 2016 to 2020. These data were analyzed from July 11, 2016, through July 10, 2022. Selection decisions were based on a composite score that was derived from technical aptitude assessments, undergraduate academic performance, and a 4-station multiple mini-interview. Assessment data, attrition rates, and rates of progression to HST were recorded for each trainee. CST performance was assessed using workplace-based and simulation-based technical and nontechnical skill assessments. Potential associations between selection and assessment measures were explored using Pearson correlation, logistic regression, and multiple linear-regression analyses. Data were available for 303 trainees. Composite scores were positively associated with progression to HST (odds ratio [OR], 1.09; 95% CI, 1.05-1.13). There was a weak positive correlation, ranging from 0.23 to 0.34, between scores and performance across all CST assessments. Multivariable linear regression analysis showed technical aptitude scores at application were associated with future operative performance assessment scores, both in the workplace (β = 0.31; 95% CI, 0.14-0.48) and simulated environments (β = 0.57; 95% CI, 0.33-0.81). There was evidence that the interpersonal skills interview station was associated with future performance in simulated communication skill assessments (β = 0.55; 95% CI, 0.22-0.87). In this study, performance at the time of Irish national selection, measured across technical and nontechnical domains in a multimodal fashion, was associated with future performance in the workplace and in simulated environments. Future studies will be required to explore the consequential validity of selection, including potential unintended effects of selection and ranking on candidate performance.

IMPLEMENTASI ALGORITMA DECISION TREE DALAM OPTIMASI PENILAIAN KINERJA OPERASIONAL

Companies nowadays derive significant benefits from adopting digital systems in their operations. Politeknik LP3I Jakarta, for example, an educational institution, has implemented digitalization to optimize operational performance assessments by auditors. Manual audit assessments consume significant time and effort. Applying intelligent algorithms, such as Decision Tree, has become key to enhancing efficiency and accuracy in decision-making during assessments. The objective of this research is to evaluate the effectiveness of operational performance assessments by identifying and addressing these issues, implementing the Decision Tree algorithm. Decision Tree, which has evolved through a series of algorithms like C5.0, is the appropriate choice as it can make decisions based on specific conditions from the input data. The Decision Tree method and C5.0 Algorithm will help in rapidly and accurately calculating assessments based on criteria. This implementation can minimize manual audit activities, save time, and improve the accuracy of operational performance evaluations. The outcome of this research is higher efficiency in the operational performance assessment process, allowing auditors to focus on strategic aspects and in-depth analysis. Thus, Politeknik LP3I Jakarta and similar institutions can experience positive impacts from technology integration in their operational management, supporting progress and enhancing the quality of educational services.

Assessment of Operational Performance in a Power Generation/Selling Integrated Company Using a Dynamic Proportional Adjustment Coefficient

Assessment of Operational Performance in a Power Generation/Selling Integrated Company Using a Dynamic Proportional Adjustment Coefficient

Operating Performance Assessment of Complex Nonlinear Industrial Process Based on Kernel Locally Linear Embedding PLS

Operating Performance Assessment of Complex Nonlinear Industrial Process Based on Kernel Locally Linear Embedding PLS