Performance Indicators Research Articles

Developing innovative financial products for sustainable economic growth

In today’s competitive landscape, technology firms increasingly turn to advanced data analytics as a strategic tool for boosting revenue growth and operational efficiency. This review explores how leveraging sophisticated analytical techniques can enable these organizations to extract actionable insights from vast datasets, thereby enhancing decision-making processes and driving business performance. Advanced data analytics encompasses a range of methodologies, including predictive analytics, machine learning, and big data analysis, which collectively empower firms to identify trends, optimize resources, and tailor products to meet evolving customer demands. One of the primary benefits of utilizing advanced data analytics is the ability to enhance revenue growth through improved customer targeting and segmentation. By analyzing customer behaviors and preferences, technology firms can develop personalized marketing strategies that resonate with their target audiences, resulting in increased conversion rates and customer loyalty. Moreover, data analytics facilitates the identification of new market opportunities, enabling firms to innovate and expand their product offerings effectively. Operational efficiency is another critical area where advanced data analytics proves invaluable. By monitoring key performance indicators (KPIs) and operational metrics, technology firms can streamline their processes, reduce costs, and minimize waste. Predictive analytics, in particular, allows organizations to anticipate potential disruptions in their operations, enabling proactive measures to mitigate risks and maintain smooth workflows. This capability not only enhances productivity but also fosters a culture of continuous improvement. Additionally, advanced data analytics supports informed strategic planning by providing insights into market dynamics and competitive positioning. Firms can leverage these insights to make data-driven decisions, allocate resources effectively, and align their business strategies with market trends. In conclusion, advanced data analytics serves as a catalyst for revenue growth and operational efficiency in technology firms. By embracing these analytical techniques, organizations can harness the power of data to drive innovation, improve customer engagement, and achieve sustainable competitive advantage. As technology continues to evolve, the strategic implementation of advanced data analytics will be essential for firms seeking to thrive in an increasingly data-driven world. Keywords: Advanced Data Analytics, Revenue Growth, Operational Efficiency, Technology Firms, Predictive Analytics, Customer Targeting, Strategic Planning.

Corporate Sustainability Reporting and Financial Performance: An Empirical Analysis of Indian Listed Companies

This empirical analysis examines the relationship between corporate sustainability reporting and financial performance among Indian listed companies. In recent years, sustainability has gained prominence in corporate strategy due to increasing regulatory requirements, investor interest, and stakeholder expectations. The study explores how sustainability reporting, as disclosed through corporate sustainability reports (CSR), affects the financial performance of companies. Using a sample of Indian companies listed on major stock exchanges, the study analyzes data on sustainability disclosures, financial performance indicators (such as return on assets, return on equity, and market valuation), and various control variables. The analysis employs statistical methods, including regression models, to identify the correlation and potential causal linkages between sustainability reporting and financial performance. The findings suggest a positive relationship between comprehensive sustainability reporting and financial performance, indicating that companies with better sustainability practices tend to experience higher financial returns and market valuation. The results also highlight the role of regulatory frameworks, industry-specific factors, and company size in shaping the strength of this relationship. This study contributes to the growing body of literature on corporate sustainability by providing insights specific to the Indian context, where sustainability reporting practices are evolving. It suggests that companies engaging in transparent and detailed sustainability reporting may achieve not only environmental and social benefits but also financial gains, making sustainability a strategic priority for long-term value creation.

Sustainable Synchronization of Truck Arrival and Yard Crane Scheduling in Container Terminals: An Agent-Based Simulation of Centralized and Decentralized Approaches Considering Carbon Emissions

Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.

Design of a Novel Macro-Micro Integrated Brain Surgery Robot Based on Modular Parallel Mechanisms

The advancement and development of medical surgical robots have provided new technological support for brain surgery and neurosurgical procedures, improving the reliability of highly complex and precise surgeries. In turn, this urges the design and development of novel surgical robots to possess higher precision, stability, and enhanced motion capabilities. In response to this practical demand, this paper introduces a macro-micro integrated medical brain surgery robot system based on the concept of modular PMs (parallel mechanisms), which have a total of 13 active DOFs (degrees of freedom). This system divides the motion process of brain surgery into a large-scale macro-motion space and a small-scale high-precision motion space for design and planning control. The introduction of the design concept that combines multiple modular parallel sub-mechanisms has brought a significant level of decoupling characteristics to the mechanism itself. A comprehensive introduction and analysis of the surgical robot are provided, covering aspects such as design, kinematics, motion planning, and performance indicators. To address the pose allocation and coordination of motion between the macro platform and the micro platform, a pose allocation algorithm based on the decoupling and non-decoupling characteristics in various dimensions of the macro-micro platform is proposed. The designed measurement experiments have demonstrated that the repeatability in positioning accuracy of the macro and micro platform reaches the level of micron and submicron respectively. Practical experiments of motion control and simulated brain electrode implantation validate the excellent performance and stability of the entire surgical robot system. This research contributes innovative insights to the development of medical surgical robot systems, particularly in the domain of mechanism design.

An evaluation of the Hawthorne effect in a clinical trial of trauma care in Ghana

AbstractIntroductionHawthorne effect refers to changes in clinician behavior in response to being observed. It has been minimally evaluated in trauma and surgical research, especially in low‐ and middle‐income countries (LMICs).MethodsWe used data from the first (control) phase of a randomized clinical trial in Ghana (2020‐21). Trained observers were stationed in emergency units in eight non‐tertiary hospitals to observe the achievement of key performance indicators (KPI) (KPIs) of trauma care. We hypothesized that the observers' presence would create a Hawthorne effect, resulting in clinicians (doctors, nurses) performing above their usual level, with performance highest immediately after the observers were introduced, and with performance then declining over time toward the usual baseline.ResultsWe enrolled 2067 injured patients. Achievement of 13 out of 16 KPIs declined significantly over the 14 months after the introduction of observers. For example, airway assessment declined from 77% initially to 60% at the end of the phase (p < 0.001). Chest examination declined from 66% to 31% (p < 0.001). Distal pulses checked declined from 45% to 7% (p < 0.001). The slope of decline in KPI achievement ranged from 0.8%/month (mobility assessed) to 15.9%/month (distal pulses checked).ConclusionsThe decline in KPI achievement over time is consistent with the hypothesis that clinicians performed above their usual level just after the observers were introduced, with performance then declining toward the usual baseline. This study is among the first to provide quantitative data on the Hawthorne effect for trauma research in LMICs. These data are useful for the design of future observational studies and clinical trials.

Evaluation of the Intensive Acute Flaccid Paralysis Surveillance System in Ghana: Post the Switch from tOPV to bOPV

The Global Polio Eradication Initiative was adopted by Ghana in 1996, and through robust AFP surveillance was able to interrupt the circulation of wild poliovirus in 2008. However, the country suffered vaccine-derived poliovirus type 2 outbreaks in 2019 and 2022. We conducted a retrospective analysis of all AFP surveillance data received by the polio program in Ghana from 2018 to 2022. An analysis of the WHO performance indicators for evaluating a surveillance system was conducted using Epi Info 3.5.4 and Microsoft Excel. Of the 4832 cases investigated, 56.3% were males, 71.1% comprised children aged 5 years and below, and more than half (65.2%) had received a maximum of three doses of OPV. Over 77% (3028) had a fever at the onset of paralysis, and 67.8% had paralysis progression within 3 days. The non-polio AFP rate of ≥2 and the stool adequacy rate exceeded the target of ≥80% in nearly every study year. The proportion of non-polio enteroviruses isolated surpassed the target of ≥10% in all years except 2018. The AFP surveillance system in Ghana is sensitive and representative. Though the surveillance became more intensive and proactive during the outbreak, the system needs to focus on improving the completeness of the data as well as the timeliness of the arrival of stool specimens within 3 days of collection.

The barriers and enablers to completing the RPS core advanced pharmacist credentialing programme via the CPPE/RPS pilot programme

Abstract Introduction The RPS and CPPE launched a pilot programme in March 2023 to support 300 pharmacists to evidence their advanced practice via an e-portfolio aligned to the RPS Core Advanced curriculum1 in a 12-week period. Successful pharmacists are credentialed as Advanced Pharmacists. The pilot involved CPPE education supervisors (ESs) support, an online learning platform and small group learning and support. Aim To ascertain barriers and enablers to completing an advanced e-portfolio and to identify how to improve the on-going programme. Methods The invitation to complete the anonymous survey in May 2024 was administered via www.onlinesurveys.ac.uk and developed linking to key performance indicators around education supervision support, to understand why candidates were deferring cohorts and to ascertain additional support accessed by candidates. Ethical approval was not required as study is service evaluation. Candidates who responded included those who successfully submitted their e-portfolio ‘submitted candidates’ n=10, candidates who are resubmitting their portfolio after not passing ‘resubmission candidates’ n=8, pharmacists currently completing their portfolio ‘current candidates’ n=44 and those who started but left without submitting ‘attrition candidates’ n=13. Results All submitted candidates scored ES support as very good/excellent; 60% agreed strongly they would not have submitted their portfolio without this support. Each cohort had two small-group tutorials where candidates accessed peer support and joined WhatsApp groups. Only 38% of resubmission candidates, compared to 80% of portfolio submitted candidates attended both group tutorials. Resubmission candidates were less likely to access additional peer support (38% for resubmission candidates vs 60% for submitted candidates) or to join the cohort WhatsApp group (75% for resubmission candidates vs 90% for submitted candidates). Resubmission candidates rated the remedial SMART action planning support highly. Submitted candidates would have found it easier to complete their portfolio with more comprehensive (60%) and timely responses (70%) from their collaborators. This compares to 55% and 59% of current candidates recognising the same issues. Submitted candidates wanted more allocated time during working hours to support completion of the portfolio (60% rising to 80% for current candidates). There is a notable increase for current candidates wanting a longer period to complete their portfolio (75% compared to 40% of submitted candidates). Half of submitted candidates (50%) reported spending more than 5 hours (average) each week on their portfolio compared to 18% of current candidates. Top reasons for attrition include too busy with work, family responsibilities, personal health challenges and no support from the workplace. Candidates across all groups suggested programme improvement ideas including more evidence examples, making the RPS platform less difficult to navigate and use, and more education supervisor support. Motivating factors for completion include recognition of advanced level working and preparation for the consultant portfolio. Conclusion Enablers include education supervision, CPPE resources/facilitated support events, peer support, WhatsApp groups, recognition and career progression. Key barriers include needing more time and lack of dedicated time at work to complete e-portfolio, lack of timely and comprehensive support from collaborators and finding the RPS e-portfolio platform difficult to navigate. Limitations – low numbers and response rate. Data collection is on-going. Reference 1. Royal Pharmaceutical Society, Core Advanced Pharmacist Curriculum, 2022 https://www.rpharms.com/Portals/0/Credentialing/RPS%20-%20Core%20Advanced%20curriculumFINAL.pdf?ver=8gc3s2aE9t1nZN37CSU7Lw%3d%3d (Accessed 28 May 2024)

Development and application of ex-ante performance evaluation framework for health projects in China

China National Health Commission issued an implementation plan for budget performance management in health projects in April 2021, which emphasizes all health projects must be evaluated in advance prior to implementation. The study aims to develop an ex-ante performance evaluation framework for health projects and formulate a self-assessment report template. The study also attempts to gain insights into the ex-ante performance of health projects in China, identify issues and explore the factors that influence the ex-ante performance of health projects and make suggestions about improvement strategies. The framework was established by using a framework development methodology. To develop the framework, the findings from existing research were pooled and organized to create indicators and key points. The preliminary ex-ante performance evaluation framework was refined, validated and tested by the experts. Weight analysis was conducted by using the Analytic Hierarchy Process. Then the indicator system has been transformed into a project ex-ante performance self-assessment report template. Samples of 698 municipal health projects in China from 2021 to 2022 were analyzed. We invited experts to complete the scoring task by using our ex-ante performance evaluation framework. The ex-ante performance evaluation framework primarily examines five aspects, the necessity of the project, economy of input, rationality of performance indicators, feasibility of implementation plans, and compliance of financing. The overall ex-ante performance evaluation score for the sample projects was (91.19 ± 8.02). The sub-scores for necessity of project were (14.74 ± 1.85), economy of input was (34.45 ± 2.43), reasonableness of performance indicators was (8.27 ± 2.28), feasibility of implementation plan was (23.99 ± 3.84), and compliance of financing was (9.75 ± 1.47). Factors such as whether it was an epidemic prevention and control project, the project budget, project content, project objective, and project implementation organization were all influencing the ex-ante performance of health projects (P < 0.05). We have developed a ex-ante performance evaluation framework for health projects and established a project ex-ante performance self-assessment report template. They can offer a sufficient reference for project process management. Project implementation organization should elevate the awareness and proficiency of project managers regarding budget performance management. Government should improve the ability of ex-ante performance evaluation, and eliminate ineffective projects through ex-ante performance evaluation.

Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data

BackgroundMost epithelial ovarian cancer (EOC) eventually develops recurrence. Identification of high-risk patients can prompt earlier intervention and improve long-term outcomes. We used laboratory and clinical data to create models based on machine learning for EOC platinum resistance recurrence identification.MethodsThis study was designed as a retrospective cohort analysis. Initially, we identified 1,392 patients diagnosed with epithelial ovarian cancer who underwent platinum-based chemotherapy at Yunnan Cancer Hospital between January 1, 2012, and June 30, 2022. We collected data on the patients’ clinicopathologic characteristics, routine laboratory results, surgical information, details of chemotherapy regimens, and survival outcomes. Subsequently, to identify relevant variables influencing the recurrence of platinum resistance, we screened thirty potential factors using two distinct variable selection methods: Lasso regression and multiple logistic regression analysis. Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. These included decision tree analysis (DTA), K-Nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost). The performance of these models was compared against that of traditional logistic regression. To ensure robust internal validation and facilitate comparison among model performance metrics, a five-fold cross-validation method was implemented. Key performance indicators for the models included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and average accuracy. Finally, we will visualize these models through nomograms, decision tree diagrams, variable importance plots, etc., to assist clinicians in their practice.ResultsMultiple logistic regression analysis identified eight variables associated with platinum resistance recurrence. In the lasso regression, seven variables were selected. Based on the findings from both Lasso regression and multiple logistic regression analysis, models were developed using these 7 and 8 factors. Among these, the XGBoost model derived from multiple logistic regression exhibited superior performance and demonstrated good discrimination during internal validation, achieving an AUC of 0.784, a sensitivity of 0.735, a specificity of 0.713, an average accuracy of 80.4%, with a cut-off value set at 0.240. Conversely, the LR model based on lasso regression yielded commendable results as well; it achieved an AUC of 0.738, a sensitivity of 0.541, a specificity of 0.836, with a cut-off value established at 0.154 and an accuracy rate of 79.6%. Finally, we visualized both models through nomograms to illustrate the significance of each variable involved in their development.ConclusionsWe have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. Among these models, the XGBoost model—derived from variables selected through multiple logistic regression—demonstrated the best performance. It exhibited high AUC values and average accuracy during internal validation, making it a recommended tool for clinical use. However, due to variations in time and context, influencing factors may change over time; thus, continuous evolution of the model is necessary. We propose a framework for this ongoing model adaptation.

Leveraging Tropical Algebra to Assess Trustworthy AI

Given the complexity of the application domain, the qualitative and quantifiable nature of the concepts involved, the wide heterogeneity and granularity of trustworthy attributes, and in some cases the non-comparability of the latter, assessing the trustworthiness of AI-based systems is a challenging process. In order to overcome these challenges, the Confiance.ai program proposes an innovative solution based on a Multi-Criteria Decision Aiding (MCDA) methodology. This approach involves several stages: framing trustworthiness as a set of well-defined attributes, exploring attributes to determine related Key Performance Indicators (KPI) or metrics, selecting evaluation protocols, and defining a method to aggregate multiple criteria to estimate an overall assessment of trust. This approach is illustrated by applying the RUM methodology (Robustness, Uncertainty, Monitoring) to ML context, while the focus on aggregation methods are based on Tropical Algebra.

ANALYSIS OF THE POTENTIAL FOR WATERMELON PRODUCTION AND MARKETING IN THE STATE OF GOIÁS FROM 2019 TO 2023

The state of Goiás stands out in agribusiness, especially in fruit production, due to its growing domestic and foreign markets. Among fruits, watermelon (Citrullus lanatus) stands out, being relevant both economically and socially, generating jobs in several sectors. However, this emerging market lacks policies and techniques as developed as those present in sectors such as soybeans and corn, which highlights the underutilization of the potential of watermelon production in the state. This study aims to evaluate innovations, whether public policies or private initiatives, that can improve the production and marketing of watermelon in Goiás. The research seeks to analyze performance indicators and identify actions that promote efficiency and gains for the local community and producers, considering that watermelon is widely consumed in several sectors, standing out for its nutritional and cultural value. The study methodology follows two stages: a review of the literature on fruit production chains and the agricultural market in Goiás, followed by an analysis of data on competitiveness, consumption trends and opportunities in the watermelon production chain. Based on these results, strategies will be proposed to optimize production, increasing the competitiveness of producers and promoting the sustainable development of the sector. The study will also highlight the main weaknesses and strengths of marketing and production, using variables such as production size, demand, and available technologies.

Enhancement of AVR system performance by using hybrid harmony search and dwarf mongoose optimization algorithms

Innovations in control algorithms, integration of smart grid technologies, and advancements in materials and manufacturing techniques all push the boundaries of AVR performance. As the demand for power systems progresses with the complexity and variety of loads, conventional AVR designs may struggle to handle these ever-changing circumstances efficiently. Therefore, the need for new optimization methods is crucial to bolstering the efficiency, reliability, and adaptability of AVRs. Thus, this work aims to improve the performance of the AVR system controller by using a novel hybrid technique between the Harmony Search (HS) and Dwarf Mongoose Optimization (DMO) algorithms to tune the proportional-integral-derivative (PID) and proportional-integral-derivative acceleration (PIDA) parameters. The suggested hybrid approach ensures an accurate solution with balanced exploration and exploitation rates. The reliability of the proposed HS-DMOA is verified through comparison with different optimization techniques carried out on time and frequency performance indicators, disturbances in the form of changes to time constants, and dynamic input signals. The proposed hybrid HS-DMOA PID-based has better overshoot than PID-based HS, LUS, TLBO, SMA, RSA, and L-RSAM by 20.37%, 18.5%, 18.5%, 2.77%, 5.55%, and 2.77%, respectively. Regarding the phase margin, the proposed hybrid HS-DMOA PID-based is better than PID-based HS, LUS, and TLBO by 39%, 37%, and 38%, respectively. While the proposed hybrid HS-DMOA PIDA-based has a better overshoot than PIDA-based HS, LUS, and PID HS-DMOA-based by 14%, 17%, and 20%, respectively. Moreover, the robustness under dynamic disturbance proved the reliability of the proposed HS-DMOA PID and PIDA based through enhancement of overshoot around 0.3%~20% for different cases. Finally, the main contribution of the paper is to propose a relatively new hybrid optimization method to enhance the AVR PID and PIDA-based performance with detailed analysis in time and frequency domains under normal and dynamic disturbances.

MANAGING THE “S” IN ESG: HOW ASSET MANAGERS DISCLOSE AND CONSIDER HUMAN RIGHTS

Objective: The purpose of the research was to evaluate the commitment and due diligence measures directed at human rights in a sample of the largest international and European asset management companies, under OECD guidelines. Theoretical Framework: According to the OECD, companies can impact most internationally recognised human rights. However, there is a lack of studies that directly assess the human rights due diligence in investment companies. Method: A documentary and qualitative analysis of 45 investment companies was applied. Five key performance indicators were created to measure human rights commitment and due diligence practices such as disclosure of human rights policies, cascading of compliance/obligations to investees, due diligence before and during the investment and collaboration with industry and non-industry initiatives. All companies were classified and rated according to these criteria. Results and Discussion: This research reveals that there is a dichotomy between recommendations of human rights integration in investment and actual commitment and compliance, indicating that only a minority of companies were compliant and committed to considering human rights in their investment. Research Implications: The results provide an initial benchmark of the state of the commitment towards human rights in the investment sector, allowing for further investigations to be conducted to assess the development of this dimension of ESG and CSR, particularly in this sector Originality/Value: The analysis consisted of accessing and evaluating the public information of the larger investment firms from an individual investor perspective, which provides an innovative view of human rights awareness and compliance in the investment sector.

Effects of grazing on the relationship between dominant shrubs and understory vegetation along sand dune stability gradient

During the process of dune vegetation restoration, understanding how grazing disturbance affects the relationship between plant species is a critical issue in ecological studies. However, there is insufficient evidence on the changes in the interaction between dominant shrubs and understory vegetation under grazing behavior. We aimed to study how grazing and dune stabilization affects the relationship between Caragana microphylla and understory vegetation. We established fencing at various stages of dune stabilization and proceeded to compared the performance indicators (e.g., richness and biomass) and the relative interaction index of understory vegetation and different functional groups along the dune stability under grazing and fencing conditions. Results showed that C. microphylla had facilitation on understory plants, and increased with dune stability, while the facilitation of Caragana microphylla on understory vegetation was stronger under grazing conditions. As sand dune stabilization increases, the facilitation of C. microphylla on understory vegetation diversity decreases significantly. However, there was no significant difference in the facilitation of C. microphylla on understory vegetation biomass at different stages of sand dune stabilization. This is related to the survival strategy of perennials being less tolerant to environmental stress than annuals, because grazing increased the richness of both annuals and perennials while reducing the overall biomass, and in the later stages of sand dune stabilization, and the facilitation of C. microphylla on perennials was higher than on annuals. Our study highlights the importance of the responses of different life-form groups to environmental factors and grazing disturbance during the process of sand dune vegetation restoration, as they play a crucial role in shaping the development of the relationship between understory vegetation and dominant shrubs.

The impact of irrigation duration on crop N uptake, lint yield and internal NUE in cotton using standard urea

Context This is the first research to investigate the impact of irrigation duration on crop productivity and nitrogen (N) performance indicators in southern New South Wales (SNSW), Australia, as the industry strives for improved N productivity. Aim To benchmark the impact of different irrigation durations on waterlogging and related impacts on available soil and fertiliser N, cotton productivity and nitrogen use efficiency (NUE). Methods Two field experiments were conducted in SNSW where cotton was grown with varying rates of fertiliser N application interacting with different durations of furrow irrigation applications during the period from first flower to crop maturity. Key results Waterlogged conditions occurred within the top 20 cm of the soil profile during irrigations. These conditions coincided with fertiliser placement and high concentrations of available mineral N, which created conditions conducive for N loss. However, the internal NUE (kg lint kg crop N uptake−1) and N balance were not impacted by irrigation duration in either experiment, despite differences in duration of waterlogged conditions that averaged 15 h irrigation−1. Partial N budgets suggested that 48% of the available N to the crop could not be found in the plant or soil mineral N pools after harvest. Conclusion Modification of the duration of irrigation applications was not an effective management option to improve NUE in irrigated cotton; however, it improved water productivity (bales per megalitre). Implications Further research is required to consider the implications of other irrigation systems such as overhead sprinkler and drip systems for improvement of NUE.

Research of Options for Constructing Information Management Systems Based on Network Models of Queuing Systems

The use of information management systems (IMSs) for the management of technical facilities is currently one of the directions for further improvement and increase in the effectiveness of the use of technical facilities in solving their target tasks. The existing modern IMSs are a set of hardware and software tools designed for collecting, processing and storing information and management. In the presence of a large amount of information and contradictory factors affecting the quality of management, making informed and timely decisions in the management process is impossible without the use of IMSs. The IMSs currently being developed are, for the most part, specialized systems and are designed to solve specific tasks. In this regard, the development and design of IMSs should be carried out taking into account the relationship with the target indicators and features of the management facilities, the results of a comprehensive analysis of information about the IMS elements in the process of functioning, and structural and algorithmic parameters that affect performance indicators. The use of mathematical models for the study of options for the construction of an IMS is the basis for the design and development of devices and subsystems of an IMS. The IMS models currently being developed make it possible to conduct research for single-stage management processes with the presence of similar service facilities in the system. At the same time, modern technical facilities and control systems are complex complexes with cyclically repeating control processes of various types of means. As a rule, such complexes have a set of parallel operating devices (control channels) that provide control of different types of objects at various stages of information processing. In this case, the structure of the IMS must be represented as a multiphase multichannel technical system in which the process of simultaneous management of several objects of various types takes place. In this regard, the purpose of the article is to develop a mathematical model of an IMS with two phases of management and the presence of an arbitrary number of serviced different types of management facilities. The basis of the model is a multiphase CFR network model with a limited waiting time for an application in the service queue. The study on the model allows choosing an option for building an IMS, in particular, choosing the optimal number of control channels for various types of objects according to the criterion of optimality and restrictions on the cost and time of management. An algorithm for selecting an option for building an IMS has been developed, and an example of calculating the number of control channels for managing three types of objects is given.

Understanding solar minigrid sustainability and impact through a holistic Key Performance Indicator framework

Understanding solar minigrid sustainability and impact through a holistic Key Performance Indicator framework

Measuring Supply Chain Performance Using the SCOR Model: A Case Study of CV. XYZ

This research discusses the design of a performance measurement system for supply chain management using the Supply Chain Operations Reference (SCOR) model developed by the Supply Chain Council. The SCOR model focuses on five main activities within a company's supply chain, namely plan, source, make, deliver, and return. All activities are linked with performance attributes such as reliability, responsiveness, flexibility, cost, and assets. The implementation of this model is applied to CV. XYZ, a company that operates in the production of plain and printed T-shirts, which is located in East Java. The research was conducted from January 2024 to September 2024. The results of the study identified that there are 35 performance indicators representing supply chain activities, with the following breakdown: 9 indicators for the plan activity, 7 indicators for the source activity, 6 indicators for the make activity, 7 indicators for the deliver activity, and 6 indicators for the return activity. The Analytical Hierarchy Process (AHP) method was used to weight each activity in the SCOR model. Based on the results, the highest priority was the source activity (0.252), followed by deliver (0.222), plan (0.221), make (0.209), and return (0.095). From the scoring system, it was found that 23 indicators have a green score, 8 indicators have a yellow score, and 4 indicators have a red score. The limitation of this research is that one SCOR activity, "enable," was not analyzed. Therefore, it is recommended that future research analyze all SCOR activities.

Optimizing Agility in the Pharmaceutical Supply Chain Using Digital Twins to Cope with the Ripple Effect

The pharmaceutical supply chain is a multilayered and complex structure designed to deliver medicines to customers in a timely manner while ensuring optimal quality and quantity of medicines. The COVID-19 outbreak exposed the vulnerability and uncertainty of the pharmaceutical supply chain, so managing the risks in the pharmaceutical supply chain has become particularly important. This study demonstrates that digital twin (DT) technology can improve pharmaceutical supply chain agility and reduce the ripple effect caused by disruptions. The ripple effect refers to the effect that a sudden disruption at one node in the supply chain has on causing a chain reaction in the rest of the supply chain. The most used risk management method today is the Enterprise Resource Planning (ERP) system, which allows for real-time data sharing but has limitations in predicting and modeling the entire supply chain and lacks the ability to make quick decisions and simulate the entire supply chain. DT technology creates a virtual model of the supply chain, which enables continuous communication and information exchange between real assets and the virtual model, offering a promising alternative to risk management for pharmaceutical supply chains. risk management by providing a promising alternative. This study utilizes the AnyLogistix platform to construct a DT model and demonstrates the effectiveness of DT technology in reducing ripple effects and improving supply chain agility using quantitative analysis. This paper focuses on analyzing the pharmaceutical supply chain for 75mg aspirin tablets in London. A supply chain that operates normally, a supply chain that introduces a disruptive event (closure of the logistics center due to an earthquake), and a supply chain that applies a proactive strategy (activation of an alternate logistics center) to mitigate risk are simulated separately. The positive impact of DT technology on the supply chain is evaluated by analyzing the methods of key performance indicators (KPIs) such as inventory level, order fulfillment rate, and delivery time. The experimental results show that DT technology enhances the responsiveness, flexibility, speed, and proactivity of the supply chain, which significantly improves the agility of the supply chain. Proactive and effective strategies also reduce the financial impact of disruptions and service levels are quickly recovered. In addition, the proactive strategy stabilized inventory levels and reduced delayed orders. The above key metrics confirm the hypothesis that DT technology can improve supply chain agility and effectively reduce the ripple effect. Despite the significant conclusions drawn in this paper, there are some limitations. Firstly, sensitivity analysis and t-test were not used in the study for secondary testing, which resulted in the lack of reliability of the validation results. In addition, the possible interaction between the ripple effect and the bullwhip effect was not discussed, leading to a confounding factor in the experimental results. The above issues can be considered in further research in the future. Finally, this paper explores the possibilities and uses of introducing machine learning techniques and considers the supply chain risk factors of globalization. In summary, this study validates the potential of digital twin technology in improving agility and coping with ripple effects in pharmaceutical supply chains. As research continues, DT technology is expected to improve supply chain efficiency and reduce supply chain risk while ensuring patient safety.

Multiobjective Optimisation of Flotation Variables Using Controlled-NSGA-II and Paretosearch

Finding the optimum operating points for the maximisation of flotation recovery and concentrate grade can be a very difficult task, owing to the inverse relationship that exists between these two key performance indicators. For this reason, techniques that can accurately find the trade-off are critical for flotation process optimisation. This work extracted well-assessed Gaussian process predictive functions as objective functions for a comparative multiobjective optimisation study using the paretosearch algorithm (PA) and the controlled elitist non-dominated sorting genetic algorithm (controlled-NSGA-II). The main aim was the concomitant maximisation of the copper recovery and the concentrate grade. Comparison of the two applied techniques revealed that the PA discovered the best set of the pareto-optimal solution for both the recovery (93.4%) and concentrate-grade (17.4 wt.%) maximisation.