Operational Metrics Research Articles

Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability

Using a machine learning approach, this study examines how operational and financial efficiency metrics influence stock prices in the aviation industry. A CatBoost regression model enhanced with SHapley Additive exPlanations (SHAP) was developed using data from 65 global aviation companies collected between 2015 and 2023. The model predicts stock prices based on various operational and financial indicators, including Total Revenue per Available Seat Mile (ASM), Passenger Load Factor, liquidity ratios, and debt-to-assets ratios. The findings suggest that operational efficiency metrics, particularly Total Revenue per ASM and Passenger Load Factor, play a significant role in predicting stock prices within the aviation sector. Financial metrics, such as the Quick Ratio and Debt-to-Assets Ratio, also contribute to the model but appear to have a secondary influence compared to operational factors. SHAP values provided interpretable insights into the model's predictions, allowing for a better understanding of the relative importance of different features. Furthermore, the study's findings offer support for the semi-strong form of the Efficient Market Hypothesis (EMH), demonstrating that operational and financial metrics are reflected in stock prices. These results indicate that aviation companies demonstrating higher operational efficiency may be better positioned for favorable stock market performance, although financial health remains important. This study contributes to the existing literature by integrating operational and financial metrics into a machine learning framework, offering a comprehensive and interpretable model for stock price prediction in the aviation industry.

DATA-DRIVEN OPTIMIZATION IN PHARMACY OPERATIONS: A PRESCRIPTION FOR ENHANCED PATIENT CARE

The convergence of patient care and business efficiency in community pharmacies through a Comprehensive Datafication Approach marks a transformative journey toward enhanced healthcare services and operational effectiveness. The aim of this study was to examine diverse perspectives on datafication, AI, and emerging technologies in healthcare, with a particular focus on optimizing community pharmacy operations. The implementation of a Comprehensive Datafication Approach in community pharmacies significantly impacts overall operational efficiency and patient care outcomes. The strict criterion mandates studies to report outcomes related to patient care, business efficiency, or operational metrics, ensuring selected literature aligns cohesively with research objectives, providing a robust basis for exploring dimensions associated with optimizing community pharmacy operations through datafication. Drawing from multiple studies, our exploration encompasses the challenges and potentials inherent in the integration of digital technologies. As we delve into the optimization of community pharmacy operations through AI, these diverse perspectives contribute to a nuanced understanding of the challenges and opportunities in data-driven healthcare transformation. The integration of AI necessitates ethical considerations, inclusive practices, and a balance between optimization goals and individual rights, ensuring a holistic approach to healthcare datafication. Peer Review History: Received 2 October 2024; Reviewed 17 November; Accepted 24 December; Available online 15 January 2025 Academic Editor: Dr. Emmanuel O. Olorunsola, Department of Pharmaceutics & Pharmaceutical Technology, University of Uyo, Nigeria, olorunsolaeo@yahoo.com Average Peer review marks at initial stage: 6.5/10 Average Peer review marks at publication stage: 7.0/10

Machine Learning-Based Anomaly Prediction for Proactive Monitoring in Data Centers: A Case Study on INFN-CNAF

Anomaly prediction in time series is crucial for ensuring the stability and security of data centers, especially in scientific contexts such as INFN-CNAF, the National Center for Research and Development in Information and Communication Technology of the National Institute for Nuclear Physics. At INFN-CNAF, large volumes of heterogeneous data critical to international experiments are managed using dedicated monitoring systems. To ensure continuous availability, artificial intelligence solutions are being explored to detect anomalies and predict potential failures proactively. This work presents a machine learning-based approach for automatic anomaly prediction in the operational metrics of INFN-CNAF’s WebDav service. We evaluate several methods, including Long Short-Term Memory, Random Forest, and various neural networks, assessing their Accuracy and sensitivity in distinguishing normal from anomalous behaviors. The results demonstrate the effectiveness of these methods, not only in predicting anomalies but also in pinpointing critical areas within monitored metrics. This contributes to more proactive IT resource monitoring and enhances data center management efficiency.

Awareness of Emergency Department Workforce about the Operational Metrics: A Cross-sectional Study.

The awareness of theEmergency Department (ED) workforce about the department's operational guidelines and metrics is essential to optimize the workflow and reduce workload and patient revisits to the ED. Therefore, this study aimed to evaluate the knowledge of the ED workforce regarding operational metrics. We conducted a national cross-sectional study using an online survey in the Kingdom of Saudi Arabia between September and December 2020. ED personnel were studied regarding the ED operational metrics like laboratory/imaging turnaround times, ED length of stay, and patient revisits, linking them to quality care. Data was summarized and analyzed using theJamovi statistical package. One hundred sixty-six participants responded to our online survey. Ministry of Health hospitals represented the largest section (n=90, 54.2%), followed by academic hospitals (n=40, 24.1%). The frequencies of an exit block in the ED were reported as 2-3 days per week (29.5%), most days (24.7%), or daily (18.1%). 41% of the respondents did not know the metrics of lab and imaging turnaround time, while 21.7% did not know about the length of stay. Most respondents agreed that the principles of ED operation should be implemented in the training programs (88.6%) and reported their satisfaction with the productivity of their department in practicing effective operations (70.9%). About one-third of the ED workers were unaware of the key operational metrics of the ED. However, the respondents believe that implementing operational metrics for ED increases the quality of medical care and should be part of the training programs.

An integrated approach to uncertainty and global sensitivity analysis in penstock structural modeling.

Enhanced penstock structural models significantly advance hydropower engineering, yet their increasing complexity introduces challenges. As model interactions intensify, predictability and comprehensibility decrease, complicating the evaluation of model accuracy and alignment with operational performance metrics and safety standards. This issue is particularly pronounced in dynamic modeling, where knowledge gaps hinder straightforward validation via observational data. Traditional techniques for model calibration and validation are becoming impractical, necessitating a strategic approach to prioritize sources of uncertainty related to critical response variability. This study aims to advance our understanding and management of structural variabilities in penstock models by developing a comprehensive, step-by-step Global Sensitivity Analysis (GSA), designed to meet the specific needs of penstock modeling. Illustrated through a free vibration analysis model of a penstock span, this structured methodology begins with Uncertainty Analysis (UA) to identify variabilities, followed by a screening phase using the Morris method to enhance computational efficiency. Subsequent application of multi-method GSA ranks parameter sensitivities, assesses robustness, and provides a comparative evaluation, providing insights into the effective tools for conducting GSA on penstock models. The process culminates with Regional Sensitivity Analysis (RSA), targeting local sensitivities and enhancing understanding of local parameter influences, thereby supporting model adjustments and design optimization. Results from this application characterize model sensitivities for the most prominent mode shapes to specific structural parameters and indicate that these sensitivity outcomes are influenced by variability in parameter spaces and output sub-range definitions. This study provides a practical framework for addressing uncertainties in penstock design, enhancing model accuracy, prioritizing parameters, managing risks, and improving the reliability and efficiency of hydropower infrastructure.

IDEAL–compliant implementation of the Dexter® surgical robot in cholecystectomy: a comprehensive framework and clinical outcomes

Abstract Objectives The integration of advanced technologies is transforming surgical practice, particularly through robotic systems. This study presents the early clinical implementation of the Dexter® surgical robot for cholecystectomy and evaluates clinical outcomes using the IDEAL framework. Methods Twenty patients underwent elective robotic-assisted cholecystectomy using the Dexter® robot. A thorough implementation process, including rigorous surgeon and nurse training and standardized care protocols, was established. Data on operative metrics, complications, and patient outcomes were analyzed, and patient well-being was assessed via a postoperative phone survey. Results Six surgeons and thirty nurses were trained, with surgeons completing a minimum of 20 h of simulation. Preoperative and operative times were significantly reduced through this process. Comparing the first 10 operations to the second, docking time decreased from 11.4 ± 4.1 min to 7.1 ± 2.1 min (p=0.0144) and operative time improved from 130.5 ± 25.7 min to 99.7 ± 21.8 min (p=0.0134). Mean intraoperative blood loss was minimal, averaging 19.5 ± 31.4 mL, and the average length of hospital stay was 3.1 ± 1.4 days. Postoperative pain levels were low, and patient satisfaction was high, as assessed by telephone survey. Conclusions Our findings highlight the value of the IDEAL framework in guiding the systematic evaluation and implementation of new surgical technologies such as the Dexter® robot. A structured approach is essential to improve patient outcomes and safety in the coming digital transformation of surgery.

Establishing Core elements for a Prehospital Emergency Care Systems Evaluation Tool (PECSET) for systems in early stages of development: A Delphi consensus

ABSTRACT OBJECTIVES International Prehospital Emergency Care (PEC) standards have been primarily developed by and for high resource settings. Most PEC systems in Asia, which are still in the early stages of development, struggle to achieve these standards. There is a need for an evaluation tool which can define achievable basic building blocks for PEC systems in low resource settings to improve quality of PEC. We aimed to identify the core, basic elements (building blocks of a PEC system) for a Prehospital Emergency Care Systems Evaluation Tool (PECSET) for low resource settings in Asia. METHODS A 4-stage modified Delphi consensus method was used to engage 32 PEC experts from 12 Asian countries. Participants voted on 32 elements identified from a prior scoping review, focus group discussions, and survey. Each round of voting was conducted through an anonymous, web-based application and followed by face-to-face group discussions. The first two rounds aimed to answer, “Is the element important and feasible in a low resource setting?” The last two stages aimed to answer “Should this element be prioritized as core in the tool?” A thematic analysis of the recorded and transcribed discussions was used to identify participants’ rationale for prioritization. RESULTS After four rounds of voting, 12 elements were identified as core elements: 1) dispatch assisted instructions, 2) protocols for screening, triage and destination, (3) medical direction, 4) standardized training programs, 5) minimum ambulance standards, 6) operational metrics, 7) quality assurance, 8) operational safety protools, 9) essential patient care documentation, 10) medical records management, 11) layperson awareness and education and 12) universal access emergency number. However, the participants decided to include all 32 elements in the tool grouped into broader categories by percent agreement for a tiered approach for early, intermediate, and advanced PEC systems. Rationales for prioritization included a need for focus on basic infrastructure and building resilience in resource-stretched systems. CONCLUSIONS Through a Delphi consensus process, stakeholders identified core elements for PEC systems in low resource settings. These findings will inform the development of a tool for quality assurance and monitoring in low resource settings in South and Southeast Asian countries.

Tampa difficulty score: a scoring system for difficulty of robotic pancreaticoduodenectomy.

Robotic platforms are increasingly utilized in surgery, offering unique technical advantages, though there is a scarcity of difficulty scoring systems (DSS) for these procedures. DSS aids in understanding operative complexities and enhancing preoperative planning. With IRB approval, data were collected on 200 consecutive adult patients who underwent robotic pancreaticoduodenectomy at a high-volume institution from 2019 to 2022. Linear regression was employed on clinical variables to analyze operative time and estimated blood loss as markers of surgical complexity. Weighted scoring system was developed using significant linear coefficient values, and an ANOVA analysis created the difficulty-level grouping system. Significant variables affecting operative time and/or EBL included: history of alcoholism, preoperative endoscopic intervention, tumor size, nodal disease on preoperative imaging, pancreatic duct dilation. These factors created the DSS ranging from 0 to 33. Group 1 (0-8, n = 9), Group 2 (9-20, n = 145), Group 3 (21-26, n = 37), and Group 4 (27-33, n = 9) showed significant differences in age, history of alcoholism, preoperative jaundice, tumor size, nodal disease, and operative metrics. Our novel DSS for robotic pancreaticoduodenectomy effectively predicts intraoperative challenges and aids in preoperative planning. Future steps include validating the system internally and externally.

Production control with Reinforcement Learning for a matrix-structured production system

With increasing product complexities in mass customisation in the automotive industry, the downsides of conventional production concepts like flow production get more pronounced. Their inability to deal with cycle time losses adequately opens up possibilities for new concepts like matrix-structured production (MSP). Due to the immanent dynamics of matrix-structured production, control concept like takt binding or control stands are no longer sufficient to achieve near-optimal performance. The application of Reinforcement Learning (RL) to solve this problem emerged in the recent years. In particular, routing and dispatching tasks have been solved by applying RL. As both tasks influence each other's performance, a combined RL approach is developed. Therefore, a car body construction is simulated to test different modelled Markov processes, algorithms, and rewards. The new approach is validated against common heuristics regarding logistic performance and relevant metrics for operating autonomous guided vehicle fleets. For this, RL systems are designed and compared. The combined approach of production control in terms of dispatching jobs and routing autonomous guided vehicles achieved equivalent performance to heuristics. Still, it excelled in fleet operation metrics, like reduced live or deadlocks.

Real-Time Data Transformation in Connected Vehicles: A Systematic Analysis of Architectures, Methods, and Applications

The emerging landscape of connected vehicles has introduced unprecedented challenges in processing and utilizing vast streams of real-time data. This article presents a comprehensive framework for real-time data transformation in connected vehicle environments, addressing the critical aspects of data processing architectures, analytical methodologies, and practical implementations. The article examines the integration of edge computing, cloud-based solutions, and hybrid architectures to optimize data transformation workflows while minimizing latency and bandwidth constraints. The article analyzes various data types generated by connected vehicles, including telemetry, diagnostics, and user-generated content, and explores their transformation requirements for enabling advanced functionalities such as predictive maintenance, traffic optimization, and enhanced driver assistance systems. Through multiple industry case studies, the article demonstrates the practical application of proposed frameworks and their impact on operational efficiency, safety metrics, and overall vehicle performance. Our findings highlight the significance of balanced architectural choices, the role of machine learning in data transformation processes, and the importance of addressing security and scalability challenges. This article contributes to the growing body of knowledge in connected vehicle technologies while providing practical insights for automotive industry practitioners implementing real-time data transformation solutions.

Estimation of induction motor equivalent circuit parameters and losses from transient measurement.

Due to robustness and low-cost, Induction motors are among the most commonly utilized types of motors in industrial applications. The operation and efficiency of any induction motor can be predicted with reasonable accuracy by solving its equivalent circuit. However, the equivalent circuit parameters may differ from the measured one with aging and when the operating conditions varies. So, it would be advantageous, if the motor parameters can be estimated by a simple and cost-effective method under running condition. Within this research, the circuit model parameters, motor losses, applied load torque and rotor inertia of a 3-phase induction motor at various loads have been estimated applying Particle Swarm Optimization (PSO) technique, from the measured transient current and supply voltage. Using the estimated quantities, various performance indicators were assessed. The predicted operational metrics were evaluated against the corresponding recorded experimental values. The comparison revealed negligible errors, establishing the reliability of the proposed method. In practical applications, the developed algorithm seems promising for predicting.

Economic and Financial Crimes Commission (EFCC) and Financial Accountability and Transparency in Nigeria

This study explores the effect of Economic and Financial Crimes Commission (EFCC) and financial accountability and transparency in Nigeria from 2010 to 2023. Using an ex-post facto research design and secondary data from the International Centre for Investigative Reporting (ICIR) and EFCC Conviction reports, the study investigates the effect of EFCC's operational metrics: number of petitions received, investigated cases, cases filed in court, and convictions secured, on the Corruption Perception Index (CPI) as a proxy for financial accountability and transparency. The analysis employs Ordinary Least Squares (OLS) regression to assess these relationships. The results reveal distinct effects of EFCC activities on corruption perception: the number of petitions received has a negative significant effect on the corruption perception index, whereas the number of investigated cases positively influences perceptions of corruption. However, the study finds that the number of cases filed in court and convictions secured have insignificant effects on corruption perception. The study recommends enhancing EFCC's processes for receiving and handling petitions transparently, prioritizing investigations based on severity and public interest, streamlining court processes to expedite legal proceedings, and strengthening collaboration between EFCC and the judiciary. Additionally, capacity building for prosecutors and legal teams, alongside systemic reforms within the legal system, are crucial for improving the effectiveness of anti-corruption measures in Nigeria.

Cloud Infrastructure Modernization in Enterprise Manufacturing: A Longitudinal Case Study of Digital Transformation at Mattel, Inc.

This article presents a comprehensive case study of cloud infrastructure modernization at Mattel, Inc., examining the implementation of cloud-native solutions and DevOps practices in a global manufacturing enterprise. The article investigates the transformation of legacy systems through Google Cloud Platform (GCP) integration, Infrastructure as Code (IaC) implementation using Terraform, and containerized microservices orchestration via Google Kubernetes Engine (GKE). The article methodology encompasses both technical implementation analysis and quantitative assessment of operational metrics. Results demonstrate significant improvements across multiple dimensions, including substantial reduction in deployment errors, enhanced system performance, and accelerated time-to-market for digital solutions. The implementation of automated CI/CD pipelines through Jenkins and TeamCity yielded marked improvements in deployment efficiency while maintaining robust security protocols for PCI-DSS and GDPR compliance. This article contributes to the growing body of knowledge in enterprise digital transformation by providing empirical evidence of cloud-native architecture's impact on operational efficiency and scalability. The article findings offer valuable insights for organizations pursuing similar modernization initiatives, particularly in regulated industries requiring stringent security compliance.

Economic feasibility and cost modeling for horse training centers: a financial perspective from Brazil

This study aimed to develop a cost calculation model for equine training centers and assess their feasibility. It established critical parameters for such centers, including facilities, equipment, labor, and operational metrics. A Microsoft Excel-based model allocated costs into variable, fixed operating, and opportunity costs. Input prices and quantities were collected to calculate economic indicators like gross margin, net margin, profit, break-even point, total factor productivity, and rate of return. A five-year cash flow projection was developed, estimating financial indicators like net present value (NPV), rate of return, internal rate of return (IRR), and discounted payback. The project obtained an annual profit of $5,298,64, with gross and net margins of $51,630.35 and $23,852.72, respectively. In this scenario, the break-even point, which represents the minimum number of horses needed for costs to equal profits, was calculated to be nine horses. The rate of return for the project was 15%. Risk indicators demonstrated a high level of security (531.49% IRR and payback within the first year), and profitability indicators were positive, with an NPV of $87,005.78, a benefit-cost index (IBC) of 2.75, and a profitability rate of 1,261%. The project was found to be financially viable, and the cost model aligned with economic theory principles. This model offers a valuable tool for equine training center operators, investors, and stakeholders, enabling informed decision-making by providing a detailed economic and financial analysis of costs and profitability. It assists in optimizing resource management, improving pricing strategies, identifying areas of inefficiency to enhance profitability, and supporting long-term planning and sustainability in equine businesses.

IoT-driven load forecasting with machine learning for logistics planning

Forecasting electricity load in logistics is crucial for managing dynamic energy demands. This research introduces the Integrated Load Forecasting System (ILFS), integrating IoT-driven load forecasting with advanced machine learning. As pioneers in logistics-focused electricity load forecasting, we acknowledge challenges posed by operational metrics, external factors, and diverse features. Starting with thorough preparation, including managing missing data and normalization, ILFS incorporates novel approaches such as Hybrid Boruta with XGBoost (BXG) for feature selection and Uniform Manifold Projection and Approximation (UMAP) for lower dimensionality. In the classification phase, we introduce a pioneering approach: the Hybrid Huber Regression with ResNet (HRRN) model, fine-tuned using the Coyote Optimization Algorithm (COA). Demonstrating scalability and interpretability, ILFS adjusts to various electricity load data scenarios, capturing trends in logistics supply warehouses across different days. Validation metrics underscore ILFS’s efficacy, achieving 98% accuracy, 4 MAE, 12 MSE, 5 RMSE, and 0.99 R-squared (R2). With an average execution time of 7.2 s, ILFS outperforms current techniques, and rigorous statistical analyses support this superiority. ILFS emerges as a pivotal solution, meeting the necessities of precise electricity load forecasting in logistics driven by IoT technologies. This research strides towards harmonious integration of load forecasting, IoT, and logistics planning, ushering in advancements in the field.

Implementing laboratory internal audit to improve compliance and quality of care in the municipal public health system-based ambulatory care health clinics in New York city.

System-wide laboratory internal audits are useful to help laboratories prepare for external audits in addition to being part of the ongoing program for compliance improvement and quality assurance in the laboratory to track and enhance care quality. A formal plan was developed and a modified audit checklist was prepared by our laboratory management team, applicable and uniquely tailored for our ambulatory care clinic settings to track operational, quality, and compliance metrics according to the New York State Department of Health Clinical Laboratory Evaluation Program. Two audits were conducted 6 months apart and the conformity documented. An overall 84% increase in compliance and conformity was observed between first and second audits, which ranged from 63% to 100% across different categories, with 100% improvement (0% nonconformity) in 75% of the sites in the second audit. A system-wide laboratory internal audit was created and carried out. Staff shortages, rapid turnover, and lack of retraining were found to be contributing factors to sites that did not achieve 100% conformance. Continuous assessment and monitoring are key elements to success in the laboratory quality management system, and through this scheduled audit process, we were able to achieve continual laboratory quality improvement.

Real-Time Data Processing Systems in Modern Law Enforcement: A Technical Analysis

Real-time data processing systems have revolutionized modern law enforcement operations through the integration of advanced technological solutions. This technical article examines the implementation, performance, and impact of these systems across major metropolitan police departments. The article demonstrates significant improvements in key operational metrics, including a 42% enhancement in crime prevention rates and a 35% reduction in response times during the first year of implementation. Through the article analysis of various components such as ShotSpotter integration, advanced video analytics, and predictive policing modules, the article reveals substantial improvements in law enforcement capabilities. The implementation has enabled processing of over 250,000 hours of surveillance footage daily, real-time tracking of 2,500+ patrol vehicles, and analysis of 1.2 million social media posts, achieving 83% accuracy in crime hotspot prediction. Furthermore, the integration of edge computing has reduced system latency from 2.5 seconds to 50 milliseconds, while maintaining 99.999% system availability. The article also highlights significant improvements in cross-jurisdictional cooperation (91% enhancement) and investigation time reduction (73%), demonstrating the transformative impact of real-time data processing on law enforcement operations.

Innovative 3-Dimensional Imaging in Preoperative Evaluation for Secondary Cytoreductive Surgery in Recurrent Ovarian Cancer—A Pilot Study

ObjectiveThe efficacy of secondary cytoreductive surgery (SCS) in recurrent ovarian cancer remains controversial, necessitating meticulous preoperative planning. While three-dimensional (3D) imaging has transformed surgical approaches in various disciplines, its application in gynaecologic oncology is nascent. This study introduces a novel investigation employing preoperative 3D modelling in SCS preparation. MethodsA retrospective analysis was undertaken at a university-affiliated tertiary medical centre, examining patients who underwent SCS for recurrent ovarian cancer between 2017 and 2022. Patients were stratified into 2 cohorts: those with preoperative CT-based 3D imaging (group A) and those without (group B). Demographic profiles, clinical data, and surgical outcomes were compared between the groups. ResultsAmong the 76 identified patients, 18 were deemed suitable for surgery, with 7 in group A undergoing preoperative 3D modelling. Demographics encompassing age and performance status were consistent across both groups, while Serous histology was more prominent in group B. Although operative metrics and collaborative endeavours exhibited no statistically significant variance, the attainment of optimal debulking with no residual disease (R0) was substantially higher in group A (100%) compared to group B (54%), with a significance level of P = 0.05. ConclusionCT-based 3D modelling in the preoperative phase of SCS for ovarian cancer shows potential for enhancing surgical planning. While this pioneering research highlights the potential benefits of integrating 3D imaging into gynaecologic oncology, the limitations of this retrospective study imply that these findings are primarily hypothesis-generating. Further prospective studies are necessary to validate the impact.

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.

Case Article—Humanitarian Supply Chains and Inventory Management: Planning Medical Supply at the International Committee of the Red Cross

This case is based on an actual project with the International Committee of the Red Cross. The rich context allows instructors to cover important topics in inventory management, supply chain management, and humanitarian logistics at a level appropriate for different student backgrounds. At a foundational level, the case presents opportunities to practice concepts like cycle stock, safety stock, and service level. Technical audiences can appreciate applications of machine learning and optimization for inventory management. Practice-oriented audiences learn about the challenges of applying commercial operations metrics and heuristics like ABC analysis to humanitarian logistics. Realistic data and models are provided. Funding: B. Thakur-Weigold acknowledges funding from the Engineering for Humanitarian Action initiative launched by the ICRC, ETH Zurich, and EPFL. International Committee of the Red Cross (ICRC), Eidgenössische Technische Hochschule (ETH) Zürich, and École Polytechnique Fédérale de Lausanne (EPFL). Supplemental Material: Supplemental materials are available at https://www.informs.org/Publications/Subcribe/Access-Restricted-Materials .