Cost Reduction Research Articles

Techno-economic feasibility analysis and optimisation of on/off-grid wind-biogas-CHP hybrid energy system for the electrification of university campus: A case study

This paper provides a comprehensive feasibility analysis of a hybrid energy system with different configurations to meet electricity and thermal load demand at the University of Southampton campus. The suggested hybrid energy system (HES) comprises wind turbine, biogas generator, battery, CHP natural gas-generator, thermal load controller, boiler, and converter and is simulated in Homer Pro software. In addition, a comparative analysis between stand-alone and on-grid HES is presented. The results indicate that the grid-connected HES is significantly more cost-effective, with a 45% reduction in cost of energy (COE) and %15.5 decrease in net present cost (NPC) compared to the off-grid system, which amounts to 0.03359 $/kWh and 467M$, respectively. The grid-connected HES is not only more eco-friendly in terms of greenhouse gas emissions (GHG) and produces 19% less emissions annually compared to the grid-independent system, but it also effectively achieves a positive return on investment (ROI) of %45 with 2.3 years of payback time. Considering the university’s total emissions of 38.56 kT CO2e, the proposed on-grid hybrid system has the potential to lessen GHG emissions by 91.2%.

The impact of a lifestyle behaviour change program on healthcare costs: Quasi-experimental real-world evidence from an open-access mobile health app in the Netherlands

ObjectiveRecognizing the substantial role of modifiable risk factors such as physical inactivity, unhealthy diets, and substance use in driving global healthcare expenses, this study aims to assess the impact of a lifestyle behaviour change mobile health (mHealth) app on healthcare costs within a real-world setting. MethodsEmploying a difference-in-difference approach, this research compared healthcare costs between users of an mHealth app (treatment group) and a propensity score-matched control group of non-users. The study included data from 15,506 participants in the Netherlands, covering healthcare cost records collected from 2015 to 2019. ResultsThe implementation of the mHealth app was linked to a steady reduction in healthcare costs, achieving a 4.9 % decrease (β=−0.05,95%CI:−0.10to0.00) during the first year following the program's implementation, and a further 5.3 % decrease (β=−0.05,95%CI:−0.10to−0.00) in the second year. This healthcare costs reduction is associated with lower expenses for general practitioner services as well as diminished specialist visits. Additionally, increased app use led to further cost reductions, with frequency of use proving more important than intensity. ConclusionsThis study demonstrates that a behaviour change mHealth app is linked to significant reductions in healthcare costs, with more frequent and intense use leading to greater savings. Given their relatively low cost and ability to reach diverse population groups, such apps not only enhance individual health outcomes but can also serve as effective policy tools for large-scale health promotion, supporting broader societal health improvements.

Fusion algorithms on identifying vacant parking spots using vision-based approach

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.</p></div></div></div>

Dynamic characteristics of pumped thermal-liquid air energy storage system: Modeling, analysis, and optimization

Pumped thermal-liquid air energy storage (PTLAES) is a novel energy storage technology that combines pumped thermal- and liquid air energy storage and eliminates the need for cold storage. However, existing studies on this system are all based on steady-state assumption, lacking dynamic analysis and optimization to better understand the system’s performance under cyclic operation. To fill this gap, the mainbody-linearized cyclic dynamic model of the PTLAES system with packed bed thermal energy storage (TES) was first developed. Then, the dynamic characteristics of the baseline system were investigated. Sensitivity analyses were carried out on TES parameters. Minimal values of levelized cost of storage (LCOS) were observed for all parameters in the range of interest. Subsequently, the TES circuit was optimized, and a triple improvement of efficiency and energy density enhancement, discharge stabilization, and cost reduction was achieved. The optimized system's round-trip efficiency and energy density increased from 61.7% to 63.1% and from 141.9 kWh/m³ to 159.2 kWh/m³, and the LCOS decreased from 163.2 $/MWh to 159.4 $/MWh. A power offset ratio lower than 3% was reached, which is the lowest value ever reported in the literature. This study provides reference for future design and operation of the PTLAES system.

Empowering industry through energy auditing: a case study of savings and sustainability

Conducting energy audits is pivotal in assessing industrial plant efficiency and formulating effective energy management plans. It identifies opportunities for efficient energy use, reducing costs and environmental impact. This study employs a techno-economic approach to analyze electricity cost reduction in an industrial facility. Through energy auditing, it explores economic benefits and improved energy quality, yielding favorable outcomes. Focused on a plastic derivative manufacturing plant, the study reveals critical audit findings. The main aim is to identify avenues for electric energy savings, cutting production costs, and enhancing product competitiveness. The audit involves a detailed analysis of consumption patterns, signal quality, and potential energy management strategies, culminating in a cost-cutting plan. The results of an economic assessment of the suggested energy-saving strategies, provide a comprehensive evaluation of their financial implications. It reveals significant cost reduction opportunities, estimating annual energy savings of $45,824.56, which represents a 23.68% decrease in expenses. These initiatives not only boost the plant's financial performance but also strengthen its competitive edge.

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation

Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50%, leading to a 56% reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29% compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.

Sustainability of collective irrigation under water competition between agriculture and civil uses: the case study of Alanya Water Users Association (Türkiye)

This study explores the patterns of sustainable use and management of competing water sectors in the Alanya Water Users Association (WUA) in Southern Türkiye, over eight years (2013-2020) before the SARS-CoV-19 pandemic, focusing on the impacts of the recorded huge growth of tourism. Performance indicators of collective irrigation services are used to identify performance patterns and trends over time. The analysis has revealed a notable increase in water consumption for agricultural activities (+30%), driven by the cultivation of tropical fruits, without a proportional rise in crop production. Concurrently, effective financial management is observed in the WUA, with a consistent reduction in unit Management, Operation and Maintenance costs (-40% to 70%). Additionally, the total water demand has surged (+100%) due to population growth and tourist flux. However, this increase has tensioned water delivery to crops, indicating higher pressures over water availability for all uses. The study has identified three distinct patterns in the technical, financial, and socio-economic performance of the WUA, particularly highlighting the last four years of increased water usage and the disruption caused by the SARS-CoV-19 pandemic in 2020. Despite the current adequacy of water resources, optimized strategies for water management are advocated to address anticipated demographic growth, the introduction of tropical crops and the environmental impacts of climate change.

Bacterial nanocellulose production: Improvement in productivity and properties via a sustainable medium

High production cost is a significant barrier to commercial bacterial nanocellulose (BNC) production. This study addresses this issue using a low-cost molasses and cheese whey medium via Gluconacetobacter hansenii. The one-factor-at-a-time method investigated the effect of critical factors on BNC production, including total sugar and total protein concentrations (g/L), initial pH, and additives such as ethanol and acetic acid (%(v/v)). The productivity in the HS medium was 0.125 g/L/day, while the low-cost medium without additives achieved a productivity of 0.5275 g/L/day. Although the addition of ethanol decreased the productivity, the inclusion of 0.4 %(v/v) acetic acid increased the productivity to 0.64 g/L/day. Using the low-cost medium with acetic acid led to a 40-fold reduction in production costs compared to the HS medium. Furthermore, transitioning from HS media to the low-cost medium resulted in BNC with thicker fibres, a higher crystallinity index (%) and improved mechanical properties. The ratio of Iα/ Iβ in BNC produced in HS media decreased from 1.68 to 1.09 in the low-cost medium. The thermal properties of BNC produced in the low-cost medium also showed slight improvements compared to those in the HS medium. The degree of polymerization significantly increased from 1096 to 1457 in the low-cost medium compared to HS media. These findings highlight the potential of using a low-cost medium to produce BNC with enhanced properties, offering a sustainable and cost-effective alternative to conventional plastics.

Graph-accelerated non-intrusive polynomial chaos expansion using partially tensor-structured quadrature rules for uncertainty quantification

Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) method has been proposed for solving uncertainty quantification (UQ) problems. This method leverages the full-grid integration-based NIPC method to address UQ problems while employing the computational graph transformation approach, AMTC, to accelerate the tensor-grid evaluations. This method exhibits remarkable efficacy on a broad range of low-dimensional (three dimensions or less) UQ problems featuring multidisciplinary models. However, it often does not scale well with problem dimensions due to the exponential increase in the number of quadrature points when using the full-grid quadrature rule. To expand the applicability of this method to a broader range of UQ problems, this paper introduces a new framework for generating a tailored, partially tensor-structured quadrature rule to use with the graph-accelerated NIPC method. This quadrature rule, generated through the designed quadrature approach, possesses a tensor structure that is tailored for the computational model. The selection of the tensor structure is guided by an analysis of the computational graph, ensuring that the quadrature rule effectively capitalizes on the sparsity within the computational graph when paired with the AMTC method. This method has been tested on one 4D and one 6D UQ problem, both originating from aircraft design scenarios and featuring multidisciplinary models. Numerical results show that, when using with graph-accelerated NIPC method, our approach generates a partially tensor-structured quadrature rule that outperforms the full-grid Gauss quadrature and the designed quadrature methods (more than 40% reduction in computational costs) in both of the test problems.

Cooperative resource sharing and cost minimization in energy hub systems using an improved grasshopper optimization algorithm approach

This study presents a cooperative paradigm for energy hub systems (EHSs) where a network of interconnected hubs cooperates in exploiting the resources with the purpose of economic saving. In such an architecture, each hub provided with various sources of energy, such as combined heat and power (CHP), hot water tanks, renewable sources, electric chillers, and absorption chillers, will integrate all these sources for more adaptability and efficiency to the system. Moreover, the integration of energy storage systems (ESSs) is considered to enhance the flexibility of the energy hub concerning power, heating, and cooling. Recognizing the complexity associated with incorporating multiple constraints, the improved grasshopper optimization algorithm (IGOA) is introduced to effectively address this challenge. By leveraging this algorithm, the study aims to overcome the intricacies involved in considering various constraints and achieve an optimal outcome. The IGOA improves the efficiency and effectiveness of local and national searches in solving complex energy hub optimization problems. Reducing the likelihood of getting stuck in suboptimal solutions, enhances the algorithm's ability to find optimal solutions considering multiple constraints, thereby enhancing the overall performance and cost-effectiveness of EHSs. The issue is defined as a planning challenge, and by collaborative efforts, the expenses associated with the network energy hubs are reduced, illustrating the efficacy of this concept. The findings indicate the influence of the suggested cooperative technique, with operating cost reductions of 19.09 %, 13.27 %, and 8.75 % for Hub 1, Hub 2, and Hub 3, respectively. Furthermore, the cooperative framework eradicates energy deficits and disruptions, in contrast to 1,198.21 kWh of unfulfilled demand and 22 interruptions in the non-cooperative scenario. These results underscore the significant advantages of the collaborative technique in improving cost-efficiency, reliability, and resource utilization.

Sensitivity and Costs of Intraoperative Trans-Impedance Matrix Recordings, Spread of Excitation Functions, and X-ray Imaging in Detecting Cochlear Implant Tip Foldovers.

Evaluate the sensitivity and financial costs of Trans-Impedance Matrix recordings, Spread of Excitation functions, and x-rays in detecting cochlear implant tip foldovers. Tertiary academic medical center. 113 ears of 108 patients. Following cochlear implantation and before concluding surgery, intraoperative Trans-Impedance Matrix recordings, Spread of Excitation functions, and x-rays were conducted to evaluate presence of tip foldover. Presence of tip foldover; recording time necessary for and costs of Trans-Impedance Matrix, spread of excitation, and x-rays. There were six tip foldovers. Trans-Impedance Matrix showed 100% sensitivity, 100% specificity, 100% positive predictive value, and 100% negative predicative value in detecting tip foldovers. Spread of excitation showed 29% sensitivity, 99% specificity, 67% positive predictive value, and 95% negative predicative value. Trans-Impedance Matrix recordings were completed significantly faster than spread of excitation and x-rays. Elimination of x-rays from our intraoperative workflow results in a twofold cost reduction. Trans-Impedance Matrix recordings have potential great clinical utility in evaluating proper CI placement intraoperatively and reducing costs of surgery while not compromising patient care. Given the low tip foldover rate, a multicenter study is in progress to evaluate the sensitivity, specificity, positive predictive value, and negative predicative value of Trans-Impedance Matrix in a larger dataset. This can provide better guidance to cochlear implant clinics interested in evaluating the impact of using Trans-Impedance Matrix on patient care as well as the economics of reducing use of intraoperative imaging.

BIM-based optimization approach to reduce life cycle costs by focusing on the integration of construction and operation phases in office-commercial buildings

This study investigates the application of Building Information Modeling (BIM) to reduce life cycle costs (LCC) by addressing critical nonconformances encountered during the construction and operational phases of buildings. The primary aim is to identify nonconformances that escalate operational costs and to evaluate how BIM can effectively reduce these costs. The research begins with a comprehensive review of existing literature and utilizes the Delphi method to identify four key nonconformances that most significantly impact operational expenses. To further analyze these issues, semi-structured interviews and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to determine the cost ranges of components associated with these nonconformances in both the construction and operational phases. The study finds that BIM-based optimization can lead to substantial cost reductions, although there are limitations due to the lack of precise data on the operational phase of low-cost equipment. The key contribution of this research is the development of a framework that helps designers create more effective BIM models, enhancing facilities management (FM) and reducing LCC. The originality of this study lies in its integrated approach, combining BIM and optimization techniques to directly address operational cost issues, which provides a novel perspective on improving life cycle cost management in buildings. Future research should expand the dataset and explore a broader range of nonconformances to refine and extend the framework's applicability.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Effect of aged treatment on improving the performance of HVAF WC–Cr3C2–Ni coating on crystallization roller

The thermal shock resistance and high-temperature performance of WC–Cr3C2–Ni coatings on the surface of the crystallization roll after aged treatment were investigated in this study. The aging treatment at 320 ℃ for 2.5 h facilitated the diffusion of Cu element from the C17200 beryllium copper substrate toward the coating side of WC–Cr3C2–Ni coating, thereby enhancing the bonding between the coatings and substrates. After the aged treatment, the internal structure of the C17200 substrate changed from a dendritic structure to an equiaxed crystal structure, the hardness increased from 225.71 Hv0.5 to 406.52 Hv0.5 (80.4 % increased), and the wear resistance increased.Although the aging treatment reduced the hardness of the coating slightly (from 1257.58 Hv0.5 to 1155.16 Hv0.5, a decrease of 8.1%) and wear resistance (the coefficient of friction, from 0.335 to 0.312, a decrease of 6.9%). However, in the thermal shock test, the thermal shock resistance of the coating after aging treatment was increased from 19 times to 20 times (5.3% increased). However, the thermal shock failure cracks between the coatings and the substrates were transferred from the substrate part to the joint part of the two, which effectively avoided the waste of the crystallization roller substrate in the maintenance process. This research effectively reduces the purchase and maintenance costs of steel mills in actual production and provides theoretical support for cost reduction and efficiency increase in other HVAF (high-velocity air fuel) coating applications.

Renewable Power Energy Management for Single and Three-phase Inverters Design

This study manages solar panels, wind turbines, and fuel cells to develop single- and three-phase Sinusoidal Pulse Width Modulation (SPWM) inverter circuits. The maximum power point tracking method in direct current (DC)-DC boost converters efficiently uses these renewable sources. Photovoltaic (PV), wind turbines, fuel cells, and bidirectional batteries are utilized in energy management. Efficiency, material cost reduction, and user convenience depend on energy management. The technology fully charges the standby battery when all sources are available. Other renewable sources will power the system if one fails. Before boost converter circuits, renewable energies generated 310V for PV, 60V for wind energy conversion, 215V for fuel cells, and 25.9V for bi-directional batteries. Proportional-integral (PI) controllers govern inverter switching to maintain high-quality sinusoidal outputs while decreasing total harmonic distribution (THD). Single-phase load voltage and current at resistive load have 0.65419% THD. Load voltage and current root main square (RMS) values are 217.3617 (V) and 3.1052 (A), respectively. When connected to an R load, the three-phase inverter circuit provides 1.5146% THD for load voltage and current. Load voltage and current RMS readings are 218.4261 (V) and 2.1843 (A). THD is 1.5969% for load voltage and 1.2905% for load current with an RL load. In this example, load voltage and current RMS values are 214.201 (V) and 2.0916 (A). Simulations demonstrate the successful integration of renewable sources to meet energy needs and maintain power supply. The above solutions use renewable energy efficiently and integrate with the grid.

A new strategy for the comprehensive utilization of wastewater from the production of soda ash by sodium sulfate-based ammonia-soda process

The production of soda ash by the ammonia-soda process using carbon dioxide and ammonia for sodium sulfate is an effective method to realize the comprehensive utilization of waste resources of sodium sulfate, but this method produces a large amount of wastewater whose main substances are sodium bicarbonate and ammonium sulfate, which limits the modular industrial application of the production of soda ash by sodium sulfate-based ammonia-soda process(SSA-Process). At present, the main utilization method for the wastewater from the production of soda ash by SSA-Process is evaporation crystallization method to deal with the by-products of sodium sulfate and ammonium sulfate, which is a low-value, complex and energy-consuming process, so is an urgent need for a low-cost, high-value, short-process method to comprehensively utilize wastewater from the production of soda ash by SSA-Process. Based on the application theory of calcium hydroxide (Ca(OH)2) in the traditional ammonia-soda process, this present study systematically investigated the causticization law and mechanism of Ca(OH)2 in a simulated wastewater from the production of soda ash by SSA-Process. On this basis, a new process of Ca(OH)2 segmented causticization was developed to realize the comprehensive utilization of wastewater from production of soda ash by SSA-Process. The results showed that this process enabled a highly efficient ammonia cycle for cost reduction and the production of high purity ultrafine calcium carbonate products and sodium sulfate solids, as well as calcium-based raw material for building materials.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

AI for Optimizing Supply Chain Management

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

Conceptual framework for AI-driven tax compliance in fintech ecosystems

This paper comprehensively reviews the integration of Artificial Intelligence (AI) into tax compliance processes within fintech ecosystems. It explores the theoretical foundations of AI technologies, such as machine learning and predictive analytics, and how they can automate tax reporting, auditing, and compliance monitoring. Conceptual models for AI-driven tax compliance are proposed, highlighting the potential for increased efficiency, accuracy, and cost reduction. The paper also examines challenges associated with AI adoption, including algorithmic biases, ethical concerns, data privacy issues, and regulatory hurdles. Strategies for overcoming these challenges and fostering broader adoption are discussed. Finally, the paper offers recommendations for fintech companies and policymakers, emphasizing the need for transparent AI models, bias mitigation, data privacy, and updated regulatory frameworks to ensure fair and effective AI-enabled tax systems.

Harnessing predictive analytics to enhance medication adherence: A strategic model for public health impact

This review paper explores the critical role of predictive analytics in enhancing medication adherence, a significant challenge impacting global public health and healthcare systems. Medication non-adherence is linked to increased healthcare costs, poor health outcomes, and reduced quality of life for patients. By leveraging predictive analytics, healthcare providers can identify patients at risk of non-compliance, enabling targeted interventions that address individual barriers to adherence. The paper outlines a strategic framework for integrating predictive analytics into healthcare systems, emphasizing the importance of data sources, algorithm selection, and patient engagement. Furthermore, it discusses the broader implications of improved medication adherence for public health, including potential reductions in healthcare costs and enhancements in system efficiency. The conclusion offers strategic recommendations for policymakers and healthcare providers, highlighting the need for further research and technological advancements in predictive analytics to fully realize its potential in improving medication adherence. This review underscores the importance of data-driven decision-making in healthcare and its role in fostering a culture of adherence that benefits patients and communities alike.