Cost Reduction Research Articles

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.

The Real-world Effect of Long-acting Injectable Antipsychotics on Annual Medical Costs of Korean Schizophrenia Patients.

Schizophrenia is a severe and chronic mental disorder that significantly impacts cognitive, social, and occupational functions, leading to substantial economic burdens. Long-acting injectable (LAI) antipsychotics have been introduced to improve treatment adherence and outcomes, yet their economic impact remains debated. We aim to analyze the impact of LAIs on the medical costs of Korean schizophrenia patients. A retrospective analysis of 164 schizophrenia patients treated with LAI antipsychotics, paliperidone palmitate, and aripiprazole monohydrate at Korea University Guro Hospital between January 2017 and July 2022 was performed. Comparisons of inpatient department (IPD) and outpatient department (OPD) healthcare expenditures one year before and after LAI initiation were conducted. LAIs led to an increase in annual OPD costs (1,437.44 ± 1,127.60 to 4,015.42 ± 1,204.59; units: 1,000 KRW) but significantly reduced IPD admission associated costs (3,826.06 ± 5,500.63 to 698.06 ± 3,619.38; units: 1,000 KRW). After LAI administration, there was an overall reduction in total annual healthcare costs (5,263.49 ± 5,333.11 to 4,713.48 ± 3,625.89; units: 1,000 KRW), but it was not statistically significant. Although the use of LAIs did not significantly lower the first-year medical costs of schizophrenia patients, they offer beneficial economic impacts over time by reducing hospitalization-associated costs. Future research should focus on long-term cost analyses and the impacts of newer LAI formulations.

Survey Paper on IoT-Enabled Seed Dispensing Robot

As a result, the IoT-Enabled Seed Dispensing Robot is used to automate the seed planting process with precision technologies. Based on real time environmental conditions such as soil moisture and temperature it optimizes seed placement through GPS tracking and IoT sensor data. Remote monitoring and control of the robot is via a cloud-based platform resulting in significant reduction in labour cost and superior farming efficiency. Having the ability to handle terrain and obstacle mapping, it is well suited for any agricultural application and is a reliable, scalable way to meet the challenges of modern farming.

ЕФЕКТИВНІСТЬ БІЗНЕС-ПРОЦЕСІВ НА ОСНОВІ ТЕХНОЛОГІЙ ОЩАДЛИВОГО ВИРОБНИЦТВА

The article examines the issue of increasing the efficiency of business processes based on Lean Production technologies. Lean production is a modern management concept aimed at minimizing losses and optimizing enterprise resources. The article compares the efficiency of implementing lean production technologies to optimize business processes at enterprises and the impact of Lean technologies on cost reduction, improvement of the quality of goods and services, and productivity improvement. In particular, the main tools of lean production, such as 5S, kanban, kaizen, standardization of work processes, and their impact on reducing costs, improving product quality, and shortening production cycles are considered. It has been established that the application of Lean technologies helps to identify and eliminate actions that do not add value to the end user, thereby improving the performance of the entire technological production system. The importance of involving personnel in the process of implementing changes and forming a corporate culture focused on continuous improvement are emphasized separately. It has been established that the introduction of Lean Production tools improves the links of the technological cycle and ensures an increase in the efficiency of business processes at the enterprise. The choice of a specific tool depends on the strategic goals of the enterprise, the industry and the level of personnel training. It has been proven that in the national business environment there is a number of barriers that prevent the introduction of lean production technologies, such as: resistance to changes in personnel and management, personnel training, limitations in financial resources, insufficient flexibility of suppliers and untimely provision of resources, as well as instability of the national economy. The results of the study indicate that the implementation of Lean Production allows to significantly increase the efficiency of business processes, reduce costs, improve product quality and increase the flexibility of the enterprise in conditions of instability of the market environment. Key words: lean production, Lean Production, business process efficiency, cost optimization, product quality, production cycles, corporate culture.

An integrated model approach for disaster impact reduction: lessons from a slow onset disaster in Chile

Purpose The prolonged drought in Chile’s Coquimbo region has created a permanent state of emergency, forcing the state to spend millions each year distributing drinking water to rural communities. The purpose of this article is to detail a study focused on optimizing the supply of drinking water through trucks in the 15 communes of the region and how the problem was faced in a context of a slowonset disaster. Design/methodology/approach A geo-referenced census and optimization analysis of the 5.541 households that receive drinking water was conducted to determine the feasibility of removing trucks from the system. To generate a greater reduction in distribution costs, it was suggested to incorporate water distribution infrastructure projects, changing the concept of ‘expenses’ to ‘investment’. Findings The study was born out of the concern for the increased demand for drinking water from rural areas. The proposed and implemented framework allowed an additional 20% reduction in the initial transportation costs; this confirmed the assumption that the proposed optimization model alone would not offer a robust solution and was complemented and integrated with this type of alternative, forming an “integrated model”. Research limitations/implications The paper has implications for the resilience of territories affected by climate change. Practical implications The methodology can be replicated in other areas where similar interactions occur. Social implications Families impacted by drought can improve their quality of life and reduce distress in the face of the constant emergency. Originality/value This research aims to contribute knowledge from the perspective of a slow-onset disaster where water resources are scarce. It presents a framework where two disciplines converge, resulting in an “integrated model” that, through its implementation, reduces the costs of resource delivery while simultaneously improving the sanitary conditions of the beneficiary families.

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.

Energy efficiency and economic analysis of mechanized vs conventional cotton production system: A comparative study under rainfed vertisols of Tamil Nadu

Energy efficiency plays a pivotal role in optimizing input use and improving the sustainability of agricultural practices, especially in energy-intensive crops like cotton. Due to its high economic importance, cotton cultivation has been in the need of the hour in developing mechanized cultivation practices aimed at improving production efficiency. This study evaluated energy consumption and performed an economic analysis of mechanized versus conventional cotton production under rainfed vertisols in Tamil Nadu. Among the various inputs, fertilizer application consumed the most energy in both systems, followed by labor energy differences between the 2 methods. Mechanized cultivation drastically reduced labor energy requirements (174.2 MJ/ha) compared to manual methods (2215.6 MJ/ha), highlighting the potential for labor savings. Mechanized cultivation also showed more efficient use of non-renewable energy sources, whereas conventional methods relied more on renewable energy inputs, highlighting the trade-offs between the 2 systems. Mechanized cultivation significantly reduced production costs (Rs. 74290/ha) compared to conventional methods (Rs. 140440/ha), largely due to a 47.8 % reduction in labor costs, demonstrating its economic viability. This study suggests that improving the energy efficiency of mechanized cotton production should prioritize efficient fertilizer use and reducing diesel fuel consumption through enhanced machinery performance.

The bright side of supplier concentration: Investor attitudes towards the reopening policy in China

Supplier concentration (SUC) has disadvantage of vulnerability along with cost savings and efficiency. While current scholarship focus on the vulnerability of firms with centralized suppliers during the COVID-19 epidemic, there is no empirical study that explores the impact of post-disaster SUC on firm value as countries removing regional isolation policy. I focus on the impact of COVID-19 reopening policy on investor attitudes towards SUC after the resolution of a supply chain disruption crisis. I try to examine whether investors still perceive SUC as a risk signal or as a positive signal for rapid recovery. Using the event shock of China’s reopening announcement and data on A-share listed companies, I find that SUC has a positive impact on cumulative abnormal returns at reopening. I also find that positive effect of SUC is more prominent for firms that benefit from a larger reduction in transaction costs due to the reopening policy. I also analyze the moderating effect and find that information intermediaries such as analysts and media attention amplify the positive effects of SUC. My research provides new perspective on achieving post-disaster value enhancement through SUC.

Utilization Fraction of Ambulatory Hand Procedures: Cost-Reduction Through Surgical Instrument Tray Optimization.

Our objective is to evaluate the utilization fraction (UF) of surgical instruments during a commonly performed ambulatory hand surgery case as an avenue for cost reduction, increased operating room efficiency, and systems quality improvement. The total number of instruments opened at the start of the case was recorded followed by instruments being divided into those used and not used during the procedure. Total sterile processing costs were estimated at $1.56 per instrument according to data from our institution's central sterilization processing (CSP) department. Nineteen hand procedures performed by 2 surgeons were included in this study. An average of 120.1 ± 10.9 instruments were opened at the start of each case, while an average of 12.6 ± 5.4 instruments were used per case (Figure 1). This yielded an UF of 10.7% ± 4.8%. Using our internal CSP estimate, we calculated an annual cost of $16 863 to reprocess the current hand tray (Figure 2). Using literature data, this cost ranged from $5 513 to $34 484 annually. The same cost calculations were performed for the theoretical optimized tray (incorporating instruments used at least 20% of the time when opened) containing 23.2 instruments. The annual reprocessing cost of this new tray according to CSP data was $3 260, demonstrating a cost-reduction of $13 603 or 80.7% (Figure 2). Evaluation of pre- and peri-operative processes is a valuable technique to mitigate increasing healthcare costs and reduce unnecessary healthcare spending, with broad applicability to multiple surgical subspecialties and procedures.

An inventory allocation model for cost reduction in the supply chain of a payment means company

The Supply Chain Management (SCM) has been gaining greater relevance in the sector, leading companies to improve the level of customer service and keep costs under control. However, it was identified that SCM is a less explored field in the acquirer market, so this work is an opportunity to contribute to the academic research. It was observed the opportunity to build an inventory model considering transportation as a variable cost, unlike most models found in literature. The objective of the study was to develop an inventory allocation model for the SIMcard chain in a Brazilian company in the acquiring sector, keeping the service level agreed with the customer. Inventory and transport costs were calculated for different supply cycles for all SIMcards operators and transportation hubs, to obtain the lowest total cost configuration in the supply chain. The results show that the costs cannot be considered individually and considering both costs, transport and inventory, there is an opportunity to increase efficiency in the studied operation of this work.

A Study on the Effectiveness of Classifiers in Diagnosing Diabetes Using Machine and Deep Learning Models

Machine learning and data mining have a crucial role in assisting in the early detection of diabetes, potentially resulting in improved health outcomes and more effective management for individuals at risk. This process is frequently termed diabetes prediction or diabetes risk assessment. This paper considers diabetes another-related dataset for applying data mining techniques to find suitable variables for future predictions. Machine learning algorithms can be harnessed in Industrial Internet of Things (IIoT) applications to unlock the advantages of cost reduction, enhanced efficiency, and improved performance. In the modern era, we've all witnessed the benefits of machine learning techniques, from streaming movie services suggesting titles based on viewing habits to identifying fraudulent activity through customer spending patterns. These algorithms excel at handling vast and intricate datasets, uncovering intriguing patterns and trends, including anomalies. This paper considers diabetes another-related dataset data data like age, gender, family diabetes, highbp, physically active, bmi, smoking, alcohol, sleep, sound sleep, regular medicine, junkfood, stress, bplevel, pregancies, pdiabetes, uriationfreq, diabetic. The machine learning approaches which is used to analysis and predict the dataset using Logistic, Multilayer Perceptron, SMO, Decision Stump, Hoeffding Tree, J48, and LMT. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Higher healthcare cost and utilization before and after diagnosis of AATD in the United States.

Patients with alpha-1 antitrypsin deficiency (AATD) often experience substantial delays from the onset of symptoms to a diagnosis. We explored the impact of delayed diagnosis of AATD on healthcare costs and utilization by assessing costs/utilization before and after diagnosis. Retrospective claims data was used to conduct a longitudinal analysis of a cohort of patients with follow-up over four years in a commercial claims database was conducted. Patients with at least four years of claims experience between the years 2011 - 2017 were included in this study. Outcome measures were calculated for each year (Year 1 pre-index diagnosis, and Years 1, 2, and 3 post-index follow-up). Measures included healthcare costs (pharmacy and medical costs), medical costs, inpatient events, and emergency room visits. Unadjusted measures in the follow-up Year 1, Year 2, and Year 3 were compared to Year 1 pre-index. A separate multivariate analysis adjusting for age, sex, and comorbidities was conducted. Among 1258 patients, mean adjusted healthcare costs were significantly higher in Year 1 post-index compared to Year 1 pre-index ($51,785 vs $41,441, p = < 0.05). In Year 2 ($36,937 vs $41,441, p = < 0.05) and 3 ($28,558 vs $41,441, p = < 0.05) post-index, mean adjusted healthcare costs decreased compared to Year 1 pre-index. Adjusted medical costs were similar in Year 1 ($25,034) post-index compared to Year 1 ($22,952) pre-index but were significantly lower in Year 2 ($15,242 vs $25,034, p = < 0.05) and Year 3 ($8,779 vs $25,034, p = < 0.05) post-index. The frequency of inpatient and emergency room events was significantly lower in all three observation periods following diagnosis in the unadjusted analysis. The adjusted analysis showed similar findings, except for emergency room visits, which were similar across all observation periods. Patients with AATD had substantial healthcare costs/utilization in the year before diagnosis. Costs were significantly higher in the first year following diagnosis. However, subsequent years showed cost reductions to levels below pre-diagnosis. These data support the need for strategies to reduce the time from symptom onset to diagnosis.

On-body sensing technologies and signal processing techniques in addressing safety of human machine collaboration

AbstractSafety is a challenge in human machine collaboration despite of the advantages in achieving efficiency, cost reduction and productivity in a collaborative scenario between human and machine/robot. During collaboration with machines, the user might not be able to follow the collaborative tasks as expected due to the cognitive burden causing potential safety concerns such as collision. Addressing this challenge, the aim of this paper is to explore the potential of on-body sensing systems in study of user experience and the psychological condition during the collaboration between machines and human. As the psychological condition is reflected in physiological signals, sensing technologies and signal processing techniques to extract features from physiological signals are explored with applicability in human machine collaboration scenarios. An experiment is designed utilising an industrial collaborative robot arm while quantitative and qualitative data is gathered for this purpose exploring the problem to study user experience and impact of mental strain and cognitive workload on user performance and experience during human machine collaboration. Results show that an adaptive machine to user experience measured by on-body sensing systems during the collaboration has the potential to address safety in human machine collaboration while improving performance and user experience.

Antitumoral action of carvedilol–a repositioning study of the drug incorporated into mesoporous silica MCM-41

We have studied repositioning of carvedilol (an antihypertensive drug) incorporated into MCM-41 mesoporous silica. The repositioning proposes a reduction in the slow pace of discovery of new drugs, as well as toxicological safety and a significant reduction in high research costs, making it an attractive strategy for researchers and large pharmaceutical companies. We obtained MCM-41 bytemplatesynthesis and functionalized it by post-synthesis grafting with aminopropyltriethoxysilane (APTES) only or with folic acid (FA), which gave MCM-41-APTES and MCM-41-APTES-FA, respectively. We characterized the materials by scanning and transmission electron microscopy, zeta potential (ZP) measurements, Fourier transform infrared absorption spectroscopy, x-ray diffractometry, nitrogen gas adsorption, and CHNS elemental analysis. We quantified the percentage of drug that was incorporated into the MCM-41 materials by thermogravimetric analysis and evaluated their cytotoxic activity in non-tumor human lung fibroblasts and the tumor human melanoma and human cervical adenocarcinoma cell lines by XTT salt reduction (2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-arboxanilide). The x-ray diffractograms of the MCM-41 materials displayed low-angle peaks in the 2θrange between 2° and 3°, and the materials presented type IV nitrogen adsorption isotherms and H2 hysteresis typical of the MCM-41hexagonal network. The infrared spectra, the charge changes revealed by ZP measurements, and the CHN ratios obtained from elemental analysis showed that MCM-41 was amino-functionalized, and that carvedilol was incorporated into it. MCM-41-APTES incorporated 23.80% carvedilol, whereas MCM-41 and MCM-41-APTES-FA incorporated 18.69% and 12.71% carvedilol, respectively. Incorporated carvedilol was less cytotoxic to tumor and non-tumor cells than the pure drug. Carvedilol repositioning proved favorable and encourages further studies aimed at reducing its cytotoxicity to non-tumor cells. Such studies may allow for larger carvedilol incorporation into drug carriers or motivate the search for a new drug nanocarrier to optimize the carvedilol antitumoral activity.

LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches

The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny—lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices.

Novel sampling strategy for regular nucleic acid testing in low risk areas during epidemics.

Large-scale regular nucleic acid testing (NAT) such as Regular-1/7, played a crucial role in the successful and sustained containment of COVID-19 in China, enabling rapid case detection in low-risk areas. However, Regular-1/7 strategies are extremely costly and time-consuming. To address these challenges and facilitate the broader implementation of large-scale NAT during outbreaks of highly contagious diseases with long incubation periods, we propose a novel sampling strategy called Weighted Hub and Time Sampling (WHTS). This strategy considers the intensity of social activity, the degree of social overlap, and the time interval since the last NAT for each individual. Based on a scale-free contact network model, we simulated the spread of scenario in a community and compared the performance of the WHTS strategy to the Regular-1/7 strategy in terms of cost reduction and shorter alert time. The results show that WHTS-1/14 strategy can save half of the cost, and WHTS-1/7 strategy can alert 1.0 days in advance, compared to Regular-1/7 strategy. Additionally, The WHTS-1/14 strategy also achieves promising results under various scenarios, even though it samples half as many people as the Regular-1/7 strategy. Our study offers a valuable reference for large-scale testing in future efforts to manage emerging infectious diseases.

Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

The article presents an exergy analysis of a district heating system (DHS) that incorporates a predictive model based on neural network algorithms. The study focuses on evaluating the thermodynamic efficiency of the DHS when these methods are applied, enabling the prediction of temperature changes and rapid adjustment of system parameters to enhance efficiency. The impact of increasing the number of connected consumers on the exergy efficiency of the DHS with the predictive model (DHPM) is examined. The use of neural network algorithms, such as LSTM, significantly enhances the system's ability to predict external temperature changes and respond promptly, resulting in optimized energy consumption and improved exergy efficiency, particularly at the beginning and end of the heating season. A comparative analysis of the exergy efficiency of traditional DHS and the system with the predictive model is included. The exergy analysis is based on modeling the system’s performance with varying numbers of consumers. The results indicate that an increase in the number of consumers leads to a rise in exergy efficiency due to better energy distribution management. The potential for significant thermal energy savings and reduced operational costs through the use of predictive methods is discussed. The study confirms that applying neural network algorithms in predictive models of DHS can substantially improve efficiency and reliability, leading to a more rational use of energy resources, cost reduction, and minimized environmental impact. The findings support the adoption of these methods in large-scale district heating systems for optimization and enhanced energy efficiency.

Unlocking the drivers behind logistics service providers' participation in Morocco's logistics clusters

PurposeIn recent years, the concept of logistics clusters has attracted the attention of several researchers and practitioners. It is an agglomeration of different types of companies and operations, notably those providing logistics services, such as supply chain organization, storage and distribution. The paper focuses on this concept by examining the factors influencing LSPs to enter the logistics clusters.Design/methodology/approachAn exploratory qualitative study based on semi-structured interviews was conducted. Regarding the sample size, the author interviewed 31 professionals belonging to logistics clusters located in the Moroccan economic metropolis of Casablanca.FindingsThe results show that the cost reduction, the reputation of the cluster, the learning from other LSPs, the communal services, the geographical proximity and the role of public authorities are the main factors impacting the entry of logistics service providers into these clusters.Originality/valueThis theme has never been investigated. Therefore, this research expands the literature review and tries to examine this gap of literature by studying the factors that can affect the adhesion of logistics service providers to these clusters.