Existence and uniqueness of solutions to neutrosophic fractional differential equations and their implications for inventory controls model

Currently, the business “MELQUIAS AUTOMOTRIZ,” located in Salamanca, Gto., is facing serious operational difficulties due to poor inventory control and errors in order management, despite having a digital system in place. The lack of accurate records leads to mistakes when fulfilling orders, resulting in financial losses and customer dissatisfaction. To address this issue, the development of a web-based system for inventory control and order management is proposed. This system will allow for the registration of incoming and outgoing products, efficient order handling, and the generation of detailed reports on sales, suppliers, and customers. The proposed solution aims to improve inventory accuracy, customer service, and overall business profitability. This research seeks to demonstrate that the adoption of appropriate technologies can positively transform the operations of automotive parts businesses.

Like many other industries, the conveyor lift belt distribution market encounters many issues related to inventory, including issues such as inventory control, restocking, and warehouse organization. Most of the pertinent literature, including the ones on Lean Warehousing and DDMRP, focuses on them separately. Quite a number of scholars have tried to cross-integrate these two approaches, though in the context of Lean Warehousing and DDMRP pertaining to commercial Small and Medium Enterprises. This research gap on the integration of DDMRP and Lean Warehousing has been addressed by proposing a 5S DDMRP model whereby DDMRP streamlines inventory conditioning and reliability in addition to the optimization of inventory. The model developed from this research attempts to bridge the gap in the model’s effectiveness in meeting demand and usefulness in aligning warehouse operations with demand-driven logic. Evidence from analyses of DDMRP implementation shows stockouts declined by 11.62%, inventory accuracy increased by 61.20%, and an overall 32% cost reduction. This research sheds light on the inventory flow of DDMRP in terms of reliability and efficiency. The 5S DDMRP model aims to assist Small and Medium Enterprises by providing a practical solution that does not involve overly sophisticated warehouse automation. On the other hand, practitioners and analysts are encouraged to focus on expanding the integration of automation, which has been proven to enhance investment adaptability alongside the investment needed to enhance it. This particular attribute will enhance the system’s responsiveness and multi-capability to variable demand integration in real time.

Grouper is a high-value fishery commodity with increasing demand in both domestic and international markets, necessitating a stable raw material supply to support sustainable processing activities. However, feasibility assessments for developing grouper processing industries in Indonesia rarely prioritize raw material availability, despite this factor being essential for maintaining consistent production. This study evaluates the feasibility of establishing a grouper processing industry in Java by integrating cross-regional supply mapping with an inventory control analysis based on the Safety Stock (SS) and Reorder Point (ROP) approaches. A mixed-methods design was used, combining semi-structured interviews, field observations at ten major supply points along the northern coast of Java, and analysis of twelve months of supply and demand data. Findings indicate that the total monthly grouper supply reaches approximately 254 tons, while industrial requirements are around 50 tons. Cold chain capacity, logistics conditions, and the distance of distribution influence price differences across regions. Seasonal fluctuations can be managed by relying on both capture fisheries and floating net cage aquaculture, which help maintain a consistent supply throughout the year. A simulation using an SS value of 602 kilograms and an ROP value of 2,402 kilograms demonstrated that no stock outs occurred during a ten-day evaluation. Overall, the study confirms that the supply of grouper raw materials in Java is stable, feasible, and capable of supporting the growth of processing industries, providing a practical basis for future supply chain planning. These findings offer guidance for policymakers and industry stakeholders nationwide. Keywords: grouper supply chain, raw-material feasibility, inventory control, safety stock, reorder point

In the current printing industries, precise forecasting of printing ink patterns is the key to cost reduction, inventory control, and environmentally friendly functioning. Conventional methods of estimation are based on coverage assumptions, which are always static and operator experience which frequently results in wastage of ink, delay in production and erratic quality. The paper provides an in-depth predictive modelling platform of ink consumption estimation based on statistical, machine learning, and deep learning methods. The proposed strategy is one that formulates ink usage prediction as a supervised regression, from which the heterogeneous inputs include the type of paper, the area covered, the color density, the print resolution, and the machine configuration parameters. The data is obtained during print job logs and in-built machine sensors and job specification files and past production logs. To increase predictive relevance and robustness, superior pre-processing methods are used, such as feature engineering of color coverage measures, ink density measures, and print complexity measures. The comparison between methods of baseline linear regression and statistical forecasting models and machine learning methods including decision trees, random forest, support vectors regression, and gradient boosting are made. Moreover, the deep learning models such as artificial neural networks, long short-term memory networks, and hybrid architectures are determined to obtain nonlinear relationships and temporal dependencies between printing workflows. Experimental evidence shows that ensemble and deep learning models are much more successful than the classical approaches, with lower error in prediction and overall generalization to a variety of print jobs.

The rapid expansion of the hospitality industry in Southeast Asia, driven by significant growth in international arrivals and evolving digital expectations, has heightened the need for service operations that are both efficient and data-driven. Despite advancements in digital infrastructure, many full-service hotels in the region continue to experience operational inefficiencies due to fragmented data systems, limited analytical competencies, and weak integration of data insights into managerial decision-making. This study investigates how data-driven decision making (DDDM) enhances service operations efficiency in full-service hotels across Southeast Asia by examining the extent of analytical adoption and its operational impact. A mixed-methods approach is employed, combining quantitative assessments of operational efficiency indicators with qualitative insights from hotel managers and frontline supervisors. The study integrates multiple operational dimensions labor scheduling, housekeeping performance, inventory control, guest service responsiveness, and revenue optimization to provide a holistic evaluation of DDDM effectiveness. Findings reveal that hotels implementing advanced data-driven practices achieve substantial gains in labor productivity, faster service response times, and improved resource utilization compared to hotels that rely on manual or non-integrated systems. Contextual factors such as staff analytical capability, digital infrastructure maturity, and leadership commitment significantly moderate the effectiveness of DDDM. This study contributes to hospitality management literature by offering the first integrated empirical framework linking DDDM with multidimensional service operations efficiency in Southeast Asian full-service hotels. Practical recommendations are provided to guide hotel leaders in designing data-driven strategies that improve operational performance and strengthen competitive advantage in an increasingly digital hospitality landscape.

This study focuses on the design, implementation, and assessment of a web-based intelligent inventory and sales management system employing the First In, First Out (FIFO) method to enhance stock precision, operational efficiency, and data visibility in microenterprises such as Warung Madura. These small-scale retailers generally depend on manual record-keeping, leading to data inconsistencies, reporting delays, and revenue losses due to chaotic inventory management. The study applies the Rapid Application Development (RAD) methodology to facilitate agile and iterative software creation, ensuring consistent alignment with user requirements through recurrent feedback loops. The system was built using PHP with the CodeIgniter 4 framework, MySQL for database management, and modeled using Unified Modeling Language (UML) diagrams to describe its structure. Evaluation results indicate strong system reliability: Black Box testing confirmed that every module functioned correctly, while User Acceptance Testing (UAT) involving 21 participants produced an overall satisfaction rate of 80.67%, categorized as Good. Furthermore, the integration of FIFO-based logic enhanced the accuracy of stock recording and the speed of report generation, leading to a 40% reduction in manual errors compared to traditional bookkeeping methods. This study promotes the digital transformation of microenterprises by introducing an efficient inventory management solution that integrates accounting-based inventory control within an accessible web platform. In subsequent development stages, priority will be given to integrating mobile and cloud technologies to improve system accessibility, extend usability across diverse retail environments, and encourage broader implementation.

Triple concepts, including statistical process monitoring, inventory control, and maintenance, are integrated to achieve more realistic results for imperfect processes. Previous research has developed integrated models in two contexts: (1) single-product systems with multiple assignable causes, and (2) multi-product systems with a single assignable cause. This study aims to optimize an integrated model for an imperfect production process in a multi-product system, considering the occurrence of multiple assignable causes. The model also accounts for process stoppages due to system failures. Constraints are imposed to prevent inventory deficits for each product and idle time within each production cycle, ensuring that all products are manufactured at least once per cycle. An X-bar control chart is employed to monitor the process and detect out-of-control states. Given the complexity of the model, a particle swarm optimization algorithm is used to determine the decision variables, which include cycle time, sample size, sampling interval, and control limit coefficients. The model's applicability is demonstrated through an industrial case study. Additionally, comparative analyses are conducted, and a sensitivity analysis is performed to evaluate the effects of key parameters on the optimal results.

Efficient inventory management plays a crucial role in ensuring accuracy, reducing operational delays, and supporting data-driven decision-making. This study develops a mobile inventory management application that integrates barcode scanning technology to enhance process efficiency. The research method includes requirement analysis, system design, development, and system evaluation. System testing involves black-box testing for functional validation, performance testing to measure response time, and usability evaluation using the System Usability Scale (SUS). Experimental results show that the application increases data entry speed by 43%, achieves barcode scanning accuracy up to 98.5%, and reduces human input errors by 72% compared to manual recording. Performance testing indicates an average response time of 1.2 seconds, meeting mobile application usability standards. These findings demonstrate that the proposed system significantly improves accuracy, speed, and reliability in inventory operations. The scientific contribution of this research is the formulation of a validated and replicable mobile–barcode integration model optimized for small and medium-scale businesses requiring real-time inventory control.

This study aims to identify the causes of delays in packaging material stock at PT XYZ, which lead to production downtime and imbalance between critical and excess inventory. A qualitative case study approach was applied using interviews, observations, and documentation. Data were analyzed through the Lean Supply Chain Management (LSCM) framework and the DMAIC methodology, while the Fishbone Diagram was used to determine root causes. The results indicate two major forms of waste: waiting waste arising from manual PR and PO approvals, and inventory waste due to the absence of safety stock and non–real-time inventory updates. These issues stem from limited human resources, manual processes, and weak information flow. The study recommends digitalizing procurement processes and improving inventory control and coordination.

This study aims to implement a web-based inventory control system at OTW29 Nasi Padang Vegetarian to improve stock management efficiency. Previously, inventory records were managed manually, leading to frequent inaccuracies and disruptions in operational processes. The developed system integrates the Economic Order Quantity (EOQ) and Reorder Point (ROP) methods to determine optimal order quantities and reorder levels. The implementation results show that the system is capable of providing real-time stock information, increasing recording accuracy, and supporting faster and more precise raw material ordering decisions. The system has proven effective in reducing the risk of stockouts or overstocking and enhancing the overall operational efficiency of the MSME.

This article presents a case of a company dedicated to the sale, installation, and service of air conditioning and heating. The company has no internal inventory control in its warehouses. In the period 2022-2023, a total of 3 043 problems related to warehouse management were recorded, with an average of 84,53 problems/month. These deficiencies have led to economic setbacks, project delays, inadequate delivery times and the impossibility of guaranteeing their quality certification objectives in the short term. The objective of this study is to reduce these problems. As a methodology, lean manufacturing tools such as 5S, kaizen and kanban are implemented. After the implementation of the improvement, the problems in the warehouses were reduced to 79, with an average of 26,3 problems/month by the end of June and beginning of November 2023. It is concluded that the application of lean manufacturing tools improves efficiency rates in warehouse management.

Digital transformation in Indonesia's manufacturing sector has accelerated the adoption of warehouse management systems, with the automated warehouse market projected to grow from USD 25.6 billion in 2025 to USD 54.3 billion in 2031. Traditional paper-based warehouse operations created inefficiencies, environmental degradation, and operational challenges, including poor traceability, coordination gaps, and significant paper waste. This research aimed to design a comprehensive Finished Goods Warehouse Management System integrated with Outgoing Quality Control (OQC) functionality to address operational challenges at PT. XYZ, including limited human resources, inconsistent inspection duration, inadequate location tracking, and excessive paper consumption of approximately 200 sheets per month. The study employed a qualitative case study approach using the Framework for the Application of System Thinking (FAST) methodology, encompassing Scope Definition, Problem Analysis, Requirements Analysis, and Logical Design phases. The PIECES framework was used to evaluate system feasibility across Performance, Information, Economics, Control, Efficiency, and Service dimensions. Data collection involved observation, semi-structured interviews with warehouse administrators, quality control staff, OQC personnel, and production planning and inventory control (PPIC) staff, along with document review. The research produced comprehensive system models, including use case diagrams, activity diagrams, sequence diagrams, Entity Relationship Diagrams, Data Flow Diagrams, and user interface prototypes. The designed system integrated real-time status updates, automatic blocking mechanisms, barcode scanning technology, digital inspection forms with AQL-based auto-calculation, and complete traceability throughout the supply chain.The integrated WMS design provided practical solutions for improving operational efficiency, eliminating paper waste, ensuring product quality through mandatory quality control integration, and supporting sustainable manufacturing practices in the plastic injection molding industry.

In supply chain management, the location of facilities, inventory control, and vehicle routing are three key components. This paper incorporates a two-warehouse inventory system into the location- inventory-routing problems (LIRPs) and develops LIRP models with two warehouses in one-level, two-level, and three-level supply chain networks. This study aims to minimize the average total costs of the models by reducing their average costs. To handle these models, two innovative hybrid algorithms, viz. Clarke and Wright—genetic algorithm (CW-GA) and Clarke and Wright—firefly algorithm (CW-FA) are put forward. Computational experiments and sensitivity analyses are conducted to compare the proposed two algorithms with Baron and test the algorithms’ effectiveness and the models’ feasibility. The management implications of this study are presented from two dimensions: model and method. Finally, future research directions and the gap between models and reality are discussed.

Effective inventory management in multi-echelon supply chains is challenged by stochastic demand and uncertain lead times, which amplify variability and increase operational costs. This study presents a Markov chain framework for modeling, analyzing, and optimizing multi-echelon inventory systems under stochastic lead-time conditions. The framework represents inventory levels and lead-time states as a probabilistic transition system, enabling computation of steady-state distributions, stockout probabilities, and transient recovery times. Analytical results are validated through Monte Carlo simulations, demonstrating high fidelity between theoretical and empirical distributions. Numerical experiments quantify the impact of lead-time variability on inventory performance, revealing nonlinear increases in stockout probability and total system cost as lead-time variance grows. Multi-echelon analyses demonstrate the emergence of the bullwhip effect and highlight the effectiveness of information sharing in mitigating variability propagation across echelons. Comparative benchmarking against deep reinforcement learning (DRL) policies shows that while DRL achieves marginally lower total costs, the Markov-based approach provides superior interpretability, robustness, and computational efficiency. The study offers theoretical contributions by unifying stochastic multi-echelon dynamics and transient analysis within a tractable Markov framework. Managerially, it provides actionable insights on lead-time variance reduction, cross-echelon visibility, and hybrid analytical–learning policy design. The framework establishes a foundation for resilient and cost-effective inventory control in complex, uncertain supply-chain networks.

Andexanet alfa was recommended by the National Institute for Health and Clinical Excellence (NICE) as an option in the management of life-threatening gastrointestinal bleeding in patients taking apixaban or rivaroxaban in May 2021. A recent UK-wide survey of local hospital protocols for the use of andexanet alfa suggested that practice across the United Kingdom is highly variable. In January 2025, NICE was unable to make a recommendation on the use of andexanet alfa for reversing anticoagulation in adults with intracranial haemorrhage. We set out to report trends and variation in the uptake of andexanet alfa across the NHS in England, using the new OpenPrescribing Hospitals platform. To assess the uptake of andexanet alfa, we analysed pharmacy stock control data from NHS trusts in England using the openly available Secondary Care Medicines Dataset. Analysis was restricted to NHS trusts with 24-h consultant-led emergency care activity. Between May 2021 and June 2025, 19 608 vials of andexanet alfa were issued in NHS trusts in England. There was wide variation in the timing and speed of uptake across NHS trusts. Substantial variation was also observed between NHS Regions in England. The use of andexanet alfa varies markedly between NHS regions and between NHS trusts in England. This study is the first analysis of adoption of a new treatment using the OpenPrescribing Hospitals platform, which is expected to provide a generalizable framework for similar analyses in the future.

Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice. This review evaluates the integration of AI tools such as IBM Watson, Medisafe, ScriptPro, and BenevolentAI into various pharmaceutical functions, including drug discovery, medication management, clinical decision support, pharmacovigilance, and inventory systems. AI applications in pharmacy contribute to optimizing operations by automating prescription verification, predicting medication adherence, minimizing dispensing errors, and enabling real-time inventory control. The integration of AI in pharmacy has the potential to improve patient safety, reduce medication errors, and enhance the overall quality of care. However, challenges such as data standardization, regulatory frameworks, and workforce development must be addressed to ensure the successful adoption of AI in pharmacy practice. This review provides insights into the current state of AI in pharmacy and its future directions.

PT. XYZ operates in the food distribution sector and handles a large number of inbound and outbound goods transactions each day. The high transaction volume creates challengess in managing inventory effectively, highlighting the need for an integrated data management solution. This research aims to develop a data warehouse for PT. XYZ by applying the Kimball Nine-Step methodology. Data were collected through interviews, field observations, and a review of relevant literature. The data warehouse design process includes identifying fact tables and dimension tables related to receiving and shipping activities, followed by the implementation of the Extract, Transform, Load (ETL) process using SQL Server Management Studio 19. The findings indicate that the proposed data warehouse is capable of integrating operational data and presenting inventory information through reports generated using Microsoft Excel. The system supports improved stock control and enables management to make faster and more accurate decisions.

Effective drug inventory management is critical for ensuring the availability of essential medicines while minimizing costs and reducing wastages in healthcare institutions. This study presents a comprehensive approach that integrates Always Better Control (ABC) and Vital Essential Desirable (VED) analysis with multi criteria analysis to optimize pharmaceutical inventory control. By combining these two methods, inventory items are stratified into more meaningful categories for prioritization. Research is warranted in light of the case Hospital's ongoing inventory management problem. This project uses both qualitative and quantitative data collection approaches in order to design a drug inventory control system specifically for the pharmaceutical department at the case Hospital. A normalization technique is applied to avoid data quality issues, reduce data redundancy, improve data analysis, and enhance data security. The Economic Order Quantity (EOQ) model and forecasting methods are also applied. In the end, the inventory cost is decreased from 207,600 to 170,998B ETB, a noteworthy 17.63% improvement that can be applied in real-world situations. The proposed ABC-VED with multi criteria analysis framework enables healthcare facilities to allocate resources more efficiently, improve stock management, and ensure the uninterrupted supply of critical medications. This integrated approach supports evidence based policy making in pharmacy management and offers a scalable model adaptable to varied healthcare settings.

The agricultural industry is undergoing a significant digital transformation through Cloud Engineering, Enterprise Automation, and AI-enabled systems infrastructure. These technologies are shifting production practices away from traditional agriculture and towards data-driven precision agriculture. Cloud-based platforms are processing immense amounts of real-time data collected from Internet of Things (IoT) sensors, drones, satellite imagery, and other devices dispersed throughout the agricultural field. AI-enabled analytics are employed to improve key on-farm practices, including soil health tracking, precision irrigation control, and pest management. Machine learning algorithms establish trends across numerous growing seasons, making the prediction of crop yields more accurate than ever. Enterprise automation is beginning to reshape agricultural supply chains through intelligent inventory control systems and autonomous farming machinery to cultivate agricultural products. Blockchain technology ensures transparency and tracking of all participants in the supply chain from farm to table. Deep learning frameworks are utilized to parse complicated agricultural data and generate applicable insights for farmers to implement. Robots and autonomous tractors are increasingly employed to solve labor shortages and improve efficiencies. Surface-enhanced Raman spectroscopy, in connection with deep learning frameworks, allows for fast assessment of pesticide residue on produce. IoT-enabled irrigation systems apply water with precision to improve water conservation. All of these technologies are being applied to better facilitate the agriculture sector by adopting more resilient, efficient, and decent sustainable production systems that are economically feasible and environmentally mindful.