Stock Level Research Articles

Road Freight Demand Forecasting Using National Accounts’ Data—The Case of Cereals

This paper investigates the potential of utilising historical agricultural production data for enhancing road freight transport forecasting, focusing on cereal production. This study applies a multiple linear regression analysis using national statistical accounts and secondary data. The data were sourced from Finland’s Statistics Agency and the Natural Resources Institute. The analysis identifies an observable correlation between agricultural production and road freight volumes, although this correlation is not statistically significant. The highest adjusted R² observed in the models was 0.62. The analysis reveals that previous years’ production data can help forecast future road freight volumes, with vehicle mileage estimable from recent production and stock levels. Additionally, annual percentage changes in the volume of transported cereals can be partially predicted by the changes in total available cereals and opening stocks from two years prior. This exploratory research highlights the untapped predictive potential of agricultural production variables in forecasting road freight demand, suggesting areas for further forecasting enhancement.

Evaluating the success of eradicating nonnative Brook Trout using Trojan Y Chromosome fish in southern Rocky Mountain streams

AbstractObjectiveBrook Trout Salvelinus fontinalis often outcompete and displace native salmonids in western North America. Due to the limitations of traditional removal methods, managers are implementing the Trojan Y Chromosome approach to extirpate nonnative Brook Trout populations. However, there are uncertainties of the comparative performance (i.e., survival, reproductive success) of YY‐male (MYY) fish and their wild counterparts. In addition, it is unclear how effective YY‐female (FYY) fish compare with MYY fish for extirpation of wild Brook Trout. To address these uncertainties, we modeled the time to extirpation of wild Brook Trout using 5 years of empirical data, assuming introductions of either MYY or FYY fish.MethodsOur simulations assumed varying stocking (0% to 100%) and removal (0% to 90%) scenarios. In addition, we evaluated model sensitivity to changes in the reproductive success and survival of MYY fish.ResultThe simulations suggest that the average time to extirpation would be 14 years using MYY fish and 8 years for FYY fish, assuming 50% annual removal and 50% initial stocking of the wild Brook Trout abundance. Increasing removal and/or stocking levels resulted in reductions in extirpation times using either MYY or FYY fish. Sensitivity analyses indicated that the model was relatively insensitive to changes in the reproductive success and survival of MYY Brook Trout.ConclusionOur results suggest that either MYY or FYY fish can be used to eradicate wild Brook Trout populations, but FYY fish are likely the most efficient means of eradicating nonnative Brook Trout in streams typical of western North America.

Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0

This is a study on advanced inventory management approaches in the mechanical industry using data-driven models, accompanied by Power BI analytics. Drawing insights from broad collaboration with relevant industrial expertise at different organizational scales, our research work addresses the intricacies and challenges in maintaining optimal inventory levels concerning growing industries and operational risks. In this global mechanical firm, we analyzed historical data using descriptive and predictive analytics to understand the trends of the past and to forecast future inventory needs. Descriptive analytics provided base insights about the present status of inventory, while predictive analytics helped proactive management of stock levels classified into on-hand and critical level categories for greater efficiency in strategies related to planning at the coordination end with its suppliers and mitigating risks. Core to our methodology will be the implementation of an automated data-driven machine-learning approach that brings minimal intervention from humans to solve some of the most critical problems in inventory management. One major contribution this study adds is a major product: a Power BI dashboard that visualizes critical part numbers falling below threshold values and recognizes suppliers with historical shipment shortages. Armed with this intuitive dashboard, supplier-coordinating engineers are better placed to take proactive action on inventory shortages, greatly improving operational efficiency in supply chain resilience.

Mathematical approaches to fisheries quota management: ensuring sustainable practices in U.S. commercial fishing

Fisheries quota management plays a critical role in ensuring the sustainability of commercial fishing practices, particularly in the United States, where overfishing and resource depletion pose significant challenges to marine ecosystems. This review explores the mathematical approaches utilized to manage fisheries quotas effectively and sustainably. The paper examines key mathematical models, including population dynamics, bioeconomic models, game theory, and Catch-per-Unit-Effort (CPUE) models, highlighting their role in maintaining fish stock levels and regulating fishing activities. Through case studies from U.S. fisheries, such as the New England groundfish and Alaska pollock fisheries, the review assesses the effectiveness of these models in real-world applications. Additionally, it explores how advances in technology, such as satellite tracking, big data, and artificial intelligence, are enhancing the accuracy and adaptability of fisheries management models. The review concludes with insights into future directions for mathematical approaches in fisheries management, emphasizing the need for continuous innovation to support sustainable commercial fishing practices. By integrating advanced mathematical techniques and data-driven models, this paper aims to provide a pathway for policymakers and fisheries managers to achieve long-term sustainability in U.S. commercial fisheries.

Global projections of plastic use, end-of-life fate and potential changes in consumption, reduction, recycling and replacement with bioplastics to 2050

Excessive production, indiscriminate consumption, and improper disposal of plastics have led to plastic pollution and its hazardous environmental effects. Various approaches to tackle the challenges of reducing the plastic footprint have been developed and applied, such as the production of alternative materials (design for recycling), the production and use of biodegradable plastic and plastics from power-to-X, and the development of recycling approaches. This study proposes an optimisation strategy based on regression to evaluate and predict plastic use and end-of-life fate in the future based on historical trends. The mathematical model is formulated and correlations based on functions of time are developed and optimised by minimising the sum of squared residuals. The plastic quantities up to the year 2050 are projected based on historical trends analysis, and for improved sustainability, projections are additionally based on intervention analyses. The results show that the global use of plastics is expected to increase from 464 Mt in 2020 up to 884 Mt in 2050, with up to 4725 Mt of plastics accumulated in stock in 2050 (from the year 2000). Compared to other available forecasts, a slightly lower level of plastic use and stock are obtained. The intervention analysis estimates a range of global plastics' consumption between 594 Mt and 1018 Mt in 2050 by taking into account its different increment rates (between −1 % and 2.65 %). In the packaging sector, the implementation of reduction targets (15 % reduction in 2040 compared to 2018) could lead to a 27.3 % decrease in plastic use in 2050 as compared to 2018, while achieving recycling targets (55 % in 2030) would recycle >75 % of plastic packaging in 2050. The partial substitution of fossil-based plastics with bioplastics (polyethylene) will require significant land area, between 0.2 × 106 km2 for obtaining switchgrass and up to around 1.0 × 106 km2 for obtaining forest residue (annual yields of 58.15 t/ha and 3.5 t/ha) in 2050. The intervention analysis shows that proactive policies can mitigate sustainability challenges, however achieving broader sustainability goals also requires reduction of footprints related to energy production and virgin plastic production, the production of bio-based plastics, and the full implementation of recycling initiatives.

Data-Driven Shelf Stocking: Robustness and Vertical-Location Effect

In this article, we study a shelf-stock allocation problem in a data-driven setting where the demand distribution is unknown. The retailer needs to decide which commodities to place on each level of the shelf (location decision), and determine the stock level for each selected commodity at the shelf (stock decision) that can be adjusted based on updated demand prediction, under constraints on allocation and capacity, so as to maximize total expected profit. Key issues of the problem include demand endogeneity on vertical-location effect and the demand ambiguity that may arise from the ever shorter life cycles of commodities and other complicated determinants. We address the problem in a rolling-horizon fashion and develop a two-phase data-driven robust optimization model leveraging a decision-dependent Wasserstein metric that incorporates time-series demand forecasts and captures vertical-location effect under demand ambiguity. We derive tractable solution schemes for the model, in which the phase-I model determines the optimal location decision by solving one instance of mixed-integer linear program, while the phase-II model admits an analytical solution for updating the optimal stock. Furthermore, we discuss the sensitivity implications on how the ambiguity aversion impacts the optimal stock, and prove the consistency of the model solution under regularity condition on the underlying demand process that well justifies the asymptotic performance of the proposed data-driven approach. Finally, we extend our model to incorporate substitution effect, and conduct sufficient numerical experiments with real-life data that demonstrate the performance of our model.

Inventory Management Practices and Their Impact on Operational Efficiency: A Case Study of MGSSK Sugars Ltd.

In today’s highly competitive business environment, effective inventory management is vital for maintaining operational efficiency and ensuring long-term sustainability. Proper inventory control reduces excess stock and prevents stock outs, leading to cost reduction and improved cash flow. By utilizing precise forecasting and strategic replenishment methods, companies can enhance their adaptability to market changes and sustain a competitive advantage. This research provides a thorough examination of inventory management metrics spanning five years. The primary objective of the study is to evaluate the effectiveness of inventory management. The data was primarily collected from secondary sources, including five years of company inventory records. To analyze the data, the ratio analysis method was applied. The results demonstrate notable improvements in the company’s operational efficiency. Key performance indicators include a steady rise in the inventory turnover ratio and a reduction in average stock levels, showcasing effective inventory control aligned with increasing demand and minimizing surplus stock. Moreover, the study reveals a decreasing trend in inventory as a proportion of current assets, indicating a strategic shift away from relying on inventory for revenue generation. Additionally, variations in raw material values, coupled with consistent material consumption, suggest fluctuations in raw material usage efficiency. Furthermore, the relationship between the raw material turnover ratio and inventory holding period highlights the company’s proficiency in optimizing inventory management. The declining inventory conversion period indicates successful transformation of inventory into sales, boosting cash flow and operational efficiency. This improvement is attributed to advanced forecasting and stock replenishment strategies.

Economic policy uncertainty and the predictability of stock returns: impact of China in Asia

PurposeMotivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.Design/methodology/approachWe estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.FindingsApplying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.Research limitations/implicationsOur analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.Originality/valueThe findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

Due date-oriented picker routing, an efficient exact solution algorithm, and its application to pick-from-store omnichannel retailing

The advent of e-commerce and omnichannel retailing has sparked renewed interest in picker routing in warehouses. This paper presents two significant methodological advances in this well-established field. First, it is a well-known fact that the parallel-aisle layout of warehouses, as opposed to general graphs, allows for polynomial-time solutions of the Traveling Salesman Problem. We show that the parallel-aisle structure can also be exploited when pickers are tasked with visiting storage positions associated with specific due dates. We establish that picker routing in warehouses, subject to soft due date constraints, is a binary NP-hard problem. We also present an exact branch-and-bound algorithm with pseudo-polynomial time complexity. This algorithm effectively solves instances with up to 60 picking positions and five cross aisles within a few seconds while guaranteeing optimality. Second, for even larger pick lists, we demonstrate the successful integration of our algorithm into a real-time framework. This approach allows us to avoid extended solution times that would otherwise delay the picker’s departure, without compromising the quality of the solution. To illustrate the practical relevance of these two methodological innovations, we apply our routing algorithm to the context of pick-from-store omnichannel retailing. By assigning due dates to critical products, we significantly reduce stockout occurrences for online customers. These stockouts occur when the stock level, initially deemed sufficient to confirm an online order, is depleted by walk-in customers before the picker reaches the relevant shelf.

Improvement of Drug Stock Management Using ABC-VED Analysis in a University Hospital

Objective: Hospitals allocate approximately 35% of their budgets to pharmaceutical and material stocks in order to provide uninterrupted health services. For this reason, hospital managers spend a lot of time and effort in order to keep a minimum level of stock so as not to disrupt health service production and thus to better manage the limited resources of the enterprise. The aim of this study is to group the drugs according to their financial value and vital importance. In this way, it is aimed to perform stock management in the least costly, errorfree and effective way. Method: In the study, data corresponding to one-year (2021) consumption of a total of 1327 items of drugs were analyzed. The total cost is approximately 83 million ₺. In this study, an ABC-VED cross-analysis was performed on the one-year drug consumption data of a university hospital with 853 beds in order to provide an example of a more controlled inventory structure. Results: According to the results, it is seen that the ratio of drugs in the first category, that is, drugs that are both high cost and vital for the patient, to the total drug item is 38%. The cost of these drugs constitutes 87% of the total cost. Conclusion: Taking into account the classifications put forward in the study, strict monitoring of a small number of drugs and regular weekly counts will make drug stock management much easier for managers.

Potential environmental implications of sandbar afforestation: Insights from ecosystem restoration initiatives in a sandbar of Brahmaputra River Assam, India

Plantation in riverine sandbars offers an excellent opportunity for restoration of ecosystem with a vast potential for enhancing carbon stock. Afforestation on barren islands is challenging task; however, Padmashree Jadav Payeng has single-handedly transformed a sandbar of the river Brahmaputra into a forested landscape in India’s northeastern state of Assam. Such inspiring initiative needs more attention in terms of scientific assessment to quantify ecosystem value and services that can be enriched through such activities. This study uses remote sensing data viz., Landsat4–5TM and Sentinel 2 A data to provide a detailed information on spatio-temporal variability of land use land cover of study region from 1990 to 2021. It aims to conduct a scientific and systematic assessment of biophysical changes that have occurred in the sandbars, understand the status of afforestation, and evaluate the current levels of aboveground biomass (AGB), belowground biomass (BGB), and carbon stock in the selected sandbars. The spatial distribution of AGB, BGB and carbon stock is derived using linear regression model between SAR backscatter and field-based AGB. The results demonstrate that forest cover increased by 31.92 % and sandbar area is reduced by 26.87 % from 1990 to 2021. Spatially derived AGB ranges from 9 to 1295.89 Mg per ha, BGB from 2.35 to 290.50 Mg per ha and carbon stock from 6.18 to 763.34 Mg per ha. Ficus religiosa exhibits a high biomass, ranging from 564.7 Mg per ha to 994.7 Mg per ha, and a high carbon stock, ranging from 8 to 557.30 Mg per ha, attributed to its larger diameter at breast height (DBH). Moreover, phytosociological assessment was conducted for the studied forest, which reveal a total tree species richness of 79. The forest exhibits a total tree density of 395 individuals per hectare. Different biodiversity indices provide a comprehensive understanding of species composition; where results show a Shannon diversity index of 2.81, a Simpson's index of 0.08, a Menhinick's richness index of 2.7, and a Margalef's richness index of 5.26. The article provides detailed information on change in forest cover and present status of AGB, BGB and Carbon stock, providing evidence-based narratives on ecosystem restoration. The initiative by a single person has created a forested landscape and habitat for a large number of wild animals, contributing to better carbon stock and a healthier ecosystem. With regard to the current debate of carbon market, the study suggests the need of instrumentalization of carbon credits for such restoration activities to encourage increase in carbon stock. It also concludes that in-depth research should be taken up on long-term effectiveness of such plantation activities and the potential for scaling up these initiatives for ecosystem restoration in view of climate change mitigation and sustainable forest development.

Dashboard Pemantauan Inventori Pada Mixue Erlangga Simpang Lima Berbasis Web

This research aims to develop a web-based inventory monitoring dashboard for Mixue Erlangga Simpang Lima. The dashboard is designed to provide ease in monitoring and managing stock in real-time, thereby enhancing operational efficiency and reducing the risk of stockouts or overstocking. The research methodology includes needs analysis, system design, development, and application testing. The result of this research is a dashboard that can display inventory data visually and interactively, and provide notifications when stock levels approach predefined minimum or maximum limits. The implementation of this dashboard is expected to assist management in making quicker and more accurate decisions regarding inventory management. User evaluations indicate that this dashboard successfully improves efficiency and effectiveness in monitoring and managing stock at Mixue Erlangga Simpang Lima.

Optimal fisheries management and the response to price changes

The literature on how price changes affect optimally managed fisheries is mostly concerned with how fish stocks and harvest rates are affected in steady state. There is little published on how prices affect optimal harvest rates at stock levels outside of steady state. Here we show the effect of an unanticipated and permanent price increase. It is shown that in a model of a pure schooling fishery, if the stock is below the steady state, it is optimal to harvest less if the price goes up and vice versa. It is also shown that in a model with stock dependent harvest costs, the optimal response to a price increase is to reduce the harvest rate for low stock levels even if the optimal harvest rate increases close to the steady state. Empirical relevance is demonstrated by illustrating the theoretical results in an estimated model.

Long-term biochar and soil organic carbon stability – Evidence from field experiments in Germany

Organic soil amendments (OSA) with long residence times, such as biochar, have a high potential for soil organic carbon (SOC) sequestration. The highly aromatic structure of biochar reduces microbial decomposition and explains the slow turnover of biochar, indicating long persistence in soils and thus potential SOC sequestration. However, there is a lack of data on biochar-induced SOC sequestration in the long-term and under field conditions. We sampled two long-term field experiments in Germany, where biochar was applied 12 and 14 years ago. Both locations differ in soil characteristics and in the types and amounts of biochar and other OSA. Amendments containing compost and 31.5 Mg ha−1 of biochar on a loamy soil led to a SOC stock increase of 38 Mg ha−1 after OSA addition. The additional increase is due to non-biochar co-amendments such as compost or biogas digestate. After eleven years, this SOC stock increase was still stable. High biochar amount additions of 40 Mg ha−1 combined with biogas digestate, compost or synthetic fertilizer on a sandy soil led to an increase of SOC stocks of 61 Mg ha−1; 38 Mg ha−1 dissipated in the following four years most likely due to lacking physical protection of the coarse soil material, and after nine years the biochar-amended soils showed only slightly higher SOC stocks (+7 Mg ha−1) than the control. Black carbon stocks on the same soil increased in the short- and mid-term and decreased almost to the original stock levels after nine years. Our results indicate that in most cases the long-term effect on SOC and black carbon stocks is controlled by biochar quality and amount, while non-biochar co-amendments can be neglected. This study proves that SOC sequestration through the use of biochar is possible, especially in loamy soils, while non-biochar OSA cannot sequester SOC in the long term.

Intelligent machines as information and communication technology and their influence on sustainable marketing practices for beneficial impact on business performance: A conceptual framework

Intelligent machines are the machines or devices that make use of artificial intelligence and robotics technologies. It has the ability to accomplish a specific task in the presence of uncertainty and variability in its operating environment. Certainly, it can be used to support information and communication technology to streamline the creation, collection, processing, transmission, and storage of information for sustainable marketing practices. The flawless application of sustainable marketing practices results in beneficial impacts on business performance. In fact, the issue of unsustainable marketing practices can be effectively managed by intelligent machines. Therefore, this study is undertaken to uncover how intelligent machines can influence sustainable marketing practices for beneficial impacts on retailers’ business performance by proposing a unique conceptual framework. The theoretical contributions discuss two techno-sustainable marketing applications. First, intelligent machines improve incremental innovation. This allows retailers to balance technology risk with sustainable marketing and lower the cost of innovations. Second, intelligent machines increase business efficiency by automating sustainable marketing practices. This allows retailers to efficiently manage the inventory, improve fulfilment efficiency, and optimise stock levels. The managerial implications discuss two goals of sustainable marketing practices. First, it can attract sustainability-minded customers who support the retail business for their own well-being. Second, it builds a strong sustainable brand reputation that can lower the price sensitivity.

Evaluating effect of Inventory controls on financial performance of Small and Medium Enterprises in Eldoret Town, Kenya

Inventory management is essential for maintaining optimal stock levels and minimizing costs, which ultimately affects a firm's overall financial performance. This study focuses on the impact of inventory controls on the financial performance of small and medium enterprises (SMEs) in Eldoret town. Inventory controls are measured through various dimensions, such as inventory levels, inspection, auditing, and management systems. Previous research has shown that effective inventory management can increase a firm's profitability by reducing unnecessary stock holding costs and avoiding stockouts. To investigate this further, a descriptive research design was employed, and data was collected from 171 SME owners/managers through self-administered questionnaires. Descriptive statistics and regression analysis were used to analyze the data. The results revealed a significant positive relationship between inventory controls and the financial performance of SMEs. Specifically, the inventory controls explained 11.5% of the variance in financial performance. These findings suggest that SMEs that implement efficient inventory management practices tend to experience better financial outcomes. Therefore, it can be concluded that effective inventory control mechanisms are crucial for enhancing the competitiveness and growth of SMEs. Consequently, this study recommends that SME owners implement robust inventory control systems to maximize their financial performance.

End-to-end solution for automatic beverage stock detection in supermarkets based on image processing and convolutional neural networks

This study addresses the challenge of detecting and identifying stock shortages in large warehouses through an advanced algorithm that integrates image processing and artificial intelligence techniques. Presently, many companies contend with the limitations of manual inventory management, such as susceptibility to errors, slow inventory actualizations, and consequent adverse economic effects. In contrast to solutions based on robotics, the proposed approach continuously monitors shelves throughout warehouse aisles using several fixed cameras, each connected to a single-board computer that processes the acquired images, identifies stock levels using deep learning, and updates a centralized database with stock analysis results. The algorithmic process begins with an image validation step based on a convolutional neural network to ensure obstacle-free images of the shelves. Subsequently, an application-specific YOLOv2 detector trained via transfer learning identifies product types captured in the images and estimates their stock levels. The proposed solution not only reduces the need for manual intervention and operational costs but also drastically enhances inventory supervision efficiency. The fully implemented system achieves an average accuracy of over 98 %, surpassing the human visual inspection performance. The proposed solution also incorporates the aspect of user-friendliness through a developed mobile application. This application connects to the centralized database, allowing inventory supervisors to receive alerts when the stock of a product falls below a user-configured threshold. This technological integration within a centralized system signifies a substantial advancement in inventory management, offering prompt responses to product scarcity situations and optimizing warehouse operational efficiency.

A bioeconomic model for a multispecies small-scale fishery system

A fishery encompasses various interconnected systems, including ecological, socioeconomic, and governing systems. Managing fisheries requires the simultaneous consideration of all these systems, making it a challenging endeavor. To address these challenges, fisheries bioeconomic models have emerged as a crucial tool. They are particularly valuable in the context of small-scale fisheries, which are often complex, overlooked and poorly understood. Thus, this paper presents a dynamic multispecies and multigear bioeconomic model that can illuminate the ecological, economic, and social dimensions of small-scale fisheries under different management scenarios. The model was applied to a small-scale fisheries system in Southern Brazil that has as a notable feature a cooperative fishing behavior between dolphins and fishers. Three scenarios were explored: the base scenario (status quo), the optimal management scenario, and the constrained optimal management scenario. The model outputs demonstrated a clear tradeoff between labour effort, species conservation, and economic rent. Shifting from the base to an optimal management scenario would result in a labour employment reduction within the system but concurrently yield higher stock levels, economic rent, and wages. These results illustrate how our model can explore critical management scenarios across the multiple dimensions of fisheries systems. In essence, this research offers a novel contribution in the form of a bioeconomic model tailored for small-scale fisheries involving multiple species.

Implementing an Agile-Driven Centralized System: A Key Strategy for Optimizing Stock Administration Efficiency

The stock administration program architecture involves user roles, such as managers, administrators, and staff, interacting with the system's components to access and process data. The system's development relies on hardware and application elements, including databases, front-end and back-end systems, code editing tools, and local servers. A use-case approach organizes stock administration, with diagrams showing users' interactions with the system. Administrators can manage stock by adding, modifying, or deleting items, while the system provides reports for analysis and decision-making. Staff members can view inventory, adjust stock levels, and receive alerts for low stock. Managers oversee stock requests, analyze reports, and manage staff accounts. A SUS questionnaire was administered to assess usability, revealing user perceptions based on ease of use, efficiency, and satisfaction. Comparing SUS scores across user roles helps identify usability challenges. High scores indicate that the system is user-friendly and effective, while lower scores suggest areas for improvement through training, redesign, or feature enhancements. Users reported high usability for ease of use, with support requirements scoring second highest. Staff ratings highlighted areas for improvement, such as stock level notifications, which can be addressed through redesign or training to enhance system usability.

Analysis of Efficient Drug Supply Control at Muhammadiyah Siti Aminah Bumiayu Hospital Using the MMSL Method (Minimum-Maximum Stock Level) Period March – May 2023

Medicine supplies that are too large or small will make hospitals sick loss. These losses can be in the form of drug supply costs enlarges and becomes disturbed smooth running of health services in hospitals. Objective study This For know influence method MMSL to supply drug in a way efficient in Installation Pharmacy RSU Muhammadiyah Siti Aminah Bumiayu. This research design uses quasi-experimental without control, by applying the method studied, namely minimum-maximum stock levels. Sampling using technique which can be obtained from Retrospective data for October 2022 – December 2022 and method application prospectively, namely March 2023 - May 2023 and carry out strategies repair use method Hanlon. All data drug results analysis A B C became the sample for this research. Analysis of inventory value data was carried out, stock value out, dea d stock and ITOR before and after application of the method using test statistics Wilcoxon Signed Rank Test. The inventory value before applying the MMSL method was IDR 289,756,421, and after applying control using the MMSL method it was IDR 184,725,126 , with a value of p=0.002<0.05. The implementation of drug control using the MMSL method shows that there is an influence on the value of drug supplies in the pharmaceutical installation of RSU Muhammadiyah Siti Aminah Bumiayu.