Efficiency Of Inventory Management Research Articles

Resilience in Cyclicality: Impact of Inventory Management Efficiency on Operational Profitability

ABSTRACTThe cyclical and non‐cyclical nature of the consumer goods industry presents unique dynamics that require discernment in analyzing working capital (WC). These firms often deal with considerable inventory levels that create costs and liquidity challenges, particularly during economic downturns. This paper investigates the impact of inventory days (InvD) on operating margin (OM) in both demand‐stable and demand‐sensitive businesses. The empirical analysis employs system GMM regression with a sample of publicly traded US consumer goods firms from 2010 to 2022. The findings reveal an inverted U‐shaped relationship between InvD and OM among cyclical companies, indicating that these firms require a comparatively lower optimal inventory level (108 days) to maximize profitability at 3%. The results highlight the importance of strategic inventory management (IM) under varying economic and demand conditions and balancing the risks of stock‐outs against the costs of overstocking to optimize profitability. This paper contributes to the literature on the impact of inventory dynamics on operating profit in stable and volatile markets. It estimates the optimal inventory level that enhances profitability and predicts the optimal inventory dynamics for maximum profit.

Revolutionizing Inventory Management: The Role of Data Mining in Industry 4.0

In the era of Industry 4.0, with the rapid development and application of technologies such as the Internet of Things (IoT), Big Data and Artificial Intelligence (AI), the manufacturing industry is undergoing unprecedented changes. In this context, data mining technology has become integral to inventory management practices in all industries. This study examines the profound impact of data mining on inventory management efficiency. By leveraging advanced analytics and machine learning algorithms, data mining enables organizations to accurately forecast demand, optimize inventory levels, and improve supply chain transparency. Through real-world case studies and comprehensive analysis, this research highlights how data mining techniques, such as central object-based clustering algorithms, can be successfully applied to optimize material classification, warehouse space utilization and operational efficiency. In addition, this study explores the broader applications of data mining beyond inventory management, including marketing, financial risk management, healthcare, transportation, social media, and cybersecurity. Overall, this study provides valuable insights into how data mining can reshape inventory management practices and drive business growth in the digital age.

Inventory Management System Using Economic Order Quantity And Reorder Point

The inventory management system for car workshops is still using Excel, which makes it inefficient and inaccurate for complex stock calculations. The solution is to design an inventory application that uses the Economic Order Quantity and Reorder Point methods. The application will provide timely stock replenishment information and optimize order quantities, increasing inventory management efficiency and accuracy. The Software Development Life Cycle used in this study is Rapid Application Development, a process emphasizing short development cycles. User Acceptance Test results show that users received the inventory management system very well, with an average satisfaction level of 98.44% for Admins and 97% for general users, indicating high satisfaction with the system's features and functionality.

Efficiency of Inventory in Thai Hospitals: Comparing Traditional and Vendor-Managed Inventory Systems

Background: Traditional procurement and price negotiation methods in public hospitals in Thailand involve group purchasing agreements and bulk orders, posing challenges to improving inventory management efficiency. The vendor-managed inventory (VMI) model is a promising alternative for enhancing hospital performance, especially during crises. This study aimed to assess the potential cost savings in implementing VMI in a large general hospital in Thailand. Methods: A simulation modeling approach was used to compare the current inventory system with three VMI models: VMI1, focused on improving inventory turnover rate (ITR); VMI2, emphasized frequent replenishment with a 1-month supply; and VMI3, eliminated safety stock. Results: The results demonstrated significant cost savings, with potential reductions in total inventory management expenses. Specifically, VMI1 improved ITR from 6.31 to 7.76, reducing average inventory by 36% and cutting management costs by 40%. VMI2, with an ITR of 12.80, reduced inventory by 44% and saved 47% in management costs, while VMI3 achieved a 70% reduction in inventory and a 69% saving in management costs. Conclusions: This study highlights the VMI’s transformative potential in hospital inventory management, demonstrating significant cost savings. However, in the public sector, the feasibility of procurement regulations requires further exploration.

Research on Innovation Strategy of Supply Chain Management of Agricultural Enterprises Under the Background of Big Data

With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.

Hospital Pharmaceutical Inventory Control in In-Vitro Fertilization Services Using Minimum-Maximum Stock Level

Type C "X" Specialized Hospital in Semarang experienced in vitro fertilization (IVF) and embryology laboratory consumable medical supplies (CMS) stockouts in early 2023, delaying medical care. Minimum-maximum stock level (MMSL) is an alternative pharmaceutical inventory procurement method. Compared to the conventional method, this study examined how well the MMSL method controls IVF medications and embryology laboratory CMS at Hospital “X” Department of Pharmacy. This study is a quantitative, secondary data-based, quasi-experimental, non-equivalent study without a control group. The study sampled all IVF medications and embryology laboratory CMS data from October to December 2023 at Hospital “X” with consistent supply. MMSL was implemented prospectively in January – March 2024. Twelve (12) IVF medications and fifty-six (56) embryology laboratory CMS were studied. Efficiency analysis included inventory value, inventory turnover ratio (ITOR), and stockout value; effectiveness analysis included stockout incidences. Statistics were tested using Wilcoxon signed-rank and McNemar tests. MMSL implementation led to increased inventory value of IVF medications (from IDR21,514,537.00 to IDR49,635,116.00, p=0.040, p<0.05), decreased inventory value of embryology laboratory CMS (from IDR117,421,582.00 to IDR105,318,804.00, p=0.005, p<0.05), and increased ITOR of embryology laboratory CMS (from 0.82 to 0.84 times, p=0.040, p<0.05). MMSL significantly improved IVF medications and embryology laboratory CMS inventory management efficiency.

IoT-based inventory monitoring system for SMEs

Industry 4.0's pervasive digital ideas have completely changed how businesses operate. A lot of big businesses have switched to digital systems. However, due to a lack of funding and digital infrastructure, SME sector digitalization issues still exist. Inventory management is one of the many manual methods that many SMEs still have to rely on, which makes it prone to error. To manage inventories more accurately and effectively, manual systems must be changed. The goal of this project is to improve inventory management efficiency by creating a dependable and affordable automated system. A cloud database is connected to the system to provide quick inventory monitoring on warehouse shelves. Prototyping and statistical testing are used in the study technique to evaluate the system's dependability. Using an Internet of Things-based single-board computer, the research creates an inventory monitoring system. With a daily implementation cost of IDR 642.92, the system is deemed cost-effective based on inventory monitoring results on warehouse shelves. Because the system's performance reached a Cronbach's alpha value greater than 0.8, the inventory management system used in the study is regarded as dependable for application in real-world systems to increase inventory management accuracy

Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method

Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data. This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.

Enhancing Inventory Management through Safety-Stock Strategies—A Case Study

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

Rancang Bangun Sistem Inventory (Studi Kasus: UD. Asia Pratama Pekanbaru)

UD Asia Pratama Pekanbaru, a trading company in the building industry, is facing problems in managing inventory which is still manual, causing inefficiencies and inaccurate data. This research aims to design and implement an efficient and effective web-based inventory management system. The system development method uses the Waterfall model, including requirements analysis, design, implementation and testing. Data was collected through interviews with employees and analysis of documents related to inventory. The research results show that the system developed can increase inventory management efficiency, with real-time stock management features, recording of incoming and outgoing goods, and accurate reporting. User testing and evaluation shows that the system is acceptable and meets UD Asia Pratama's needs in overcoming inventory problems.

Analisis Pengaruh Forecasting Demand Terhadap Efisiensi Manajemen Persediaan

This study analyzes the impact of demand forecasting on inventory management efficiency, focusing on the implementation of the ARIMA method in the context of a manufacturing company in Indonesia. Using a quantitative case study approach, the research explores the relationship between forecasting accuracy and key inventory performance metrics. Results indicate that improved forecasting accuracy significantly enhances inventory efficiency, with the ARIMA method outperforming traditional methods. The implementation of ARIMA resulted in a 36.8% increase in Inventory Turnover Ratio and a 53.3% decrease in stockout rate. Time series analysis revealed a long-term improvement trend in inventory efficiency with significant seasonal components. This research provides valuable insights for practitioners and academics on the critical role of demand forecasting in optimizing inventory management and the potential of the ARIMA method in improving companies' operational efficiency.

The practice of customs logistics in the export and import of petroleum products

In Russia, oil production and refining are the leading industries. The customs logistics of the export and import of petroleum products has its own characteristics and reserves for improvement. In order to comply with the rule of law and increase the efficiency of foreign trade activities in relation to petroleum products, it is necessary to strictly control all stages of the logistics process. Effective customs logistics implies an integrated approach to solving problems from the perspective of identifying costs and reducing them in conjunction with all logistics links – from supply to distribution. In the course of the study, the specifics of the approach to customs operations in relation to petroleum products as an object of export and import logistics flows within the framework of their movement by various modes of transport were studied. The importance of the development of customs logistics for the development of foreign economic activity and the oil industry is also determined. The result of the study was the directions proposed by the authors to improve the efficiency of customs logistics in the export and import of petroleum products. They include: reduction of losses of oil and petroleum products during transportation; organization of the logistics process, taking into account the analysis of the best practices of oil processing enterprises; improvement of the efficiency of inventory management; optimization of transport routes and choice of vehicles; introduction of modern information technologies into logistics processes.

Exploring Green Inventory Management through Periodic Review Inventory Systems—A Comprehensive Literature Review and Directions for Future Research

With the increasing environmental concerns and legislative pressures, the focus on incorporating ecologically sustainable practices into inventory management systems has grown, leading to the emergence of green inventory management. However, this field is not without its challenges, with numerous conflicting real-world constraints and goals. A comprehensive literature review targeting green inventory management operating under a periodic review inventory system was conducted to identify research gaps and potential directions for future research. Despite the growing interest in the field, this review highlighted the scarcity of relevant studies. Out of the 1272 papers reviewed, only 16 studies, or 1.3%, met the criteria for exploring periodic review inventory systems while simultaneously considering environmental and economic aspects. These studies were further analyzed in detail and categorized according to key classification criteria. The future research directions highlighted the need for additional studies on periodic review inventory systems operating under stochastic market demand in the context of green supply chain management. The standardization of emission calculation methodologies was also emphasized as a crucial step towards aligning inventory management practices with the aim of increasing inventory management efficiency and the related improvement in the environmental performance of supply chains.

Increasing the efficiency of inventory management in the field of trade based on integrated BI and ERP technologies

The article explores the possibilities of improving the efficiency of inventory management in the trade sector by integrating business intelligence (BI) and enterprise resource planning (ERP) technologies. The article considers a model of combination of these technologies, systemises their advantages and limitations related to determination of economic efficiency of such integration on the basis of analysis of practical cases in three aspects related to information technology, economics and management. The study uses the methods of systematisation, analysis, synthesis, grouping, integrated ABC-XYZ analysis and case analysis. Further research is aimed at generalising practical cases in order to determine the prospects for the introduction of integrated technologies in the activities of trade enterprises. Particular attention is paid to optimising inventory management and increasing productivity through the use of business analysis tools and automation of operational planning and accounting processes. The practical value of the study lies in developing ways to rationalise inventory management, which will allow companies to formulate a procurement plan; plan an appropriate budget of income and expenses to avoid unforeseen costs and insolvency crises; improve cost management processes by drawing attention to the goods that account for the largest share of costs, as well as those characterised by high unpredictability of demand. This information is important for internal financial accounting, budgeting, operational and strategic planning.

Inventory Management Optimization: Balancing Cost and Service Levels

Efficient management of inventory plays a role, in the success of businesses across sectors. Finding the equilibrium between keeping inventory costs low and meeting customer needs is a challenge that requires constant exploration and creativity. This study delves into the complexities of optimizing inventory management focusing on strategies that effectively handle the trade offs between minimizing expenses and fulfilling customer expectations. By delving into an analysis of existing literature this study consolidates approaches, models and best practices implemented by companies to enhance the efficiency of inventory management. It thoroughly examines aspects such as improving demand prediction accuracy reducing lead times optimizing safety stock levels and integrating supply chains. Additionally it explores how advanced technologies like intelligence data analytics and automation are transforming inventory management processes.Moreover this research delves into how industry landscapes, market dynamics and business goals influence inventory management tactics. Through real world examples and empirical data analysis it offers insights, on how organizations can tailor their strategies to meet needs while balancing the objectives of cost control and service level improvement. In the end this study adds to what we know about improving inventory management by providing advice and tips for companies to gain lasting edges in todays ever changing market. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

Development of a Smart Shopping Cart for Enhanced Retail Experience

Abstract: This research introduces a novel approach to revolutionize the shopping experience through the development of a Smart Shopping Cart. The system integrates technologies such as RFID, weight sensors, and real-time inventory management to provide instantaneous updates and an engaging user interface. The project’s primary objectives include enhancing inventory management efficiency and establishing a seamless RFID connection. Future enhancements envisage increased automation, enhanced security measures, personalization options, and connectivity improvements. The system employs RFID, weight sensors, a Tkinter GUI, and MySQL for database management. Key performance indicators include response time, database efficiency, RFID scanning speed, and overall system throughput. By amalgamating these technologies, this project aspires to transform the retail sector, augmenting customer satisfaction and operational efficiency

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

The Influence of Inventory Costs on Product Sales: An Analysis in the Global Sneaker Market

The pandemic has greatly impacted the global athletic shoe market, leading to supply chain instability and inventory management challenges. Taking the global athletic shoe market as an example, this study delves into inventory management issues from three aspects: store operations, price changes, and supply chain modules, and analyzes Nike's data in China and Vietnam in detail. The epidemic led to challenges, such as declining market demand, production disruptions, and supply chain problems, which affected the company's profitability. By analyzing inventory management strategies, this study proposes two key recommendations to help firms cope with uncertainty and improve inventory management efficiency. First, firms are advised to maintain moderate inventories to respond to changes in market demand flexibly. Second, inventory levels should be increased moderately through marketing programs and promotional activities to improve product competitiveness. These recommendations are intended to help athletic footwear and apparel companies meet the challenges of reducing inventory costs, increasing product sales, and ultimately achieving inventory management success during the epidemic.

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Gambaran Penjualan dan Investası Obat Bermerek Apotek “X” Kelurahan Karang Anyar Kota Tarakan dengan Metode ABC Tahun 2022

Effective drug management requires careful inventory control to prevent problems with shortages or excess stock which can be detrimental to both the pharmacy and consumer service. This research aims to identify the percentage of drugs based on sales and investment transactions using the ABC method at Pharmacy "X" and grouping drugs into categories A, B, or C. This research is retrospective and uses a quantitative descriptive approach. The research object is data on the use of branded drugs at Pharmacy "X" for the period January- December 2022. The sample consists of 897 drug items throughout 2022, and samples were taken using the total sampling method. Data analysis involves marking numbers, item names, types, quantities, units, total prices, and trade groups, which are then sorted based on sales and investment transactions from largest to smallest. ABC analysis of this study shows that Group A dominates drug sales, while Group C has the largest number of drug items despite low movement. Close monitoring is required in Group A to prevent losses due to expired drugs. Pharmacy "X" needs to consider reducing inventory in Group C to optimize expenditure and storage space, thereby reducing the risk of expired medicines and increasing inventory management efficiency.