Inventory Management Research Articles

A study on inventory control system for a supply chain using Markov decision processes

This paper examines the application of Markov Decision Processes (MDPs) for controlling supply chain inventories. MDPs effectively simulate decision-making problems related to uncertainty, facilitating the determination of optimal inventory policies. The MDP framework addresses various inventory management challenges, including demand fluctuations, lead times, and holding costs. The study investigates the modeling of inventory management as a Markov decision process, detailing the states, actions, and transitions within the MDP model, along with their respective advantages and disadvantages. The research employs policy and value iteration techniques to evaluate and optimize inventory management policies. The paper assesses the proposed MDP-based inventory control system through simulations that utilize supply chain data, aiming to identify optimal policies using the MDP model. A comparative analysis of the MDP approach against conventional inventory management methods is conducted to demonstrate its efficacy in reducing costs and enhancing service levels. Additionally, the paper proposes the incorporation of multiple commodities, multi-echelon supply chains, and perishability considerations into the MDP model. The findings indicate that MDPs facilitate improved optimization of inventory policies, cost reductions, and enhanced customer service, thereby emphasizing the significance of computational complexity and the necessity for accurate data. This research provides a comprehensive investigation into the role of MDPs within inventory control systems, contributing valuable insights to the field of supply chain management. Ultimately, this study lays the groundwork for advancements in MDP-based inventory control methodologies.

Cloud-Enabled Innovation in Retail: Transforming Inventory Management and Demand Forecasting

This comprehensive article explores the transformative impact of cloud computing technologies on the retail industry, examining core platform solutions, implementation strategies, and future trends. The article investigates how cloud integration has revolutionized inventory management, demand forecasting, and operational efficiency across the retail sector. Through detailed case studies of major retailers, the research demonstrates the substantial benefits of cloud adoption in areas such as real-time data processing, predictive analytics, and supply chain optimization. The article encompasses technical considerations, best practices, and emerging technologies, providing insights into successful implementation strategies and operational guidelines. The article further evaluates the integration opportunities and standardization initiatives shaping the future of retail cloud computing, highlighting the critical role of artificial intelligence, blockchain, IoT, and advanced analytics in driving industry innovation.

AI-DRIVEN BRIDGING LOCAL SELLERS AND GLOBAL BUYERS

This project aims to create an accessible digital platform tailored for small-scale businesses, enabling them to engage in cross-border e-commerce and global trade with minimal complexity. By leveraging the MERN stack (MongoDB, Express, React, Node.js), the platform provides a responsive and scalable solution that connects local producers with a global customer base. Small-scale industries often face barriers in reaching international markets, from supply chain complexities to payment processing challenges.This platform addresses these obstacles by simplifying logistics, incorporating multi-currency support, and securing transactions through integrated payment gateways.The core features, such as real-time order tracking and streamlined supply chain management, empower small businesses to operate with the same efficiency as larger enterprises. Buyers can browse products from local producers, pay in their currency, and track shipments globally, making cross- border shopping more accessible. This project bridges the gap between local and international markets, fostering growth for small-scale industries and expanding their reach to a worldwide audience. By reducing the traditional costs and complications associated with global trade, this platform offers an efficient pathway for small businesses to scale their operations beyond local borders. Keywords: AI-Driven Solutions, Cross-Border E- Commerce, Small-Scale Industries, Global Buyers and Local Seller, MERN Stack, Inventory Management, Real-Time Order Tracking, Secure Payment Gateways, Logistics Simplification, Customer Engagement, Customer Trust and Satisfaction.

FUZZY PRODUCTION MODEL WITH FUZZY VALUED DEMAND WITH NONLINEAR SELLING PRICE, WARRANTY, AND LEVEL OF GREENNESS OF THE PRODUCT UNDER FUZZY DETERMINATION RATE

Many studies on manufacturing and inventory management for increasingly problematic commodities have focused on how quality, greenness, product warranties, and environmental concerns might be incorporated into industrial operations. However, comparatively few articles have integrated these two themes. To close this knowledge gap and advance practice, these two decaying object criteria must be included in the study. The following assumptions are used to create a warranty-based, sustainable production inventory model for green products. In this article, we look at how greenness, product assurances, and reducing carbon emissions technologies impact overall profit to assist those making decisions to make better choices regarding pricing and replenishment. Due to the consideration of fuzzy valued inventory parameters along with the objective function's nonlinearity and the presence of five decision variables, we have addressed this problem with the help of the Lingo 18 software along with the defuzzification technique. The mathematical formula is quantitatively demonstrated in one experiment, and a sensitivity analysis is performed to determine the effects of various factors on overall profit and management insights.

Smart topology detection using multi-armed bandit reinforcement learning method

ABSTRACT Inventory management in network infrastructures currently relies on static methods, leading to data consistency issues due to the difficulty of keeping static reports and lists up to date with the dynamic nature of real-world networks. An alternative approach is the use of automatically generated network topology diagrams, which provide a visual representation of the network’s structure and organization, depicting the configuration and interconnections of nodes and links. These diagrams serve purposes such as network planning, management, and security management. However, the creation of automatic network topology diagrams remains a challenge. This paper addresses this challenge by proposing a Reinforcement Learning-based approach that utilizes network traffic data to automatically generate network topology diagrams. The preliminary results demonstrate promising outcomes, enabling the automatic generation of efficient network topology diagrams for various purposes, even in the case of large-scale networks. These automatically created diagrams can further facilitate security analysis, planning, and optimization activities. By focusing on enhancing security outcomes and enabling robust network analysis, this research contributes to advancing the field of network management, particularly in the domain of automatic network visualization and comprehensive security analysis.

Automation in purchasing order through items classifications: a case study in Finland

ABSTRACT The aims of this study are to analyse the procurement process of a case company in Finland and to find ways to improve both the inventory level and purchasing efficiency. This study adopted both theoretical and empirical methodological approaches. In the theoretical section, it presented various models of modern purchasing and inventory management principles, whereas, in the empirical research, it is presented an implementation of an XYZ-analysis extension for the case company’s current ABC analysis. In this research, the case company’s year of worth of inventory requirements from six selected suppliers are analysed to create an ABC-XYZ matrix that is refined to a set of inventory management rules. The results from the ABC-XYZ analysis and the created matrix of rules, a proposal for an automated process with system suggestions is presented in this study. This automation system is based on the idea that low value items that have low number of fluctuations in their production demand can be automated to reduce the purchasing workforces. The study is concluded on the idea that the case company has the capabilities to implement an automated system basing on its current system capability, but it requires building new processes and purchasing or building a new supplier system to fulfill the proposed workflow.

Model Optimization and Dynamic Analysis of Inventory Management in Manufacturing Enterprises

This study investigates inventory management systems using a sample of listed manufacturing companies in China from 2019 to 2023. By constructing a static mathematical model, the impact of inventory management on corporate performance was empirically tested. Additionally, based on a classical inventory management dynamical model and considering inventory delay characteristics, a new class of two-dimensional inventory management systems was reconstructed. The system’s periodic and chaotic nonlinear characteristics were verified using 0-1 tests, bifurcation diagrams, Lyapunov exponents, and system eigenvalue plots. Furthermore, MATLAB simulations were employed to examine the effect of resource transfer rates on the nonlinear dynamic behavior of the inventory management system. The results from both static mathematical models and dynamical models provide a theoretical basis for inventory management and safety stock level predictions in the manufacturing industry.

The Implementation of AWS Cloud Technology to Enhance the Performance and Security of the Pharmacy Cashier Management System

This study examines the implementation of Amazon Web Services (AWS) in the MEKATEK pharmacy cashier management system to address the limitations of traditional systems, such as slow transaction processing, data loss risks, and challenges in handling transaction surges. The prototyping method was employed, involving user requirements analysis through interviews and observations, followed by iterative development of core features like inventory management, transactions, reporting, and data backups. Black box testing demonstrated a 100% success rate for core functionalities. Performance analysis recorded stable CPU utilisation below 5% under normal workloads and the ability to handle throughput up to 2532 packets/minute. System optimisation reduced AWS operational costs to IDR 150,000–160,000 per month. AWS implementation improved operational efficiency, strengthened data security through encryption and role-based access control, and minimised human errors. Initial user feedback indicated faster workflows, although adjustments are needed for users with limited technical backgrounds. This study recommends further development, including AI-based analytics and digital payment integration, to enhance MEKATEK’s functionality and competitiveness in the future.

The Role of Industry Concentration on Competitive Inventory-Management Behaviors: Evidence from U.S. Manufacturers

This research study investigates the potential effect of industry concentration on the rivalrous moves and counter moves made between leader and challenger firms relative to inventory management decisions. Theoretically, “the perennial gale of creative destruction” proposed by Schumpeter (1934) enables an examination of the competitive dynamics between firms. Mimetic isomorphism (as embedded within institutional theory, per DiMaggio & Powell (1983)) and signaling theory (Porter, 1980) have likewise been applied here to the presumed actions and reactions of firms relative to management and operational decisions. For their part, Ferrier, Smith & Grimm (1999)—in leveraging earlier research from Young, Smith & Grimm (1996)—offer evidence to indicate that higher industry concentrations may result in fewer rivalrous moves. In such concentrated environments, the dynamism of competitive rivalries may be lessened, thereby suppressing otherwise potentially aggressive moves and counter moves relative to inventory and other operational decisions.

ANÁLISE DE ESTRATÉGIAS PARA DISPONIBILIDADE DE MATERIAIS EM MANUTENÇÃO DE MÁQUINAS DE VIA: UMA REVISÃO SISTEMÁTICA

This systematic research addresses the critical importance of material availability for the effective maintenance of track machines, a fundamental element to ensure safety and efficiency in railway operations. The central problem investigated focuses on the challenges faced in provisioning these materials, including logistical issues, stock errors, and delivery delays, which can compromise timely and efficient maintenance. The aim of this study is to analyze innovative and effective strategies that can optimize the provisioning of materials necessary for track machine maintenance. To achieve this goal, a bibliographic research methodology was adopted, focusing on the qualitative analysis of 19 literatures published between 2018 and 2023. The results reveal a convergence among authors on the need to integrate strategic planning, advanced technologies such as artificial intelligence, and efficient inventory management to overcome provisioning challenges. Prescriptive maintenance strategies and the use of advanced diagnostic technologies are highlighted as key elements for more proactive and less reactive maintenance. The final considerations demonstrate that the research problem has been thoroughly explored, with the proposed objectives being achieved. For future research, evaluating the real impact of emerging technologies on the life cycle of railway maintenance is suggested, aiming to provide effective strategies for the continuous optimization of maintenance operations.

Integration and Optimization of Route Planning and Inventory Management in Supply Chain Transaction Systems: A Comprehensive Analysis

This comprehensive article analysis explores the integration and optimization of route planning and inventory management within supply chain transaction systems, examining their transformative impact across various industries. The article investigates the evolution of modern logistics operations, emphasizing the crucial role of advanced technologies, blockchain frameworks, and integrated systems in enhancing operational efficiency. Through a detailed examination of core components including algorithmic foundations, real-time tracking systems, demand forecasting mechanisms, and unified data architectures, this article presents a holistic view of contemporary supply chain optimization. The article analysis encompasses multiple sectors, including retail, e-commerce, and manufacturing, demonstrating how integrated systems have revolutionized traditional approaches to inventory management and route optimization. By exploring performance metrics, return on investment, and future trends, this article provides valuable insights into the implementation strategies and scalability considerations essential for modern supply chain systems. The article highlights the significance of cross-functional optimization and cloud-based solutions in achieving operational excellence while maintaining high levels of customer satisfaction and cost efficiency.

Smart Inventory Management System Using RFID Technology

The Smart Inventory Management System using RFID technology is a cutting-edge solution designed to streamline inventory processes, enhance accuracy, and improve operational efficiency. By integrating RFID (Radio Frequency Identification) technology, the system enables real-time tracking and management of inventory items, eliminating the need for manual data entry and reducing human errors. The system leverages RFID tags and readers to automate inventory identification, monitoring, and auditing. Key features include instant stock updates, reduced operational costs, enhanced security, and improved decision-making through data analytics. This innovative approach is particularly beneficial for industries such as retail, logistics, and manufacturing, where efficient inventory management is critical. The implementation of this system demonstrates the potential of RFID technology in revolutionizing inventory management practices and achieving greater productivity.

Harnessing Technology for Enhanced Efficiency in Supply Chain Operations

The international supply chain environment is quickly changing due to innovations that are reinventing not just the method companies operate. Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, And Automation are some technologies that change the supply chains with more efficiency, better decisions, and live views. The technologies simplify processes like inventory management, demand forecasting, order fulfillment, and logistics optimization, which can keep businesses ahead of the competition in a fast-paced global marketplace. In this paper, we look at how organizations use these technologies to drive operational excellence, entitlement, and responsiveness to customer demand. The report presents some case studies of the successful applications of these technologies and talks about what companies are facing when incorporating these new systems into their existing supply chain process. It also explores strategic best practices in addressing these challenges, beginning with defining clear goals, aligning technology, and ensuring their workforces are ready for change. Finally, the paper looks at the future for supply chain technology. With burgeoning trends such as hyper-automation (driven by AI and IoT), we can expect things to keep improving and improving supply chain performance in years to come. The results show that despite challenges, successfully implementing these technologies increases efficiency and reduces costs, improving customer satisfaction and providing firms with a sustainable comparative advantage. Keywords Supply Chain Management, Technology, Artificial Intelligence, Internet of Things, Blockchain, Automation, Operational Efficiency, Demand Forecasting, Logistics, Inventory Management, Hyper-Automation.

Comprehensive Smart Mess Management Web Platform

The Mess Management System is an innovative software solution designed to automate the manual process of managing mess-related activities such, inventory management, and billing. The system is developed using PHP as the programming language and MySQL as the database management system. The primary objective of this system is to reduce the workload on mess staff, increase efficiency, and reduce errors in the mess management process. This research paper focuses on the design and implementation of the Mess Management System and its impact on college hostel mess management. This is a cost- effective, efficient, and user-friendly software solution that automates the manual mess management process. The system can be customized to cater to the specific needs of different institutions and can significantly reduce the workload on mess staff. The system's scalability, flexibility, and ease of use make it a promising solution for college hostel mess management. However, further research can be carried out to address the system's limitations, such as the need for regular maintenance and the requirement of technical expertise.

Analisis Penerapan Metode Economic Order Quantity (EOQ) dalam Pengendalian Persediaan Produk

Supply chain management is a crucial aspect of a company's operations. This study aims to examine the application of the Economic Order Quantity (EOQ) method in managing finished product inventory at Evodis Aroma, a company engaged in the essential oil aromatherapy industry. Efficiency in supply chain management, particularly inventory management, is key for companies to meet customer demand while minimizing operational costs. The EOQ method is applied to determine the optimal quantity of products to order or produce in order to minimize storage and ordering costs. In addressing the challenges of demand fluctuations and storage constraints, the EOQ method is expected to optimize inventory and enhance operational efficiency at Evodis Aroma. The findings of this study indicate that the implementation of EOQ significantly contributes to reducing storage costs and improving product availability to meet market demand, thereby increasing the company's overall efficiency.

Enhancing supply chain resilience with recovery strategies under electricity shortages

Electricity shortages triggered by extreme events have profoundly disrupted supply chains (SCs) in recent years. As SC operations heavily depend on electricity, simultaneous uncertainties may occur in supply and production capacities, demand, and transportation capacity if electric trucks are used. To enhance SC resilience, it is imperative to combine new recovery strategies (i.e. alternative energy sources and alternative transportation vehicles (ATVs)) with traditional ones to restore SC performance. However, these strategies have not been investigated quantitatively. Besides, only partial probability distribution information of uncertainties may be available under electricity shortages. Thus, this work improves SC resilience from a worst-case perspective, where the moment information is known. For the problem, a bi-objective distributionally robust chance-constrained optimisation model is constructed to balance the total cost and the service level. Then, an effective ϵ-model-based constructive heuristic (ϵ-MBCH) is developed. Compared with the ϵ-constraint method, the efficiency of ϵ-MBCH is improved by 99.81%. Based on stress tests and sensitivity analyses on a real case, key managerial insights include: (i) the ATV strategy is indispensable under varying severities of electricity shortages; (ii) the inventory management strategy helps reduce reliance on power generators and diesel trucks; and (iii) mitigating demand variability is essential for reducing costs.

Investigations of ultraviolet laser patterning QR codes on printed circuit boards for inventory management

Investigations of ultraviolet laser patterning QR codes on printed circuit boards for inventory management

Analisis Penerapan Economic Order Quantity (EOQ) untuk Efisiensi Pengelolaam Bahan Baku

Inventory plays an important role in modern industry, affecting the Company's overall performance. Effective inventory management ensures smooth operations by avoiding material shortages, optimizing storage costs. And maintaining product quality. PT Eteris Prima Wiyasa, an essential oil manufacturer, currently manages its raw material inventory based on market demand, resulting in occasional shortages or overstocks. The Economic Order Quantity (EOQ) model provides a solution by optimizing ordering and storage costs to determine the optimal order quantity, reduce stockouts and lower inventory costs. This study aims to optimize raw material inventory at PT Eteris Prima Wiyasa using EOQ, overcoming problems related to inventory imbalances and supplier minimum order quantities.

Optimasi Proses Transaksi Jual Beli Bahan Pangan Rempah-Rempah melalui Sistem Informasi Pencatatan Berbasis Website pada Toko Stella Jaya

Information technology has become a crucial element in business management, especially in financial recording. Many small to medium-sized businesses, such as Toko Stella Jaya, still rely on manual systems that cause various problems, including data loss, recording errors, and low efficiency. Manual bookkeeping at Toko Stella Jaya results in inaccuracies in stock recording and difficulties in producing timely financial reports. To overcome these obstacles, a web-based system is needed that can automate the process of recording sales, making notes, inventory management, and preparing financial reports. The proposed system is equipped with supplier data management features, real-time sales recording, automatic note generation, and financial reports, which are expected to increase operational efficiency and simplify the bookkeeping management of Stella Jaya Store. This research aims to design a technological solution that not only reduces the risk of data loss, but also increases accuracy and speed in business financial management.

The Design of a Good Data Processing App Applying QR Code

Designing a good data processing application using QR Codes aims to increase efficiency and accuracy in data management. QR codes are a fast and easy technology for storing and scanning data, allowing users to quickly access information without entering data manually. This application is designed for various purposes, such as inventory, time and attendance management, and document tracking. The application design process includes user needs analysis, interface design, back-end development, and integration with QR code technology. The prototype method is used during the system development stage. The programming language used is a hypertext preprocessor with a MySQL database, and a framework is used to ensure that the application behaves as expected and can provide effective solutions in data processing. The results of this design are expected to significantly contribute to operational efficiency, reduce human error in data entry, and increase the speed of accessing and managing information. Apart from that, implementing QR codes in data processing applications is hoped to be widely used in various industries and business fields.