Inventory Management Research Articles

Developing a web-based system to automate sales and inventory management procedures for small food enterprises is the main goal of "Data-Driven Point-of-Sale and Inventory System for Pastil sa Tabi: Integrating Sales Forecasting Algorithms with Predictive Analytics." The system, developed using PHP, MySQL, HTML, CSS, and JavaScript under the Waterfall SDLC approach, integrates transaction processing, inventory management, and sales forecasting to address inefficiencies caused by manual processes. It predicts product demand using forecasting algorithms, helping the business minimize waste, prevent shortages, and improve ingredient procurement. Fifty (50) respondents, including thirty (30) users and twenty (20) technical experts, evaluated the system using ISO/IEC 25010 software quality standards. They rated it highly for functionality and usability and suggested improvements in performance and reliability. The system improved operational speed, accuracy, and decision-making while providing real-time analytics on inventory and sales trends. Results show that both technical and user respondents agreed the system achieved its objectives by enhancing customer service, updating inventory reliably, and automating business processes efficiently. The system’s ability to record transactions and generate reports precisely improved accuracy and user satisfaction. Although reliability and performance efficiency were slightly lower, they remained favorable, highlighting the need for further optimization during high-traffic operations. The study concludes that incorporating automation and forecasting technologies in small food businesses like “Pastil sa Tabi” enhances decision-making and sustainability. Regular system updates, better multi-user optimization, and future features such as offline and mobile access are recommended. Expanding scalability for multi-branch use and adopting machine learning-based forecasting can further improve performance. Overall, the system demonstrates how digital transformation guided by quality standards enables small businesses to operate efficiently in a data-driven economy.

Abstract This study examines the impact of both random and anticipated disruptions on transportation costs within different stages of a downstream oil supply chain. Conducting a comprehensive literature review, a MILP model was developed to simulate a multifaceted refined oil supply chain, integrating refining and import facilities, storage depots, and customer demand nodes. The study unfolds in two phases: a deterministic model establishing a supply chain performance baseline, and a Monte Carlo simulation generating disruption scenarios. Results reveal increased transportation costs and significant flow modifications between entities. Imports of refined oil products surged to counter local production shortages, with increased use of cost-effective bulk cargo modes and a notable reliance on road transport to offset disrupted pipelines. The study highlights the substantial impact of disruptions on transportation costs, emphasizing diversified transportation methods where pipelines are constrained. Acknowledging study limitations focusing on a singular supply chain’s transport costs, it advocates for research on inventory management and alternate pipeline development to enhance supply chain resilience under disruption scenarios.

This project, titled “A Web-Based Data Driven Analytics System for Income and Operations Management Using Linear Regression for Modern Concept Prints,” was developed to address the inefficiencies of manual income tracking and operations management in small enterprises. Income management, which includes tracking revenue, managing expenses, and ensuring profitability, is crucial for financial planning and long-term sustainability. Operations management, which involves the efficient handling of day-to-day processes such as task delegation, inventory monitoring, and order fulfillment, is equally essential for business optimization. When these areas are not integrated or managed manually, it becomes difficult to control costs, optimize performance, and make informed decisions. This study follows an applied research approach, focusing on the development of a Web-Based System to solve real-world business challenges faced by Modern Concept Prints, a local printing business. The business had been relying on spreadsheets and verbal coordination for its operations, leading to frequent delays, inaccurate records, and limited forecasting capabilities. To address these issues, the researchers designed and implemented a centralized platform that automates key business processes, including Sales Monitoring, Inventory Management, and Task Tracking, while utilizing Linear Regression for Income Prediction based on historical data. The system also integrates Predictive Analytics to forecast future income, thus enhancing decision-making. The project followed the Spiral Model as its software development methodology, allowing for iterative development, continuous risk assessment, and frequent refinement of the system based on user feedback. Developed using PHP, MySQL, HTML, CSS, and JavaScript, the system offers a dynamic, user-friendly interface that supports real-time data analysis and visualization. Evaluation results, guided by ISO 25010 quality standards, showed high satisfaction among both technical and user respondents in terms of System Usability, Data Security, functionality, reliability, and security. The system significantly improved operational workflows, reduced manual errors, and enhanced financial planning through automated income prediction and sales monitoring. The project demonstrates how integrating automation, Predictive Analytics, Linear Regression, and business management can help small businesses optimize decision-making, productivity, and long-term sustainability. Future recommendations include adding accounting and payroll modules, mobile compatibility, and advanced forecasting algorithms to further enhance scalability and performance.

An Integrated Inventory and Supplier Management System Using SAS: A Framework for Supply Chain Optimization

In inventory management, business organizations gradually face challenges due to the complexities of managing perishable goods whose value diminishes over time. In such circumstances, interval’s bounds estimated business policy can be adopted to study a non-deterministic inventory model incorporating decay, preservation technology, and financial incentives, viz. advanced payments and fixed discounts. This study explores an interval Economic Order Quantity (EOQ) model incorporating advanced payment with discount options under preservation technology framework in interval environment. In this model, the demand rate is expressed as a convex combination of linear and power patterns of the selling price. The present model is formulated mathematically using interval differential equations and interval mathematics. Then, the corresponding interval-valued average profit of the model is obtained. In order to optimize the corresponding interval optimization problem, C-U optimization technique is developed. Employing the C-U optimization technique, the said interval optimization problem is converted into crisp optimization problems. Then, these problems are solved numerically by Wolfrom MATHEMATICA-11.0 software and validated with the help of two numerical examples. Finally, sensitivity analyses have been performed to study the impact of known inventory parameters on optimal policy.

This study explores the implementation of lean manufacturing principles, specifically focusing on line balancing, in the automotive components industry to address productivity challenges, such as inventory management, scrap reduction, and lengthy die exchange times. By optimizing the assembly line, the study aimed to reduce manpower and improve efficiency. The methodology involved calculating process cycle times, identifying nonvalue-added activities, and using tools like the standardized work combination table (SWCT), value stream mapping (VSM), and single minute exchange of die (SMED) to streamline operations. Key findings revealed that the number of operators required per part was significantly reduced, leading to a theoretical reduction of 18 operators per day. This optimization not only minimized cycle time variations and die changeover times but also enhanced overall production efficiency, resulting in annual cost savings of ₹28,08,000. The analysis demonstrated substantial improvements in line efficiency by addressing bottlenecks, balancing workloads, and implementing lean tools, such as 5S, Kaizen, and visual management. The introduction of a more balanced and efficient production process reduced nonvalue-added activities and idle times, with line efficiency (LBR) improving on balanced lines (e.g. CFT Outer U129: 73%-93%) and process efficiency (PE) in the stores, line segment increasing from 37.89% to 65.45% ([Formula: see text]72.7% relative), alongside a 60 min reduction in stock staging time at the process control (PC) zone. The case study underscores the potential of lean methodologies to significantly enhance productivity, reduce costs, and improve resource utilization in medium-scale manufacturing industries. These findings provide a valuable framework for other industries facing similar challenges, highlighting the importance of continuous improvement and sustainability in maintaining a competitive edge. The novelty of this study lies in the empirical integration of lean tools to address specific constraints within a mid-scale automotive supply chain, resulting in quantifiable manpower savings and cost reduction.

It streamlines the content creation process by helping writers, marketers, and content creators by coming up with ideas, writing articles, and offering advice on how to make the text better. As a virtual tutor, ChatGPT makes learning more accessible and individualized by assisting students in understanding difficult subjects, responding to inquiries, and offering clarifications. Better communication between various linguistic groups can be facilitated by its assistance with text translation. ChatGPT increases software development productivity by enabling developers to troubleshoot code, obtain explanations of programming concepts, and even generate code snippets. As a personal assistant, ChatGPT can help users organize information, manage tasks, and set reminders, all of which increase individual productivity. All things considered, ChatGPT improves productivity, innovation, and accessibility in a variety of domains, making it a useful tool in both personal and professional settings. It streamlines the content creation process by helping writers, marketers, and content creators by coming up with ideas, writing articles, and offering advice on how to make the text better. As a virtual tutor, ChatGPT makes learning more accessible and individualized by assisting students in understanding difficult subjects, responding to inquiries, and offering clarifications. Better communication between various linguistic groups can be facilitated by its assistance with text translation. ChatGPT increases software development productivity by enabling developers to troubleshoot code, obtain explanations of programming concepts, and even generate code snippets. As a personal assistant, ChatGPT can help users organize information, manage tasks, and set reminders, all of which increase individual productivity. All things considered, ChatGPT improves productivity, innovation, and accessibility in a variety of domains, making it a useful tool in both personal and professional settings. AI systems analyze user behavior and preferences to tailor user experiences. Recommendation systems used by social media, e-commerce sites, and streaming services frequently exhibit this. Based on past data, AI can predict future trends, assisting businesses in making well-informed decisions. This is especially helpful in fields like risk assessment and inventory management. AI improves healthcare outcomes and operational efficiency by helping with disease diagnosis, treatment plan personalization, and patient data management. Overall, AI is a transformative technology that continues to evolve, impacting numerous aspects of daily life and industry practices.

In modern laboratories, automation and safety rely heavily on accurately detecting and identifying laboratory equipment. To address this need, we introduce a comprehensive and well-curated dataset designed to detect 25 commonly used chemistry lab apparatuses. The dataset comprises 4,599 JPG-format images captured under diverse real-world conditions, including varying lighting, backgrounds, angles, overlaps, and distances - factors that enhance the robustness and generalizability of model training. It is split into training (70%), validation (20%), and testing (10%) subsets. This resource is particularly valuable for developing laboratory automation systems, with potential applications in safety monitoring, inventory management, and real-time tracking of lab tools. We evaluated the dataset using seven state-of-the-art object detection models, all achieving impressive performance with mAP@50 scores exceeding 0.9: RF-DETR (0.992), YOLOv11 (0.987), YOLOv9 (0.986), YOLOv5 (0.985), YOLOv8 (0.983), YOLOv7 (0.947), and YOLOv12 (0.92). To the best of our knowledge, this is the most extensive publicly available dataset of its kind, covering 25 categories of chemistry laboratory apparatuses and establishing a strong foundation for future research in laboratory automation.

In this research article, the Six Sigma DMAIC methodology was applied in conjunction with the Warehouse 4.0 concept to optimize the distribution process in a limited environment derived from the restructuring and continuous improvement in the management and logistics of a distribution center. For each stage of DMAIC, specific goals and concrete actions were established, starting from the mapping of the native process together with Python software, adding new functions that enabled the optimization of inventory and stock management processes. The methodology employed consisted of on-site observation based on the principle of risk detection and resource optimization. Based on the results obtained, the process was standardized, an increase in the sigma level, an improvement in user interaction, the standard search time was reduced, and a considerable reduction of errors was achieved when locating items and verifying stock levels

Abstract - This project focuses on designing and developing an Inventory Management System (IMS) using RESTful APIs. The main goal is to simplify inventory operations by automating key tasks and giving users complete control—without using any artificial intelligence (AI) features. Instead of relying on predictive algorithms, the system uses clear rule-based automation to handle stock updates, shipment tracking, and reporting. This makes the solution easy to understand, reliable, and simple to maintain. It is especially suitable for small to medium-sized businesses that value transparency, accuracy, and straightforward troubleshooting in their daily inventory workflows..

In competitive markets, companies often lack access to their rivals’ sales, costs, and strategies. Can they still learn to make optimal decisions? In a new study, Li and Mehrotra show that the answer is yes. Their research demonstrates that even without competitor data, firms can adaptively learn to make near-optimal choices using only their own operational information. More strikingly, when all players follow such self-driven learning, the entire market converges to a Nash equilibrium—the stable state predicted by economic theory—without explicit coordination. The study establishes theoretical guarantees for both convergence rates and regret performance and illustrates the framework in inventory management and dynamic pricing settings. These findings provide a foundation for data-driven decision making in competitive and uncertain environments and offer insights into how markets naturally self-organize.

This research aims to analyze and optimize the partnership system (distributors, agents, and resellers) at Ramli Collection, a local Muslim men's clothing brand, in an effort to increase sales. Based on data from January to August 2025, sales through the partnership channel showed a fluctuating and declining trend, indicating that the current partnership system is not yet optimal. The core problems identified are the lack of standards in the selection of new partners, resulting in payment defaults and contract terminations, and the absence of a structured mentoring program for partners, which has led to a decline in sales performance. This study uses a qualitative approach with data collection techniques in the form of observation, interviews, and documentation. The results of the analysis show that a comprehensive partnership system improvement is needed. The proposed solutions include: (1) Developing a Structured Partnership System by establishing strict partner selection criteria (surveys, interviews, and preliminary assessments) and formulating binding cooperation agreements; (2) Standardizing Partner Assistance through regular training programs (marketing and inventory management) and digital promotional material support; and (3) Strengthening Brand Awareness through strategic collaboration with public figures relevant to the brand image. Optimizing this partnership system is expected to overcome payment default issues, consistently improve partner sales performance, and ultimately contribute significantly to increasing Ramli Collection's total sales. Keywords: Partnership, Optimization, Sales Improvement, Marketing Strategy.

Ensuring road safety is a critical concern in urban planning and transportation management. Current approaches to monitoring roadway assets rely on periodic, labor-intensive nighttime field surveys and lack an efficient way to exploit crowd-sourced street view imagery for real-time insight, leaving agencies without timely information on infrastructure such as street luminaires that are vital for nighttime driving safety. This study explores the application of machine learning (ML) to enhance the development and maintenance of robust roadway and roadside inventories. Utilizing image segmentation techniques applied to Google Maps street view images, the research focuses on detecting and localizing street luminaires, which are crucial for nighttime driving safety. The detection module achieved high precision (≥0.98) and recall (≥0.90), demonstrating its effectiveness. However, the localization module faced challenges in complex scenarios, such as varied camera positions and uncommon objects like steel bridges. Factors influencing performance included the camera’s lane position and the limitations of third-party image databases. Recommendations for addressing these challenges include the use of video logs for accurate location recording, fixed survey cameras for precise distance calculations, and consistent survey protocols. The findings underscore the potential of ML to improve roadway inventory management. Future research should focus on expanding the training dataset, optimizing hyperparameters, and conducting comprehensive tests in diverse environments. By implementing these recommendations, transportation agencies can leverage ML to create more accurate and efficient roadway inventories, enhancing road safety and reliability.

Introduction: Religiosity is a core component of palliative care and whose roles in patients with cancer were described. However, there is a paucity of studies in cardiovascular disease (CVD). Hypothesis: Religiosity can alleviate the symptoms of CVD patients in end-of-life (EOL). Aims: To clarify the influence of religiosity on physical symptoms, quality of care (QOC) and quality of death (QOD) in EOL care for CVD patients. Methods: We conducted a nationwide cross-sectional mortality follow-back survey using a questionnaire for bereaved caregivers of patients who had died of CVD in Japan. Measurements included Memorial Symptom Assessment Scale (MSAS), Care Evaluation Scale (CES) and Good Death Inventory (GDI). We assessed each outcome by the presence of religiosity. Results: Of the questionnaire distributed to 15,047 descendants, we finally analyzed 4,436 responses about religiosity. 795 (17.9%) descendants answered that patients had been supported by religion 1 month before death. The religion-supported group was older (Religion: 88.9±8.7 vs. No Religion: 86.7±9.4, P<0.001), more frequent in females (70.9% vs. 60.3%, P<0.001), less frequently stayed at hospital 1 months before death (15.8% vs. 24.0%, P<0.001), and significantly more likely to report having fewer symptoms in MSAS sub-score and being more satisfied in CES and GDI sub-score. The results were the same except for “Lack of energy” and “Nausea” in MSAS after adjusting the confounders. The total mean score of CES and GDI was also significantly higher in the religion-supported group (69.2±21.9 vs. 63.6±24.2, P<0.001, 65.4±17.0 vs. 53.7±22.1, P<0.001, respectively). Conclusion: Religiosity could benefit CVD patients in EOL care by reducing physical symptoms and improving QOC and QOD.

The growing prominence of environmental, social, and governance (ESG) considerations has introduced new challenges for firms worldwide. While ESG practices are often framed as long-term drivers of competitiveness, uncertainty surrounding their regulatory requirements has created significant operational risks. The primary objective of this study is to examine how ESG uncertainty (ESG) affects inventory management in listed firms. The study analyzed data from Chinese A-share listed companies over the period 2010 to 2024. A series of econometric estimations, including fixed effect models, two-stage least squares (2SLS), and system GMM, were employed to ensure the robustness of the results and to address issues of heteroscedasticity, endogeneity, and dynamic effects. The empirical results consistently revealed that ESG uncertainty exerted a significant negative effect on inventory management. Firms facing greater unpredictability in ESG-related requirements experienced disruptions in supply chain coordination, difficulties in demand forecasting, and inefficiencies in inventory turnover. Beyond this, larger firms and those with higher environmental expenditures exhibited weaker inventory efficiency, while debt ratio, cost of capital, and firm performance were positively associated with improved inventory outcomes. For corporate managers, the study highlighted the importance of embedding sustainability considerations into inventory strategies and adopting flexible procurement systems, predictive analytics, and stronger governance mechanisms. The findings underscored the broader societal need for clarity and stability in ESG regulations. For this, reducing policy unpredictability could enable firms to align sustainability commitments with operational efficiency, thereby improving competitiveness while minimizing waste and resource misallocation. This study was among the first to empirically establish the link between ESG uncertainty and inventory management, bridging the gap between sustainability research and operational efficiency.

ABSTRACT This study explores a two-period pricing and inventory control model, employing both competitive and cooperative policies with a reference price effect for two retailers. With increasing consumer demand for fresh products and the perishable nature of such items, effective pricing and inventory management are critical for maximizing profitability. The proposed model examines how retailers can determine optimal selling prices and replenishment times while minimizing holding, ordering, and deterioration costs. A game-theoretic approach is employed to derive equilibrium pricing and replenishment strategies, considering the reference price effect on consumer behavior. The results indicate that an increase in the deterioration rate reduces retailer profits by negatively impacting the demand function. Moreover, adjustments to the reference price directly influence the profitability of each retailer.

Abstract Global food waste is a pressing issue, with around 1.3 billion tons wasted annually worldwide. While estimates may vary, the scale of this problem is undeniable, impacting the environment, society, economies, and global food security. Restaurants, facing rising food costs, industry competition, and environmentally conscious consumers, are under growing pressure to tackle food waste effectively. In Romania, vegetables, baked goods, and fruits are among the most wasted items, accounting for 13–17% of food waste. This study investigates the root causes of fruit and vegetable waste in restaurants and explores potential solutions. The research addresses three exploratory questions: “How do restaurant professionals perceive the meaning, types, and extent of fruit and vegetable waste?”; “What factors do they identify as drivers of fruit and vegetable waste in the food service sector?”; and “What strategies do they propose for reducing fruit and vegetable waste?” A qualitative approach was employed, consisting of in-depth interviews with restaurant employees. The results show that inefficiencies in handling and poor communication, both internally and with external partners, are seen as the main causes of fruit and vegetable waste in restaurants. Proposed solutions include better inventory management, portion control, and staff training. These findings call on chefs and managers to critically reassess their operational practices and ensure alignment with existing legislation. By providing a systematic framework of causes and potential solutions, this study offers practical and actionable guidance for reducing food waste within the restaurant industry.

Optimizing economic inventory management under trade-credit policies with payment delays, learning effects, and demand dynamics

Additive manufacturing (AM) has transformed the present-day supply chains with on-demand manufacturing, adaptive design, and digital inventory management. The distributed and heterogeneous nature of AM networks, however, induces inherent problems with stable product quality across distributed networks. Reactive and inspection-based quality control practices common with traditional quality control prove insufficient to handle variability in processes, material variability, and machine variability characteristic of AM networks. This paper proposes a conceptual framework of AI-augmented predictive quality control (PQC) applicable to additive manufacturing supply chains. The framework uncovers multi-faceted components: in-situ monitoring-based real-time data capture, AI-based analytics for defect prediction and anomaly recognition, decision support systems for adaptive intervention, and closed-loop continuous feedback facilities aided with digital twins. With the help of machine learning, deep learning, and reinforcement learning-based strategies, predictive frameworks are able to forecast defects, reduce scrap to a minimum, and transform supply chains with increased resiliency. The paper also elaborates on the theoretical benefit of AI-augmented PQC, including improved traceability, economy of costs, and increased congruity with just-in-time logistics. Data heterogeneity, scalability, cybersecurity, and workers' adaptability are also paramount challenges discussed in the paper. The future research directions are also enumerated in terms of hybrid AI-physics models, standardizable datasets, integration with blockchain, and human-AI teaming. This research enlists the transformative potential of AI-augmented PQC in making AM supply chains more reliable and viable.

Dynamic programming in inventory management: A review