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"Inventory control Automation"
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RFID Security
RFID is a method of remotely storing and receiving data using devices called RFID tags. RFID tags can be small adhesive stickers containing antennas that receive and respond to transmissions from RFID transmitters. RFID tags are used to identify and track everything from food, dogs, beer kegs to library books. RFID tags use a standard that has already been hacked by several researchers. This book discusses the motives for someone wanting to hack an RFID system and shows how to protect systems.
eBook
The Design of a Vision-Assisted Dynamic Antenna Positioning Radio Frequency Identification-Based Inventory Robot Utilizing a 3-Degree-of-Freedom Manipulator
In contemporary warehouse logistics, the demand for efficient and precise inventory management is increasingly critical, yet traditional Radio Frequency Identification (RFID)-based systems often falter due to static antenna configurations that limit tag detection efficacy in complex environments with diverse object arrangements. Addressing this challenge, we introduce an advanced RFID-based inventory robot that integrates a 3-degree-of-freedom (3DOF) manipulator with vision-assisted dynamic antenna positioning to optimize tag detection performance. This autonomous system leverages a pretrained You Only Look Once (YOLO) model to detect objects in real time, employing forward and inverse kinematics to dynamically orient the RFID antenna toward identified items. The manipulator subsequently executes a tailored circular scanning motion, ensuring comprehensive coverage of each object’s surface and maximizing RFID tag readability. To evaluate the system’s efficacy, we conducted a comparative analysis of three scanning strategies: (1) a conventional fixed antenna approach, (2) a predefined path strategy with preprogrammed manipulator movements, and (3) our proposed vision-assisted dynamic positioning method. Experimental results, derived from controlled laboratory tests and Gazebo-based simulations, unequivocally demonstrate the superiority of the dynamic positioning approach. This method achieved detection rates of up to 98.0% across varied shelf heights and spatial distributions, significantly outperforming the fixed antenna (21.6%) and predefined path (88.5%) strategies, particularly in multitiered and cluttered settings. Furthermore, the approach balances energy efficiency, consuming 22.1 Wh per mission—marginally higher than the fixed antenna (18.2 Wh) but 9.8% less than predefined paths (24.5 Wh). By overcoming the limitations of static and preprogrammed systems, our robot offers a scalable, adaptable solution poised to elevate warehouse automation in the era of Industry 4.0.
Journal Article
Automation of Life Cycle Assessment—A Critical Review of Developments in the Field of Life Cycle Inventory Analysis
The collection of reliable data is an important and time-consuming part of the life cycle inventory (LCI) phase. Automation of individual steps can help to obtain a higher volume of or more realistic data. The aim of this paper is to survey the current state of automation potential in the scientific literature published between 2008 and 2021, with a focus on LCI in the area of process engineering. The results show that automation was most frequently found in the context of process simulation (via interfaces between software), for LCI database usage (e.g., via using ontologies for linking data) and molecular structure models (via machine learning processes such as artificial neural networks), which were also the categories where the highest level of maturity of the models was reached. No further usage could be observed in the areas of automation techniques for exploiting plant data, scientific literature, process calculation, stoichiometry and proxy data. The open science practice of sharing programming codes, software or other newly created resources was only followed in 20% of cases, uncertainty evaluation was only included in 10 out of 30 papers and only 30% of the developed methods were used in further publication, always including at least one of the first authors. For these reasons, we recommend encouraging exchange in the LCA community and in interdisciplinary settings to foster long-term sustainable development of new automation methodologies supporting data generation.
Journal Article
When I use a word . . . Avoiding drug stock-outs
A medicine stock-out is defined by the World Health Organization (WHO) as “any time when, at a defined moment in a given inventory, a needed medicine item is not in stock and orders or prescriptions cannot be filled.” The WHO has published a set of indicators for use in optimising the management of pharmaceutical stocks, in order to avoid stock-outs and the related but separate problem of overstocking. There is some evidence that use of the WHO’s indicators can help avoid these problems.
Journal Article
Hybrid Fuzzy and Data-Driven Robust Optimization for Resilience and Sustainable Health Care Supply Chain with Vendor-Managed Inventory Approach
One of the problems that government managers deal with are medical inventory management in COVID-19 conditions. Based on this situation, the best strategy for managing and reducing inventory costs can be Vendor-Managed Inventory (VMI) policy in the recent decade. Therefore, a hybrid fuzzy and data-driven robust optimization for Resilience and Sustainable Health Care Supply Chain (RSHCSC) with VMI approach is appropriate for improving the inventory management system and tackling uncertainty and disruption in this situation. Three RSHCSC models are suggested using hybrid fuzzy and data-driven robust optimization with a stochastic programming approach. The first model is average and mean absolute function, the second model is Conditional Value at Risk (CVaR), the third model is Minimax model, and the final model is the traditional inventory model. Each of the proposed models has advantages and disadvantages that depend on the conservative level of decision-maker. Sensitivity analysis is done on essential parameters like fuzzy cut, confidence level, robust and resilience coefficient, and size models. The results show that increasing fuzzy cut, confidence level, robustification coefficient, resiliency coefficient, and CVaR confidence level amount of costs grows. The Minimax function is suitable for conservative decision-makers.
Journal Article
Control Systems: Theory and Applications
In recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of advanced methods of control theory with focus on its practical implementation in various fields of human activity such as space control, robotics, control applications in marine systems, control processes in agriculture and food production. Control Systems: Theory and Applications consists of selected best papers which were presented at XXIV International conference on automatic control “Automatics 2017” (September 13–15, 2017, Kyiv, Ukraine) organized by Ukrainian Association on Automatic Control (National member organization of IFAC – International Federation on Automatic Control) and National University of Life and Environmental Sciences of Ukraine. More than 120 presentations where discussed at the conference, with participation of the scientists from the numerous countries. The book is divided into two main parts, a first on Theory of Automatic Control (5 chapters) and the second on Control Systems Applications (8 chapters). The selected chapters provide an overview of challenges in the area of control systems design, modeling, engineering and implementation and the approaches and techniques that relevant research groups within this area are employing to try to resolve these. This book on advanced methods of control theory and successful cases in the practical implementation is ideal for personnel in modern technological processes automation and SCADA systems, robotics, space and marine industries as well as academic staff and master/research students in computerized control systems, automatized and computer-integrated systems, electrical and mechanical engineering.
eBook
Scalable and Affordable IoT-based Inventory Control with Real-Time Monitoring for Small and Medium Enterprises
Small and medium enterprises (SMEs) often struggle to adopt advanced inventory management systems due to high implementation costs and infrastructure complexity—barriers that are especially challenging in the context of Industry 4.0. This paper presents a scalable and affordable IoT-based stock monitoring and control system designed for small and medium enterprises. The proposed system integrates low-cost microcontrollers with ultrasonic sensors, enabling real-time stock tracking while reducing hardware expenses and complexity. Unlike existing solutions, it leverages a modular architecture for seamless scalability across different inventory sizes and environments. The system is validated through compliance with the Industry 4.0 Maturity Index and the ISA-95 standard, demonstrating its suitability for digital transformation in SME operations. Performance evaluation shows an accuracy rate exceeding 98% and response times under 10 seconds, ensuring reliable operation under varying environmental conditions. A comparative cost analysis highlights significant savings compared to conventional automated inventory systems. This approach provides an accessible entry point for SMEs seeking to enhance inventory visibility, operational efficiency, and readiness for Industry 4.0 integration.
Journal Article
Multi-Scale Attention Networks with Feature Refinement for Medical Item Classification in Intelligent Healthcare Systems
The increasing adoption of artificial intelligence (AI) in intelligent healthcare systems has elevated the demand for robust medical imaging and vision-based inventory solutions. For an intelligent healthcare inventory system, accurate recognition and classification of medical items, including medicines and emergency supplies, are crucial for ensuring inventory integrity and timely access to life-saving resources. This study presents a hybrid deep learning framework, EfficientDet-BiFormer-ResNet, that integrates three specialized components: EfficientDet’s Bidirectional Feature Pyramid Network (BiFPN) for scalable multi-scale object detection, BiFormer’s bi-level routing attention for context-aware spatial refinement, and ResNet-18 enhanced with triplet loss and Online Hard Negative Mining (OHNM) for fine-grained classification. The model was trained and validated on a custom healthcare inventory dataset comprising over 5000 images collected under diverse lighting, occlusion, and arrangement conditions. Quantitative evaluations demonstrated that the proposed system achieved a mean average precision (mAP@0.5:0.95) of 83.2% and a top-1 classification accuracy of 94.7%, outperforming conventional models such as YOLO, SSD, and Mask R-CNN. The framework excelled in recognizing visually similar, occluded, and small-scale medical items. This work advances real-time medical item detection in healthcare by providing an AI-enabled, clinically relevant vision system for medical inventory management.
Journal Article
Data-Intensive Inventory Forecasting with Artificial Intelligence Models for Cross-Border E-Commerce Service Automation
Building an adaptative, flexible, resilient, and reliable inventory management system provides a reliable supply of cross-border e-commerce commodities, enhances supply chain members with a flow of products, fulfills ever-changing customer requirements, and enables e-commerce service automation. This study uses an e-commerce company as a case study to collect intensive inventory data. The key process of the AI approach for an intensive data forecasting framework is constructed. The study shows that the AI model’s optimization process needs to be combined with the problems of specific companies and information for analysis and optimization. The study provides optimization suggestions and highlights the key processes of the AI-predicting inventory model. The XGBoost method demonstrates the best performance in terms of accuracy (RMSE = 46.64%) and reasonable computation time (9 min 13 s). This research can be generalized and used as a useful basis for further implementing algorithms in other e-commerce enterprises. In doing so, this study highlights the current trend of logistics 4.0 solutions via the adoption of robust data-intensive inventory forecasting with artificial intelligence models for cross-border e-commerce service automation. As expected, the research findings improve the alleviation of the bullwhip impact and sustainable supply chain development. E-commerce enterprises may provide a better plan for their inventory management so as to minimize excess inventory or stock-outs, and improve their sales strategies and promotional and marketing activities.
Journal Article