This paper explores cost-effective solutions for automated guided vehicle (AGV) through the design and implementation of a low-cost, hoverboard-based line-following AGV tailored for textile manufacturing environments, specifically within sewing plants. The designed AGV leverages the capability of a commercial hoverboard as its mobility platform, significantly reducing development costs while maintaining effective operational performance. Utilizing affordable sensors such as infrared line detectors and ultrasonic sensors, the AGV autonomously navigates pre-defined pathways marked on the factory floor. Its primary function is transporting materials such as fabric bundles and partially or finished products between workstations, addressing common logistical challenges in dynamic and labor-intensive textile production settings. The system is designed for easy integration with both existing plant layouts and information and communication environment, requiring minimal infrastructural changes. Field testing demonstrated the AGV’s reliability, maneuverability, and responsiveness in real-world sewing plant conditions. The proposed solution underscores the potential of retrofitting existing consumer electronics for industrial automation, offering a scalable and economically viable alternative for small- to medium-sized textile enterprises seeking to enhance productivity and workflow efficiency.

This paper presents MZAP, a mobile application designed to digitalize basketball match tracking and generate secure, searchable endgame reports. Used by the Basketball League of Serbia, MZAP creates tamper-proof digitally signed records stored as password-protected PDFs with unique UUIDs, digital signatures, and QR codes. Each report is accompanied by a JSON file containing match data, enabling efficient validation through hashed checksums and facilitating data extraction and searchability. The system supports both online and offline modes, bilingual interfaces, mobile and tablet use, and includes features such as WiFi-based monitoring, physical printing, and various sharing options. The solution aims to reduce officials’ working time and increase data accuracy by minimizing human error through structural and UI-level validation methods and real-time monitoring by multiple observers during games. As part of the MZAP software suite, MZAP Converter is under development to support the digitization of legacy paper-based reports using custom CRNN neural networks to optically recognize and digitize historical paper-based reports, bringing them to the same standard as newly created digital ones. The paper also reflects on the broader impact of digital transformation within the Basketball League of Serbia.

The rapid development of Industry 4.0 brings significant benefits to intralogistics through increased flexibility, interoperability, and real-time responsiveness. However, the growing complexity of industrial environments creates challenges for logistics process adaptation and the integration of emerging technologies such as voice-driven control of heterogeneous robotic and IoT systems. Open-source solutions offer a promising approach by enabling vendor independence and cost-effective deployment. This paper investigates the use of the OPIL cyber–physical middleware, developed within the Horizon 2020 L4MS initiative, combined with convolutional neural networks for speech denoising and keyword recognition, to enable robust voice-controlled intralogistics in demanding noisy industrial environments. A voice-controlled logistics system was designed, deployed, and empirically evaluated on the OPIL platform. The results confirm the technical feasibility of OPIL as an open and modular alternative to proprietary Industry 4.0 platforms. They further demonstrate the feasibility of integrating AI-based keyword spotting and voice interaction into environments using an open middleware architecture. The study also identifies key limitations, including reduced performance for non-native speakers and mild speech over-suppression at high signal-to-noise ratios. To support reproducibility and external validation, the complete network configurations, training procedure, and deployment data are provided.