The main goal of this article is to depict the automation and digitization of the real production process and to describe an entire system for achieving complete product traceability based on a decentralized knowledge graph, blockchain, and Industry 4.0 technologies. Presented sys-tem architecture provides the possibility of interoperability between existing enterprise re-source planning systems and blockchain. Different traceability levels were accomplished in practice by using and combining certain Industry 4.0 technologies which led to full final product verification. At the same time, stated research undoubtedly illustrates the actual shift from a traditional production process to a digital one, which is firmly established on new technologies and the OriginTrail blockchain.

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.