Intelligent Real-Time Kanban Automation Using Ultra-Wideband Positioning: Methodologies and Performance Evaluation

Abstract

Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.

Keywords

Industry 4.0 / eKanban / Real-time locating system / Omlox / DeepHub / UWB / Geofencing / Sustainable manufacturing

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the author(s) used Grammarly in order to improve grammar, English expression, and overall readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Author Contributions

Conceptualization, T.C. and V.F.; Methodology, T.C. and V.F.; Software, T.C.; Validation, T.C., V.F., L.G., G.R. and V.A.; Formal Analysis, T.C. and V.F.; Investigation, T.C. and V.F.; Resources, T.C. and V.F.; Data Curation, T.C. and V.F.; Writing—Original Draft Preparation, T.C. and V.F.; Writing—Review & Editing, T.C., V.F., L.G., G.R. and V.A.; Visualization, T.C. and V.F.; Supervision, T.C. and V.F.; Project Administration, T.C. and V.F.; Funding Acquisition, T.C. and V.F.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are not publicly available due to privacy but are available from the corresponding author upon reasonable request.

Funding

The study was developed under the project A–MoVeR—“Mobilizing Agenda for the Development of Products & Systems towards an Intelligent and Green Mobility”, Operation No. 02/C05–i01.01/2022.PC646908627–00000069, approved under the terms of the call No. 02/C05–i01/2022–Mobilizing Agendas for Business Innovation, financed by European Funds provided to Portugal by the Recovery and Resilience Plan (RRP), in the Scope of the European Recovery and Resilience Facility (RRF), framed in the Next Generation UE, for the period from 2021–2026.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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