RF Walk-in Goods Issue with Honeywell Movilizer – AI Enabled Robotic Processing with Pick to Light Technology

Abstract

Warehouse execution in manufacturing and distribution environments is under sustained pressure to deliver faster material throughput, higher inventory accuracy, and adaptive responses to unplanned demand. Radio frequency (RF)-based walk-in goods issue processes address some of these needs but remain constrained by reactive transaction flows, limited operator guidance, and the absence of predictive inventory logic. This article proposes and analyzes an integrated architecture that combines RF walk-in goods issue workflows with Honeywell Movilizer as a mobile execution middleware, artificial intelligence (AI)-enabled robotic processing, and Pick to Light (PTL) physical guidance technology. Each technology layer is characterized by its functional role, architectural interfaces, and contribution to overall execution performance. Evidence from peer-reviewed literature establishes that PTL-guided picking can reduce order-line completion time by approximately 7.5 seconds relative to RF scanner-based methods, that 95% of domain experts surveyed confirm IoT deployment improves inventory accuracy, and that XGBoost-based machine learning (ML) demand forecasting may reduce forecast error by approximately 32% compared to Autoregressive Integrated Moving Average (ARIMA) baselines. The article addresses system integration design, latency management, scalability, and change management, and proposes a reference architecture for warehouse engineers and operations technology practitioners seeking to advance goods issue execution through converged physical and digital automation. Conflict of interest: none declared. Funding: none.