Information Fragmentation and Accessibility

Operationally, the warehouse workforce faces a practical “information disconnect” that creates noticeable inefficiencies in daily logistics. Frontline workers handling physical goods usually lack direct access to digital systems while moving through the facility. To answer routine logistical or technical questions, such as “Is article X currently in stock?”, “Is this timber batch treated for outdoor use?”, or “Where is commission Y stored?” they frequently need to interrupt their workflow to walk to a central desktop terminal. Alternatively, they must locate and ask a more experienced colleague. This manual information retrieval causes recurring disruptions and unnecessarily ties up the resources of senior staff. Ultimately, the lack of immediate, hands-free data access at the point of action slows down the picking process and leads to avoidable delays in overall warehouse operations.

Operational Inefficiencies in Logistics

From a logistical perspective, the lack of real-time, hands-free information access creates bottlenecks. Warehouse staff often struggle to locate packed commissions or verify stock levels instantly while operating machinery or handling goods. The current environment lacks a digital interface that supports the mobile, hands-busy nature of timber logistics, leading to avoidable search times, picking errors, and delayed order processing.

Acoustic and Hardware Constraints for Speech-to-Text

Beyond operational hurdles, establishing a reliable voice interface in this specific setting presents a massive technical challenge. The timber yard is inherently loud, characterized by the continuous noise of heavy forklifts, machinery, and the physical handling of massive wooden goods. Furthermore, the workforce relies on standard two-way radios for mobile communication. These devices transmit highly compressed, narrowband audio that frequently suffers from static, frequency interference, and clipped sentences (push-to-talk delays). Achieving accurate Speech-to-Text (STT) recognition and extracting exact data points, such as alphanumeric article numbers or matchcodes, from such noise-polluted, low-quality audio streams is highly complex. Standard STT models struggle under these harsh conditions, requiring the underlying AI system to be exceptionally robust in processing garbled input to ensure the assistant remains a helpful tool rather than a source of frustration.

Objective 1: Enable Real-Time, Hands-Free ERP Interaction

The first and main objective is to bridge the gap between the physical workforce and digital record-keeping systems. The experiment aims to develop and deploy “Agent” capabilities within the Open Voice OS (OVOS) framework that allow the DIA to autonomously query the ERP and WMS via APIs. This will enable logistics staff to perform stock checks, track order statuses, and locate packed commissions using voice commands via radios, targeting a 40% reduction in average information retrieval times.

Objective 2: Validate User Acceptance in a Non-Desk Environment

The second objective is to successfully integrate the conversational AI into the daily routine of the logistics workforce. The experiment seeks to demonstrate that a voice-first interface via radios is a highly viable and preferred tool for a rough, hands-busy industrial setting. A key success metric is achieving an 80% user adoption rate among the warehouse staff within the first two months, proving that the solution effectively reduces their cognitive load without disrupting physical workflows.

Objective 3: Democratize Technical Knowledge via RAG

The third objective is to make complex, unstructured technical knowledge instantly accessible to all operational employees, regardless of their tenure. By implementing a Retrieval-Augmented Generation (RAG) pipeline, the experiment aims to ingest and index vast libraries of supplier documents and internal guidelines. The goal is to enable the Digital Intelligent Assistant (DIA) to answer specific technical queries with high accuracy (target: 90%), thereby reducing the dependency on senior experts and accelerating the autonomy of junior warehouse staff.

Sector and Operational Environment

The experiment takes place in the Timber Trade and Logistics sector. To validate the solution in a real-world scenario, the pilot is carried out in an operational test environment provided by Holzhandel Vogt in Germany, which acts strictly as the use-case provider. The setting focuses specifically on the active warehouse and logistics yard, where goods are actively picked, packed, and loaded. This rough industrial environment is characterized by high noise levels, heavy machinery such as forklifts, and a vast inventory of varied physical products, necessitating robust and hands-free communication methods.

Target Users and Stakeholders

The primary target users are warehouse employees and logistics staff who require immediate, hands-free information access to maintain safety and efficiency during their daily physical operations. By utilizing two-way radios, these workers can retrieve essential operational data without interrupting their physical tasks. Secondary stakeholders include the IT department of the use-case provider, which manages the underlying ERP and Warehouse Management Systems (WMS), as well as the management team, which is highly interested in workforce resilience, knowledge preservation, and process optimization.

Constraints and Requirements

The experiment must adhere to strict data privacy and security standards, including GDPR, particularly regarding any employee data or internal operational metrics processed by the LLM. From a technical perspective, the solution must seamlessly integrate with the existing ERP and other system infrastructure via REST APIs and MQTT, and function reliably through the audio interface of two-way radios operating within the warehouse’s existing communication network. Safety is paramount in this setting; the voice interface must provide clear, concise information without distracting operators while they are maneuvering heavy machinery or handling materials.