Sustainable Compliance in Regulated Manufacturing: Deploying a Low-Code, AI-Driven eDHR Framework for Scalable Traceability

Abstract

This article describes the deployment of a validated, low-code electronic Device History Record (eDHR) system on a high-mix, low-volume manufacturing line at Smith & Nephew’s Memphis site. Prior to this work, all Device History Records were maintained on paper, creating delays in inspection, opportunities for transcription errors, and long retrieval times during audits. The new system runs on an industrial IoT platform and connects wireless gauges, barcode-based calibration checks, dynamic sampling logic, and modular inspection applications. Each module was validated independently and assigned to specific workstations so changes could be controlled without large documentation overhead. In day-to-day use, inspectors capture measurements directly from calibrated tools, materials are verified at scan, and each routing step is logged with a timestamp and operator ID. The transition from paper packets to a digital workflow reduced inspection time, lowered documentation defects, and improved audit preparation through immediate access to traceable records. The framework described here has since expanded beyond the original pilot to additional sites and value streams within Smith & Nephew, with further integrations and AI-enabled capabilities in active development. The resulting deployment provides a practical and replicable model for regulated manufacturing environments that need to strengthen compliance while gaining flexibility for future analytics and system integration.