This paper explores the challenges of applying digitalization in regulated pharmaceutical manufacturing environments. A large range of complex equipment including pumps, valves and vessels may be associated with pharmaceutical batch production processes. Maintenance of such equipment are often based on reactive or preventative strategies which are not always effective and not completely successful in preventing costly downtime or scrap. This research examines how predictive maintenance Key Performance Indicators (KPIs) can be developed through data capture using non-intrusive sensors and their integration with production data derived from Programmable Logic Controllers (PLCs), Enterprise Resource Planning (ERP) systems, and Product Lifecycle Management (PLM) systems. The significance of regulation and the associated challenges in applying digitalization within such a highly regulated environment are also considered. This research aims to shed light on the potential benefits and challenges of implementing digital solutions for predictive maintenance in regulated manufacturing environments to contribute to the enhancement of operational efficiency and product quality while reducing costs due to outages.

With the advent of Industry 4.0 and Industrial Internet of Things, it is appreciated that data availability is essential to provide information to facilitate decision-making in relation to effective production operation and maintenance strategies. This study presents a case application of digitalisation to improve operational efficiency in a regulated medical device manufacturing environment. The case study focuses on a legacy ureteral stent production process in which ageing sideporting machines—classified as critical equipment — pose reliability challenges that impact operational efficiency. Regulatory constraints and cleanroom requirements limit the ability to retrofit sensors or replace legacy controllers, creating a significant gap in data availability. This paper describes a brownfield integration study enabling the transition to a more data-driven production environment. To address these challenges, a non-intrusive data aggregation solution was implemented using an Omron NX102 controller, enabling near-real-time monitoring of machine cycle counts, run times, and punch changes without altering validated equipment. A custom Human Machine Interface (HMI) and local CSV logging ensured traceability and compliance. The collected data was analysed using statistical methods and machine learning algorithms to predict maintenance needs. This approach facilitated the calculation of previously unavailable metrics such as Overall Equipment Effectiveness (OEE) and supported targeted maintenance planning and operator training. The results demonstrate that applying Industry 4.0 principles to brownfield legacy systems in regulated environments can extend equipment life, reduce downtime, and enable data-driven decision-making. This work provides a practical roadmap for integrating legacy equipment with enterprise manufacturing systems in highly regulated manufacturing settings, bridging the gap between traditional processes and smart IIoT-enabled strategies.