AI-Driven Predictive Maintenance for Biotech Equipment using Lean Six Sigma: Enhancing Operational Efficiency and Reducing Downtime for Medical Practices in the UK

Abstract

The purpose of this research is to assess how the application of AI predictive maintenance can enhance the correct biomedical equipment in facilities such as those of the United Kingdom’s National Health Service (NHS) medical practices. Magnetic Resonance Imaging Units, diagnostic equipment and analysing systems which are categorized under biotechnology apparatus/instruments are core in providing optimum service to patients. However, failure in equipment is accompanied by an understanding of challenges by offering services interferences, cost and on patients’ outcomes. This research has employed the machine learning models that included; but not particularly limited to, Random Forest classifiers (RF), SVMs formulated within Lean Six Sigma DMAIC. Real-time data was captured using sensors placed on equipment to monitor failures based on temperature, vibration, and usage hours on which basic AI structures and algorithms were designed. The Lean Six Sigma methodology was applied with the help of templates, such as Value Stream Map and Failure Modes and Effects Analysis to eliminate marring maintenance processes. For this study, logs on equipment performance, sensor, and maintenance records of five NHS affiliated medical practices were gathered for 18 months. Research papers compared performance figures before and after the implementation of the system to consider reductions in machine availability, costs, and OEE. The results show that by assimilating AI-PdM technologies and LSS strategies, the company was able to slash its overall downtime to a level that was 32% less unexpected, drive down maintenance expenses by 20%, and boost the equipment’s OEE level from 78% to 90%. Best results came from the Random Forest model, with accuracy of 93%, precision of 90% and recall of 89% in predicting equipment failures. The lean six sigma was able to make steady improvements within the processes to minimise interruptions to effective operations and also to improve patient care. This dual efficiency model proves to be very effective for the practices affiliated with NHS, and affords a solution that is at once integrated and efficient in addressing the problem of biotech equipment maintenance. The study recommends broader adoption of AI-driven predictive maintenance (PdM) integrated with LSS and further research into advanced AI models and IoT technologies to extend these capabilities across the NHS network.