Globally there is an increasing trend towards the use of Industry 4.0 principles and with the Industrial Internet of Things (IIoT) being a key component, regulators are actively encouraging pharmaceutical companies to modernise their approaches to drug manufacturing. The world’s patient population is experiencing a rapidly increasing frequency of drug shortages whereby patients cannot get access to the medicines they critically need. According to the FDA, drug shortages are caused by many factors, including raw materials (27%), manufacturing problems (37%), Quality; delays/capacity (27%), as well as many other disturbances within the supply chain. The industry has issues with batches being rejected and in the worst case being recalled from the marketplace contributing to these drug shortages. Better process understanding, drug product development and manufacturing throughout the commercial lifecycle of drug products will lead to faster to market products and a more reliable, predictable supply chain (Kiernan, 2019).

Many of these issues can be resolved by embracing the Industrial 4.0 revolution and incorporating technologies and tools such as process analytical technology (PAT), big data analytics, manufacturing intelligence, in-process control and cloud architecture into everyday pharmaceutical product development and commercial manufacturing. Adoption of these technologies would also dramatically improve productivity while maintaining competitive advantage and reducing costs for the manufacturer (Dedeurwaerder, et al., 2018), (Gaertner, 2016). This paper presents an example of an advanced, controller-based, approach to Fluid Bed Granulation, incorporating Industry 4.0 principals. The controller development and process execution outlined here was facilitated by SmartX, an Advanced Manufacturing Platform developed by Innopharma Technology Ltd. Incorporating Process Analytical Technology (PAT), the controller uses real-time particle size and moisture content data as well as Fluid Bed Granulation process data to make realtime process control decisions. Particle size was measured in real time by the Eyecon2 particle analyser, while real-time moisture content was measured by the Multieye2 NIR Spectrophotometer. This automated approach resulted in greater in-process control and repeatability as well as less batch to batch variation. The controller design presented here is intended as a novel example to highlight the flexibility and potential when developing such an automated control driven approach