Pharmaceutical companies spend billions of dollars developing, marketing and distributing drugs to the public. It is a complex, intricate process. Yet for decades, their factories have been manufacturing drugs in rather dated ways, “mixing ingredients in large vats and in separate steps, often at separate plants and with no way to check for quality until each step was finished.”
Any moves to modernise have been blocked by cost, complexity and regulation. Ageing factory equipment gaining wear-and-tear are used time and time again, resulting in a severe knock-on effect for pharmas around the world. The supply chain is a fractured one, and could instantly fall apart in the event of a drug recall. Unplanned downtime can cost factories as much as $2 million for a single incident.
In early 2016, SPARKL and Cisco worked with British pharmaceutical giant GSK to develop a proof-of-concept (PoC) using the SPARKL Sequencing Engine technology, in an effort to solve these challenges. There is immeasurable value in collecting and analysing factory sensor data, creating efficiency, reducing downtime and enabling local intelligence amongst the factory’s most important assets - its machines.
SPARKL created a digital profile system, developed over Intel Edison and Arduinos, for physical devices in a specific pill production train, e.g. motors and screw feeds, creating smarter assets. This enabled the devices to spot anomalies within its pill production train in operation, therefore:
- Eliminating tasks from the scheduled maintenance procedures, focusing only on work necessary to maintain compliance and performance
- Predicting failures in advance, reducing negative impact on production
- Extending the operational lifetime of production equipment and components, whilst maximising performance opportunity
- Understanding why a machine or component has failed
- Taking advantage of a component upgrade potential to maximise operational performance over longer periods
Download the PDF to learn how SPARKL used its Finite State Machine abstraction, based on Intel technology, to detect anomalies on a pill production line.