PC-FEB22-PG30-31.1_Layout 1 09/02/2022 16:20 Page 30
CHEMICALS & PHARMACEUTICALS
PHARMA’S FUTURE IS NOW AT THE EDGE
plant produces a lot of data. To put the volume of this data into perspective, the average clinical researcher at a pharmaceutical company can generate tens of terabytes (TB) of data per day through scientific experiments. This translates to the equivalent of 1 million phone books worth of data. And this does not account for the data produced elsewhere, such as in the production, dispensing or packaging of medication.
The rapid deployment of the Internet of Things (IoT) across the manufacturing spectrum means that sensors are being placed at virtually every touch point of a production line. These sensors record an abundance of data; but, making this data available and meaningful to operators in real-time is key to achieving agile production. This is only possible if the data is processed close to its source — on the shop floor at machine level.
Processing data locally
Johan Jonzon, CMO and co-founder of Crosser, shares insight into how edge analytics can optimise pharmaceutical processing methods
ccording to ABI Research, pharmaceutical manufacturers will spend USD 1.2 billion on data analytics by 2030. The industry is accustomed to one way of operating — large batch size production. As pharmaceutical production moves to a more personalised approach, manufacturers may struggle to keep pace and remain agile. So how can the industry catch up?
A
Unlike pharmaceutical manufacturing facilities of the past, which often focused on producing just a handful of medicines — typically of the same form, such as tablets, liquid medicines, or vaccines — today’s manufacturing lines are expected to adapt for multiple different products. This expands to accommodating the manufacture of personalised medicines and small batches, which can be challenging for pharma manufacturers who are accustomed to large scale manufacturing for a limited number of products.
Making pharma flexible
Personalised medicine moves away from a ‘one size fits all’ approach to creating products that are tailored to a patient’s personal condition. Using genetic or other biomarker information to tailor medical treatments offers the potential to treat previously incurable diseases. The method is becoming favoured across the industry.
30 FEBRUARY 2022 | PROCESS & CONTROL
However, the manufacturing of personalised medications isn’t without its challenges. Working with smaller companies to develop smaller batches of product brings added pressure for pharmaceutical manufacturers, such as reduced time-to- market if the medicine is in sudden high
hyperautomation solution brings intelligent workflows to industrial and asset rich organisations, like pharmaceutical plants
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demand. The development of COVID-19 vaccines is a pertinent example of this need for agility, as production adapts to a fast, urgent influx of new medicines. To keep up with demand, pharmaceutical facilities need to adapt to a new way of operating. This might sound like it requires more equipment to handle increasingly complex methods of production, but instead manufacturers must consider investing in a good data management strategy. No matter what kind of product it’s producing, a pharmaceutical manufacturing
” Crosser’s
There are additional struggles manufacturers must battle too. Security and data privacy are major concerns for the industry, especially when dealing with data such as personalised medication. Processing data locally, however, is often more secure than transferring it to the Cloud. In 2020, pharma giant Pfizer suffered a data breach when patient information was exposed on unsecured Cloud storage. In fact, between 2018 and 2020, over 30 million patient records were exposed because of Cloud misconfigurations, costing organisations an estimated $5 trillion. While cyber hacking is a severe issue — one that’s proving difficult to overcome — edge analytics can make data interference more challenging for hackers. Processing at the edge limits the frequency at which data is transferred between its source and the data center, reducing the risk of data breaches. What’s more, processing data at the edge reduces operational costs. Instead, by processing close to the source,
manufacturers can condense the quantity of data that would originally be sent to the Cloud by more than 98 per cent. This reduces bandwidth and Cloud service costs, while promoting faster and more efficient data collection.
In addition to processing data at the edge, making it available in almost real-time, pharmaceutical manufacturers must also make their data infrastructure simpler. From laboratory information systems (LIMS) to enterprise resource planning (ERP), to inventory management systems, pharmaceutical companies use many types of software and it’s not uncommon to have different vendors and generations of
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