Digital transformation in life sciences is at an inflection point. An advanced digital agenda will give companies a distinct competitive edge, says David Cooper, segments vice president, Schneider Electric.
As a result, companies are actively changing their approach to digital transformation strategy.
Many organisations recognise how their paper-based operations and siloed data capture constrain their ability to respond to rapidly changing marketplace demands. The pandemic forced radical changes to standard operations. The ability to capture, consolidate and rapidly analyse data has emerged as a critical success factor impacting regulatory compliance and profitability.
Compartmentalised data creates barriers
Existing data silos are problematic because research and development and engineering design data are challenging for process line manufacturing stakeholders to access. On manufacturing lines, the numerous types of equipment are often maintained by entirely different teams of engineers. It becomes difficult, costly and time-consuming in such environments to blend the data created across these various “islands.”
The data silos also make it complex to communicate back to the regulators who demand evidence that a highly controlled production environment is being maintained. With vast quantities of data generated from many isolated systems – such as research, production, and quality control − visibility is limited on how that data impacts production efficiency, and product quality.
Maximising value through data control
True digital transformation is about developing the capabilities of capturing, blending, and contextualising data. It then enables that data to be quickly accessed and used by multiple organisational stakeholders.
Building data ontology (the ability to discern the properties of the various pieces of data and the relationships between them), building a data hierarchy, and designing a data architecture based on a consistent set of rules and governance all help to maximise the business value of the data that daily operations generate.
The ability to capture and analyse data should also extend to life sciences operations’ contracted manufacturing. For example, from a regulatory standpoint, questions that pertain to drug ingredients, where those ingredients were sourced, and when the drug was produced need to be quickly answered — even if the drug in question has been manufactured via third-party contractors.
The data must also be managed beyond just one plant within one geography. Enterprise-wide data must account for variations across multiple plants and global regions where regulatory statutes may differ.
Logical areas to accrue benefits
To achieve the goals of high visibility and flexible operations, life sciences firms have launched digital transformation initiatives designed to help drive faster and more accurate business decisions.
Below are several examples of areas where significant digital transformation-based improvements are taking place:
In the life sciences industries, traceability for safety reasons is critical to regulators and the customers who consume the manufactured drugs. Pharmaceutical firms are tasked with assuring that the drugs they manufacture are identical regardless of where they are produced.
Making these drugs completely replicable across all environments requires clear visibility into the details of laboratory and manufacturing operations. Digitisation of operations in a way that facilitates this high level of traceability can now be accomplished through the right combination of digital services, artificial intelligence, and analysis software.
Such tools enable real-time feedback on an ongoing basis. In addition, since levels of connectivity increase dramatically in such scenarios, cyber security becomes a critical success factor for protecting digitised operations.
During the intense rush to roll out COVID-19 vaccines, major drug manufacturers were forced into an “all-hands-on-deck” emergency work approach.
Though temporarily successful, such an approach is not a tenable long-term strategy. The pandemic only exacerbated the issue of experienced labour shortages. To address this issue, and to empower new, inexperienced employees with the correct information to guide them in their tasks, tools such as “digital twins” have emerged as a realistic and affordable option.
A digital twin is a virtual software model that analyses data and uses it to run simulations and benchmark performance. This allows plant operators to pinpoint where efficiency gains can be made. By pairing both virtual and physical worlds, data analysis and monitoring of systems can actively avert problems before they occur, preventing downtime and developing new efficiency opportunities.
In the case of the life science industries, the new workforce is empowered to work in augmented reality (AR) and virtual reality (VR) training environments, confronting and addressing problem scenarios without causing damage or disruption to existing operations. Those types of AR-VR tools are then expanded to places like engineering design and manufacturing production environments.
Mastery of both product and process data enables better quality of output. In some cases, batch processes can affordably be converted to continuous processes, increasing output delivery while boosting output volumes.
Such changes also create a beneficial ripple effect of minimising waste while enhancing sustainability in business processes. When manufacturing a better-quality product consistently, operations inevitably save energy. This is a critical criterion in the production of higher-sustainability drugs.
The increased flexibility in operations also enables the reuse of formerly only single-use process materials− like bioreactor processes that traditionally deployed only single-use/throw-away bags.
David Cooper joined Schneider Electric in 2007, previously holding positions at Routeco plc. He leads Schneider’s segments teams throughout Europe, CIS, and Africa across Food & Beverage, Life Sciences, and Household & Personal Care.