Life Sciences: A Data-Led Approach to Sustainability Transformation
By Julie Neal, Director at management consultancy, Vendigital, and Dr. Ulli Waltinger, Vice President specialising in AI and IoT at Siemens Advanta.
Pharmaceutical and biotech companies have gained significant recognition for their focus on innovation, but they face many challenges in achieving a sustainability transformation and mapping their journey to net zero. Harnessing the power of advancing technologies including Artificial Intelligence (AI) and automation can help businesses to accelerate change and achieve their sustainability goals. But are they progressing plans to adopt sustainability and AI practices?
Research conducted by Vendigital with 200 senior-level decision makers at life sciences and healthcare-related organisations in the UK and Ireland shows that 71 percent are already leveraging AI-based solutions and other advanced technologies to progress their sustainability journeys and the majority have net zero targets in place. However, the complex range of factors that need to be tackled to decarbonise products, processes and supply chains in the life sciences sector is a major challenge, and progress in relation to Scope 3 emissions is still at an early stage.
The pharma industry is facing a particularly complex set of challenges to become more sustainable. It is a waste intensive industry, generating an estimated 100 kg of waste for every kilogramme of drug made. Pharma production lines are also responsible for 23 percent of global water consumption, and widespread use of heating and cooling systems means they consume a lot of energy too. Achieving net zero will demand digital transformation on an industrial scale.
Early Wins Can Offset Capital Investment
About 70 to 80 percent of a pharma company’s emissions come from its manufacturing processes, and a holistic life cycle carbon assessment is required to identify where the greatest opportunities for carbon reduction lie. Building a bespoke data-based model and overlaying it with detailed cost, carbon emissions and other third-party data can help businesses to prioritise measures that will reduce carbon emissions quickly and significantly, at the same time as reducing costs and improving operational efficiency. This approach can bring early wins and help to offset capital investment in advanced technologies, such as automation.
As well as focusing on their own sustainability goals, pharma and biotech companies need to look beyond the things that they can control directly – i.e. their own products and processes – and focus on tackling Scope 3 emissions. This will require much greater collaboration with partners across the value chain. There is also a need for close governance, as even the smallest process or product change is likely to require regulatory approval and could trigger the need for clinical trials. The complexity involved in designing and implementing a sustainability plan that encompasses all of these elements requires a systematic approach, and it is important to use an industry-specific framework.
Embracing AI to Deliver Sustainability Goals
As a recent whitepaper by Siemens Advanta shows, significant adoption of AI is evident in several key areas directly related to life sciences, including drug discovery and development; precision medicine; precision therapeutics and production. For example, AI is enabling the biotech industry to identify repurposing opportunities and accelerate R&D processes, so new drugs can be developed and brought to market more quickly. In terms of production, AI-powered data analysis enables companies to reduce waste and optimise inventory control and demand planning. But are life sciences businesses ready to embrace the predictive power of AI and advanced technologies to achieve their sustainability goals?
Most manufacturers are aware of the role that digital twins can play in aiding decision making and improving operational efficiency. By creating a digital model of a physical asset or process, such as a complete factory or a production line, it is possible to test new energy-efficient machinery or a low-carbon product change before any capital investment is made. Crucially, these AI-powered models can be trained to become ‘green digital twins’, capable of modelling the lifecycle carbon reduction impact of a proposed process or product change before it happens.
Up-Skilling or Re-Skilling the Way to Net Zero
Data-based models can also be used to assist businesses in delivering change programmes by improving their skills management. For example, businesses may need to reduce or adapt their workforce to make way for more automated systems. They may also need to increase capability in the area of science and digital tech as they move closer to a circular economy business model. By planning ahead, data-based models can be used to identify reskilling or upskilling opportunities within the existing workforce, which could help to minimise the need for workforce reduction. In some industries, businesses have taken action by developing their own learning and development initiatives and opening them up to others in order to build a pipeline of skilled talent for the future.
The life sciences factory of the future has an opportunity to re-model itself to become more sustainable, adaptive and people-centric on route to net zero. There is no time to waste; improving data understanding and adopting AI and advanced technologies is now essential to stay competitive and achieve sustainability goals.