As companies come out of the pandemic-induced disruptions, life sciences companies that have made significant investments in digital technologies during the past 2-3 years are better positioned to drive growth. Deloitte’s 2022 Global Lifesciences Outlook report predicts that, in 2022, visionary leaders will use automation, smart factories, and Artificial Intelligence (AI) to transform the value chain and build supply chain resilience. This would entail shifting gears from ‘doing digital’ to ‘being digital’, where the leadership will have to drive enterprise agility at scale and adopt a digitally native mindset. Before we look at how organisations can use analytics, including AI, across the value chain, understanding the dimensions driving this change would be key.
The past couple of years into the pandemic have resulted in unprecedented uncertainties across the value chain. Companies had to be agile and respond quickly to the regularly changing market and supply conditions. Although the situation is stabilising, companies are evaluating the resilience of their supply chains to better prepare themselves for future disruptions. We also witnessed stakeholders, including regulatory agencies, collaborating to ensure availability of drugs, especially vaccines, with an accelerated timeline. In the future, adopting a patient-centric approach to treatment will be a key focus area. Hence, stakeholders are evaluating how the learnings from the past two years can be used to collaborate for personalised treatments. In addition, regulatory aspects and ESG-related disclosures are expected to bear upon companies as they try to adhere to global standards.
To address these challenges while maintaining growth, pharma companies would need to move away from the legacy ways of working. In the past, especially for Indian pharma companies, high margins (relative to other industries) ensured that there was little need for digital technologies to drive quality, efficiency, and visibility across the chain. Given the pricing pressure and the other challenges discussed earlier, pharma organisations have started seeing the merits of using analytics, albeit a bit late. In the pre-COVID-19 era, some pharma companies started AI pilots. During the pandemic, these companies accelerated investments in AI. By 2040, pharma companies will acknowledge AI as a significant enabler for developing new drugs and building intelligent manufacturing and supply chain capabilities.
Across the pharma ecosystem, R&D probably offers the maximum ROI from the use of AI and analytics. Significant costs and efforts are incurred in R&D. Starting from the discovery phase, the approval of a novel drug takes 10-12 years. Among the 20 targets evaluated at the pre-clinical stage, only one makes it to the approval stage. Hence, a large number of companies, including start-ups, are looking to use AI to expedite drug discovery and development. Using AI, companies have been successful in moving from discovery to the pre-clinical trial stage within a year that otherwise used to take multiple years. Pharma companies are also exploring the potential of AI algorithms for stratifying patients to improve the probability of trial success. Although we saw accelerated development and manufacturing of vaccines during COVID-19, we can expect more drugs to be developed and brought to the market much faster in the future using AI.
Another interesting area for pharma companies is manufacturing. The industry has traditionally been reactive to the issues that come up during manufacturing, analysing, and fixing them manually after a problem has occurred. Advanced analytics provides opportunities for companies to carry out proactive quality control using Machine Learning (ML) and preventive maintenance; automate batch releases using AI and Robotic Process Automation (RPA); and improve yield. A few percentage points of yield improvement lead to significant benefits for the pharma industry. The challenge that the industry would need to consider is the regulatory aspect as most lines and processes are validated for regulated markets. Hence, making changes overnight may not be feasible. The required process changes for manufacturing and the subsequent revalidation can therefore take months or a couple of years, making this a long-term play.
On the other hand, supply chain orchestration is a low-hanging fruit for the pharma industry. There are numerous examples and learnings from other industries, such as FMCG and retail, on how advanced analytics can be used to build automated/autonomous supply chains − starting from demand planning to distribution. Sales and marketing teams are exploring analytics to optimise Healthcare Professional (HCP) interactions and marketing spend across channels. Technology is being looked at as an enabler to bring the voice of HCPs and patients to the product design team for subsequent iterations and incorporating feedback.
The analytics journey can start with identifying the use cases that have sufficiently clean data; address key pain points; and can also be tested out quickly. In parallel, companies would also need to hire and groom ‘digital talent’, who would be able to support and sustain digital transformation initiatives.
Authors: Antony Prashant, Partner, Deloitte India and Sreejith Unnikrishnan, Director, Deloitte India