Pharma Tariffs Uncertainty: Why Data and AI Are Powerful Supply Chain Management Tools
Looming tariffs are driving life sciences organizations to once again reconsider the sources of their active pharmaceutical ingredients (APIs), manufacturing locations and ways to offset supply chain costs. With the shifting policies and volatility of the global tariffs landscape, many challenges can hinder the fast and precise insights that companies need to keep up.
Since many large pharmaceutical companies manufacture the bulk of their generic antibiotics, vaccines and other medicines outside of the United States, these drugs are particularly at risk of tariffs’ effects. The drugs are already produced on very thin margins, so tariffs are likely to increase existing costs, with the potential to create shortages in the supply chain and cause disruption, according to The New York Times. In this context, life sciences organizations face many challenges — but also opportunities — in optimizing their end-to-end manufacturing and distribution processes using data and AI to stay competitive. Companies across industries are realizing that staying agile and competitive in this tariff landscape requires a unified data foundation with collaborative sharing and AI capabilities to speed up analysis, improve forecasting and adapt quickly to market shifts.
Drug production challenges: Keeping up with tariffs
A big obstacle comes from a lack of visibility across the supply chain, necessitating better data collaboration with suppliers and sources to understand potential disruptions from weather, labor or supply shortages. Siloed data across internal teams, suppliers and distribution partners further impedes coordinated responses.
Tariffs on APIs, which are often sourced from a few specific countries, can lead to potential drug shortages due to the increased cost of producing drugs in the United States — in fact, 35% of APIs come from outside the United States. This requires companies to improve planning, proactively anticipate cost or supply issues, shift production locations and source alternative providers.
Furthermore, economic headwinds compel organizations to achieve more with less, as expiring patents accelerate R&D and manufacturing cycles. This makes AI a crucial tool for driving operational efficiency.
The data and AI opportunity
These same challenges also present an opportunity for leading life sciences companies to turn complexity into simplicity by connecting critical data and delivering trusted insights to navigate any disruptions with speed and confidence. Data is the foundational starting point — uniting siloed data across sources and systems helps generate greater supply chain visibility.
Such a unified data foundation empowers AI capabilities such as enabling better predictive analytics at scale, leading to a stronger understanding of potential demand for vaccines, antibiotics and other medicines; identifying unvaccinated populations; and leveraging dashboards and automated triggers for predictive and preventive activities. For example, Siemens Healthineers AG created a supply chain management (SCM) control tower in the form of comprehensible dashboards by pulling in various data sources from sales orders, inventory, shipping, delivery and more. With dashboards, automated triggers for workflows, and data analytics, the global control tower provides a methodology for predictive and preventive activities, as well as at-a-glance information about divergences in the supply chain.
AI also helps drive efficiency across the supply chain by anticipating maintenance downtime and proactively identifying gaps and alternative providers. One source is through the aggregation of first-party and third-party data, including Snowflake Marketplace data (such as Verato, CareJourney Merative and Caresyntax).
Snowflake solution
In an uncertain economic environment, data and AI have the potential to enable more predictability in the life sciences supply chain. Pharma companies are using Snowflake’s AI-powered analytics to actively reengineer their sourcing and supply chains.
Snowflake simplifies the elements, from building a data foundation to sharing democratized insights and harnessing the power of AI analytics and agents to make better-informed decisions under changing tariffs. Snowflake’s AI Data Cloud brings together vast amounts of internal and external data, enables predictive modeling and advanced analytics and makes it easy to share governed insights among stakeholders. With Snowflake, not only are organizations responding to tariff shifts, but they are proactively optimizing operations and making smarter, faster decisions at scale.
In a world of trade volatility, life sciences companies that embrace a unified AI data strategy will not just survive but thrive, turning tariff challenges into opportunities for competitive advantage and sustained growth.
