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Supply chain management can be infamously complex. But in no industry is it more complex than in life sciences.
Life sciences supply chains operate under unique, high-stakes conditions. It's more than moving products from point A to point B; it's about managing disparate yet sensitive data, meeting ever-changing regulations, and handling materials with various shelf lives across a global landscape.
Unfortunately, international conflicts and fluctuating trade tariffs are just some of the many factors that make it difficult to maintain momentum in supply chain planning. Meanwhile, whether it's a lifesaving drug or a state-of-the-art medical device, patient health hangs in the balance.
To succeed, life sciences supply chains need to be flexible, predictive, and, above all, ready to respond to decisions. Working closely with leading companies, we've identified 10 considerations for building a better life sciences supply chain.
1. Regulatory planning
Life sciences companies live and breathe regulatory scrutiny. Regulatory bodies are vast and varied, asking companies to consider where and how products are manufactured, component sourcing, and matching supply to demand.
Often, life sciences companies need to prebuild inventory so they can respond as soon as a new material, component, or product is approved. If not, this can lead to huge waste.
Predictive analytics can help. From estimating regulatory approval dates, anticipating demand over the approval evaluation period, and providing visibility across the supplier network to balance demand and supply, these insights can help minimize waste and maintain product availability for patients.
2. Shelf-life planning
When shelf life impacts the efficacy of a potentially lifesaving drug, life sciences can't afford to play loose with expiration dates. At a minimum, life sciences companies need to plan for today's demand and make sure to consider shelf life across the supply chain. But the leading companies look much further ahead. They use AI to calculate future demand against expiration dates and external factors to get the right product in the right place at the right time – and at the right quality – to protect patient safety.
3. Artwork planning
When regulatory approvals change or drugs are being marketed in different countries and languages, this can cause chaos in labeling products and directions for use. A supply chain that is agile and flexible is essential to move artwork planning from reactive to proactive. Life sciences companies can tackle this by leveraging late-stage differentiation and postponement strategies to prevent final labeling until it's ready to ship.
4. Launch and tender planning
When a life sciences company launches a new drug or chooses to participate in a tender bid, it relies heavily on what-if scenario planning. Vendors bid on price and volume, and if the deal closes, the company needs to provide a big chunk of the product as soon as possible. If a robust data foundation and transparent cross-functional communication are in place – both internally and across the supply chain – then AI can help companies prepare for tenders. This means giving supply chain managers visibility of possible outcomes so they can make well-informed decisions before a deal is even done.
5. Sequencing and setup optimization
In life sciences manufacturing, there needs to be an extremely thorough cleaning between the production of each product. But any time they aren't manufacturing, companies are losing money. Introducing advanced analytics can help companies discover a production sequence that reduces manufacturing downtime between each product and, in turn, production costs, to keep the supply chain supplied.
6. Quality assurance and quality control (QA/QC)
Manufacturing in life sciences must adhere to QA/QC guidelines, so time must be set aside for QA/QC processes. As quality resources are often managed outside the bill of materials, a planning system that integrates with the quality management system can improve service levels by considering product availability and order fulfillment dates.
7. Clinical trial management
Demand planning for clinical trials differs greatly from demand planning for commercial products. Time-series modeling and AI-powered forecasting can be used for commercial products, but for clinical trials, there are simply too many variables when considering the treatment arms, protocols, patient enrollment, and other variables.
For clinical trials, life sciences companies need to build a forecasting approach from the bottom up, working with partners who understand the complexities of both the supply chain and the life sciences industry – especially if they share manufacturing resources with commercial products. Better clinical trial planning leads to more trials completed for the same cost, increasing market speed, boosting revenue, and potentially saving lives.
8. Contract manufacturing operations (CMOs)
CMOs can help life sciences companies reduce manufacturing costs, but they come with reduced visibility and communication complexities. An additional technology solution is often needed to provide visibility into CMO inventory, production dates, and product availability. If life sciences companies can improve this visibility and transparency with interconnected data, they can reduce waste, achieve more accurate order promise dates and higher service levels, and cut costs.
9. Inventory planning
Inventory in life sciences is not your standard stock. Strategic inventory management is about maintaining stock without compromising compliance or creating waste. Plus, medical device companies have the added challenge of managing consigned inventory at hospitals and trunk stock. Having visibility into various inventory levels is essential. AI tools can also support life sciences companies with robust inventory planning and review processes to mitigate waste due to write-offs from expiration.
10. Long-term planning
True supply chain transformation comes from a long-term vision, especially considering the lead times for regulatory approvals. Life sciences companies need to have tools that support executive-level planning. It's why industry leaders are adopting agentic AI and advanced analytics to simulate how their supply chains will perform under different scenarios, both from a long-term planning (5–10 years) perspective and before the next disruption hits.
Time to act
Life sciences supply chains are among the most heavily scrutinized – and for good reason. A strong supply chain gives patients access to lifesaving drugs or medical devices.
To put patients first, supply chain and life science leaders must shift their focus from firefighting to forward thinking. And it begins and ends with data. With clean, connected, and reliable data – enhanced by AI – companies can make smart decisions for years to come.
