Why pharmaceutical supply chains fail despite heavy investment:
When Hurricane Helene flooded Baxter’s North Cove plant in 2024, hospitals were rationing IV bags within days. Cisplatin ran short around the same time, after quality shutdowns hit the Indian facilities that supply most of the U.S. generic market. Oncology teams scrambled for alternatives mid-treatment. In both cases supply chain data existed somewhere, but no system was connecting supplier risk signals to specific products in specific pipelines early enough to act.
Pharma has put serious money into the surrounding infrastructure. USP data puts U.S.-manufactured API volume at just 12% of domestic consumption, which has pushed significant reshoring investment. DHL committed €2 billion over five years to life sciences logistics. The global GLP-1 receptor market was actually valued at an estimated $53.46 billion in 2024, and every unit requires 2–8°C coverage from manufacturer to patient.
But the industry is still missing the execution layer that ties all of that together. To truly reduce the fragility and risk, these supply chains need a system that takes a real-time signal and acts before the exception becomes a loss.
The first peer-reviewed empirical study of serialization costs at Irish pharmaceutical manufacturers, published in 2024 by O’Mahony et al. at the University of Galway, covered 114 packaging lines across 11 manufacturing sites, representing about 65% of Ireland’s automated packing capacity. Capital costs came in four times higher than European regulator projections. The average increase in cost of goods sold was 2.7%. OEE declined at most sites and never recovered. One manufacturer was still running 10% below its pre-serialization baseline a year later. Another tracked roughly 45 production days of rework tied to serialization annually.
The cost stayed because nobody automated the exception handling. A data mismatch during receiving still needs someone to catch it, figure out what went wrong, and route it somewhere.
U.S. manufacturers are working through the same challenges under DSCSA. By mid-2023, only about 35% of U.S. manufacturers could send serialized data to distributors, per a McKesson estimate. The largest distributors were warning that data alignment failures could force them to quarantine roughly a third of incoming product. The FDA granted phased exemptions, pushing full enforcement from November 2023 into 2024. Much of the compliance machinery is in place. The exception handling it generates is still largely manual.
A temperature excursion on a gene therapy is not the same problem as a temperature excursion on a generic antibiotic. One product has a stability window that can be measured in hours; the other has months of margin. The response needs to reflect that, and it needs to happen before the product is compromised.
A single gene therapy shipment can carry a treatment value of $4.25 million. When one goes out of range in transit, whether it’s still usable depends entirely on that product’s remaining stability budget — how much of its viable window is left. At most companies today, someone is checking a dashboard, pulling up the specs, and making that call. Often after the shipment has already been delivered.
Connecting excursion data to product-specific stability thresholds and triggering an intervention in time is harder work than installing the sensors. Most companies have done the sensor part.
Drug shortages are becoming more chronic and frequent. ASHP counted 216 active drug shortages in Q4 2025, and the USP’s 2025 Annual Drug Shortages Report revealed that the average shortage now runs over four years. Underlying these shortages are thin margins, manufacturing complexity and geographic concentration that create risk. For example, India manufactures 95% of ACE inhibitor APIs for the U.S. generic market.
Those factors wouldn’t be nearly as dangerous if networks could see disruptions coming. Unfortunately, many can’t.
According to McKinsey’s Supply Chain Risk Pulse 2025, only 42% of leaders say they have meaningful visibility beyond their tier-one suppliers, a number that actually declined over the previous two years. When respondents rated their own capabilities, long-range supply visibility came last.
The combination of deep concentration and shallow visibility makes acute failures inevitable. The Baxter IV shortage and the cisplatin disruptions felt like discrete emergencies, but given the supply structure, they were entirely predictable.
Domestic manufacturing investments will eventually reduce that geographic concentration, but building factories takes years. In the meantime, networks need an execution layer that can detect a tier-two supplier quality event, trace it to specific products and orders, and start working alternatives before customers even have to call asking where their shipment is.
Most pharma supply chain control towers in production today are dashboards. They consolidate data, surface alerts, and wait for a person to act.
An AI-powered pharmaceutical supply chain control tower takes a temperature excursion, connects it to that product’s stability data, evaluates the remaining stability budget, and triggers an escalation based on rules the operations team already defined. No dashboard review required. Similarly, a serialization data mismatch can be routed to the right exception workflow before the shipment hits a quarantine hold, or a supplier quality event traced to specific orders before it surfaces as a shortage. Each exception travels from signal to decision without waiting for someone to open a screen.
This is exactly what FourKites has brought to production. Rather than waiting for human handoffs, leading pharmaceutical networks use FourKites’ AI agents to automate custom exception workflows. The platform connects real-time track-and-trace automation with digital twins of your inventory to handle the execution based on parameters you define. When an exception occurs, the system provides AI-driven inventory recommendations and executes tasks, from automated document management to booking replacement freight with a single click.
A gene therapy shipment worth $4.25 million has sensors, qualified lanes, and serialization codes. Those systems do their part. Getting from a signal to an action, in time, is what the FourKites execution layer adds.
See how FourKites supports real-time pharmaceutical supply chain execution →
