Why AI-Augmented Traceability Hubs are the Future of Pharma Serialization

The pharmaceutical industry has focused on getting its track-and-trace house in order for more than a decade now. Compliance was the big driver, with the European Union Falsified Medicine Directive (FMD, enacted in 2011) U.S. Drug Supply Chain Security Act (DSCSA, 2013) later joined by a growing list of national drug-safety and anticounterfeiting regulations.

Motivating all parties: a global counterfeit-drug epidemic estimated to cost the industry $200 billion to more than $400 billion a year while putting patients health at risk with fake weight-loss and cancer medications, Botox, HIV/AIDS therapies, and more. In developing countries, an estimated 10% of pharmaceuticals are fake, which is an epidemic in itself.

The big FMD/DSCSA push is behind us, and, in general, the industry’s work on the first four levels of serialization is mostly in place: the barcoding of product units and packages; the packing-line-level serial-number management; the manufacturing-site-level orchestration and control; and the enterprise-level serialization systems.

The big challenge now resides at level 5, the cross-organizational space involving interoperability and visibility among hundreds of thousands of players—manufacturers, distributors, wholesalers, and dispensers—managing data tied to billions of orders a year throughout the global pharmaceutical supply chain.

Enter the traceability hub

Level 5 serialization is typically described as the “network level.” A combination of secure public-cloud technology, AI-powered analytics, and AI-assisted serialization collaboration capabilities is now enabling something more: traceability hubs. These enable immediate supply chain visibility and, with that, quick issue detection and resolution.

Crucially, the economics work for companies of all sizes. That’s critical, because countless smaller players who historically haven’t been in a position to justify the cost of major track-and-trace systems must also participate in traceability hubs if serialization is to achieve the goals of regulatory mandates.

What’s more, the promise of serialization traceability hubs involves more than better safety, counterfeit-drug detection, and compliance. They will be good for business, too.

AI will play a key role in that. First, when given the huge volume of messages pouring through pharma manufacturing and distribution ecosystem and its diversity of IT infrastructure, mistakes happen.

Gen AI can track analyze exceptions, propose root causes, and speed up responses to incidents with real-time decision support in serialization exception handling.

On the business side, integrating serialization data into a common data layer shared with ERP and manufacturing systems can enable a pill-bottle-level grasp of the supply chain, a level of detail that will drive better demand forecasting and matching of production to demand, reducing warehouse and cold-chain capacity needs and eliminating waste, particularly with perishable drugs.

Those sorts of capabilities can help the industry realize business value from compliance-driven investments. Exactly how pharma firms harness the enormous amount of data traceability hubs present remains to be seen, but improved supply chain visibility and integrity and optimized batch-recall processes are two appealing near-term candidates.

An example of a traceability hub in action

But for now, let’s walk through a simple example of a traceability hub adding value. A manufacturer produces a batch of medications, generates randomized serial numbers, labels the meds, and transports them to a wholesaler warehouse 30 minutes away.

The manufacturer’s enterprise serialization repository captures the data, consolidates what can be hundreds or thousands of messages associated with the shipment, and sends them to the wholesaler in a transaction information exchange message.

Normally, the message just goes through. But let’s say something gets lost or garbled and the wholesaler only gets part of the message.

The manufacturer’s serialization operations team goes on the hunt for the problem, which involves looking into a couple of internal IT systems. That may take 15 minutes if the team is skilled.

If the problem is with the network solution provider (say the wholesaler system has an expired security certificate and the transaction failed as a result) or in the wholesaler system, that adds more time as those partners look for issues. And because the trucks are already headed to the wholesaler, you’ve got less than 30 minutes to avoid a supply chain disruption.

Delays can mean drugs don’t reach patients when they should, which is bad for their health and the brand reputations of those involved in the delays.

The role of Gen AI in traceability hubs

Traceability hubs’ combination of improved supply chain visibility and collaboration tools speeds exceptions handling between players. Generative AI also plays a vital role.

It can automatically ingest, summarize, and compare the issue at hand with past issue patterns, pointing serialization operations staff to the problem quickly and letting them focus on resolving it.

The DSCSA requires that wholesalers quarantine products without valid accompanying data. A few minutes saved could be the difference between a product return and smooth sailing.

And across a given pharmaceutical supply chain, those sorts of savings could happen hundreds of times a day.

The pharmaceutical industry will never run on a single, shared IT platform, and that’s just as well. But for traceability to work for regulators and the industry, that fifth and highest level of serialization requires more than just networks.

Achieving the vision of end-to-end traceability with real-time issue resolution will take AI-driven traceability hubs based on open standards, available at reasonable cost, up and down the supply chain.

About the Author

Dennis Keilbach is SAP’s head of life sciences serialization product management.

Anil Suresh is SAP’s director of life sciences product management.