Pharmaceutical supply chains are facing unprecedented pressure. Expanding product portfolios, globally interconnected manufacturing networks and regulatory and geopolitical disruptions are making it harder for traditional planning and execution models to keep pace.
While pharmaceutical companies have invested heavily in digital transformation, much of that progress has remained confined within the four walls of the organization.
Planning systems and analytics tools may be more advanced, but true end-to-end visibility and coordination across external partners, such as contract development and manufacturing organizations (CDMOs), raw material suppliers, logistics providers and regulators, often remain out of reach.
In a recent webinar, two experts from Blue Yonder, Tiffany Brewer, Sr. Industry Advisory Director, Life Sciences, and Rochelle Brock-Smith, Senior Manager, Life Sciences Solutions and Industry Marketing, explored how AI-driven networks are helping pharma companies move from reactive responses to proactive, patient-centric decision-making.
The discussion looked at how AI agents and connected networks help unify fragmented data and improve collaboration among supply chain partners. Together, these capabilities support faster, more precise responses to emerging risks while protecting patient outcomes.
Why Digital Transformation Alone Is No Longer Enough
AI may dominate industry conversations, but technology adoption alone has not done much to resolve the structural challenges facing pharmaceutical supply chains.
Despite years of investment in digital tools, many organizations are discovering that efficiency gains inside the enterprise do not always translate into resilience across an external ecosystem.
“Rather than just jumping into a new technology for innovation’s sake, there really must be a clear purpose and a strategy in place, whether that is breaking down silos to better engage with your partner ecosystem, or using AI to proactively manage demand fluctuations or regulatory changes.”
— Rochelle Brock-Smith
Brewer explained that this critical gap remains even though pharmaceutical companies have largely completed digital transformations across planning systems, transportation management systems (TMS), warehouse management systems (WMS) and analytics platforms.
Pharmaceutical companies are largely seeing that they made these digital transformations within the four walls of their organization,” Brewer said. “But what’s missing is they haven’t made the transformation outside those four walls.” That gap becomes most visible when disruption originates beyond a company’s direct control.
Manufacturing delays at CDMOs, upstream material shortages, port congestion or regulatory changes can quickly make internally optimized plans obsolete. Without shared, real-time coordination across partners, supply chain teams are often forced to rely on manual workarounds, such as emails, spreadsheets and phone calls, to get back on track.
As pharmaceutical supply chains grow more global, outsourced and interdependent, this model is proving unsustainable. Closing the disconnect between internal optimization and external coordination is urging the industry toward network-based approaches that connect partners in real time and support faster, more informed responses to disruption.
Collaboration for Modern Pharmaceutical Supply Chains
For decades, pharmaceutical supply chains have relied on point-to-point integrations to connect manufacturers with suppliers, CMOs and logistics providers. While sufficient for basic information exchange, this model was never designed to support the scale, speed and complexity of today’s multi-tier, globally distributed networks.
As partner ecosystems expanded, point-to-point connections introduced friction, increased maintenance demands and limited collaboration to one-to-one relationships.
These limitations are becoming more pronounced as supply chains grow more interconnected and disruption more frequent. Traditional linear collaboration models struggle to keep pace when decision-making depends on fragmented data and disconnected workflows.
Brewer emphasized that collaboration itself is not new, but the tools supporting it have not evolved with the industry.
“We have people in supply chain organizations and across all of these partner organizations who are going by email, going by phone calls, sharing spreadsheets with each other,” she explained. “And once it reaches the supplier or once they have a chance to act on it, it’s no longer real-time data. It’s outdated data.”
She explained that a network-based approach creates a shared digital environment in which partners connect once and collaborate across the ecosystem. By standardizing and automating data sharing, organizations can respond more quickly and collectively to disruptions, such as port congestion or manufacturing delays, before impacts spread through the supply chain.
Managing Multiple Drug Modalities Through a Connected Network
Today’s pharmaceutical supply chains are no longer built around a single type of product. Instead, organizations are managing portfolios that span traditional therapies, biologics and personalized treatments, each placing different demands on manufacturing and distribution.
Traditional products are typically produced at scale with relatively predictable lead times and limited reliance on CMOs.
Biologics, by contrast, bring greater variability in yields, higher margins and a heavier dependence on contract manufacturing partners.
Personalized therapies introduce an additional layer of complexity, often operating under make-to-order models that require serialized tracking, tightly coordinated logistics and precise, on-time delivery.
Despite their differences, these modalities frequently draw on the same constrained resources, including raw materials, manufacturing capacity and logistics infrastructure.
When disruptions occur or demand shifts unexpectedly, balancing priorities across such a diverse portfolio becomes increasingly difficult.
A connected network helps organizations navigate these trade-offs by providing visibility across products, partners and processes, enabling more informed production and distribution decisions across modalities.
“In the end, the most important thing is patient outcomes,” Brewer said. “The network really allows that visibility to everything and allows the sifting through of all kinds of data to help make those decisions.”
Using Network Intelligence to Bring Precision to Risk Management
Risk management has long been difficult for pharmaceutical supply chains, especially when risk is evaluated only at a regional or portfolio level. In complex, multi-tier networks, this broad view can make it hard to see which disruptions actually matter. Network-enabled intelligence is changing that by allowing organizations to identify and assess risk with much greater precision.
By connecting planning, execution and orchestration data across partners, a networked approach brings risk into focus across the full supply chain. This includes collaboration with CMOs and suppliers during planning, as well as real-time coordination with logistics providers through shipment tracking and predicted ETAs. It also provides end-to-end visibility across inventory, cold chain and product flow to the patient.
Rather than viewing disruption in aggregate, organizations can understand how specific events affect individual products, shipments or patients.
“What pharmaceutical manufacturers really need to know is where that risk exists.”
— Tiffany Brewer
Brewer emphasized that network-level visibility allows supply chain teams to move beyond broad assessments and focus on what truly requires intervention. “Is it within a specific SKU? Is it within a specific batch?” she explained.
That precision enables more targeted action, helping organizations protect service levels, financial performance and, ultimately, patient care.
How AI Agents Impact Decision-Making in Pharmaceutical Supply Chains
Greater visibility into supply chain operations has long been a priority for pharmaceutical companies. However, visibility alone does not solve the challenges created by growing complexity and disruption. The shift lies in how AI changes the way decisions are identified, prioritized and acted upon.
Brewer distinguished AI agents from traditional analytics and machine learning tools by highlighting their role in proactive decision-making. Rather than producing static forecasts or after-the-fact alerts, AI agents continuously monitor supply chain data across suppliers, manufacturing operations, logistics networks and regulatory environments. They not only detect potential issues, but also surface recommended actions in real time.
“The agents are actually sitting there continuously watching what’s changing across suppliers, production, logistics, and they’re bringing decisions to the attention of planners, of management,” Brewer explained. “Instead of getting an alert after the fact, or saying, ‘Hey, this is something that might happen,’ the agents are able to say, ‘This is what you should do next.’”
In a real-world example from inventory management, Brewer described how AI agents help distinguish between low inventory that poses no immediate risk and shortages that threaten critical orders, allowing teams to act with confidence and clarity.
AI agents continuously process vast amounts of data, something humans simply cannot do. At the same time, planners and executives retain control over final decisions. The result is faster, more informed and more consistent responses to disruption.
From Disruption Response to Competitive Advantage
As pharmaceutical supply chains grow more complex, resilience is no longer about reacting faster. It’s about anticipating issues before they escalate.
Network-based platforms unify partners and apply AI-driven decision-making across the supply chain, replacing fragmented visibility with shared intelligence. This enables pharmaceutical companies to manage volatility more proactively and turn uncertainty into a competitive advantage.
Brewer emphasized that the future of pharmaceutical supply chains lies in clarity, precision and collaboration, supported by AI but guided by human judgment.
This article was created in collaboration with the sponsoring company and the Xtalks editorial team.
Join or login to leave a comment
JOIN LOGIN