Every few months, a new and unprecedented challenge threatens the global pharmaceutical supply chain. And while uncertainty affects all industries, the human cost of disruption in the drug supply chain is especially severe, because it can directly undermine patient care and jeopardize patient health and well-being.

The risk factors prevalent in recent years have become the “new normal,” and more precise, proactive measures are needed to mitigate and manage them. In response, pharmaceutical global distributors are increasingly employing machine-learning tools, particularly artificial intelligence, to strengthen supply chain resilience and safeguard patient access to essential medicines.

Not all AI applications are equal, however, and the strength of the foundation on which they’re built holds the key to their sustainability and success.

The Increasing Role of AI Capabilities

Today’s pharmaceutical supply chain is more complex and interconnected than ever before, and AI tools have emerged as a transformative technology to help spot or avert any potential disruptions. This enables distributors to flip the script, from simply reacting to problems to implementing predictive, proactive risk management. They can model various scenarios, deliver insights that enable faster, better decision-making across the supply chain, and provide tailored, intelligent recommendations for precise interventions. 

At the basic level, AI streamlines repetitive tasks in the supply chain, such as data entry, reporting and routine analysis, while generative AI, a capability that can predict outcomes, enables more advanced scenario planning. That said, while genAI in particular has shown immense promise within supply chain systems, much work remains to be done. Research conducted by Cencora suggests that, on its own, genAI’s ability to predict supply chain disruptions peaked at 70% given the data and context that could be provided. When it comes to supply chain management and patient care applications, the highest degree of precision and accuracy are needed.

There are clear, tangible benefits of integrating AI tools into supply chain management. AI-driven analytics can flag the earliest signs of supply constraints, enabling rapid interventions such as  rerouting shipments and adjusting inventory to maintain patient care continuity, even before a shortage is officially declared.

The Acceleration of Knowledge Graphs

To improve the accuracy and reliability of genAI, knowledge graphs can provide a consistent model of the pharmaceutical supply chain on which to build the model’s genAI-based predictions.  Knowledge graphs represent or “map” data across stakeholders, locations and other entities, and show the relationships between them in dynamic ways that produce more accurate, contextual insights and empower precision decision-making. The tool improves the accuracy of predictive models and enables nuanced analysis of the supply chain, tracking not only how products move, but also identifying risks all along the journey. 

While many companies are using AI and genAI to some degree, the introduction of knowledge graphs to enhance AI tools and machine learning capabilities across the supply chain is novel within the business sector, and sets a new standard of excellence. To date, this kind of technology has generally been found only on social media and streaming platforms. 

The benefits are tangible. By combining knowledge graphs with advanced AI, platforms can analyze historical sales data against external factors, such as market trends and weather patterns, to anticipate shortages, optimize inventory and ensure patients’ timely access to the medicines they need. It can also help develop faster delivery routes, by synthesizing real-time traffic and logistics data, and producing predictive maintenance schedules for supply chain equipment. 

While genAI supports scenario planning, enabling distributors to simulate how potential disruptions could play out in the real world, these insights are further enhanced when paired with knowledge graphs. Knowledge graphs reduce guesswork by mapping relationships between data points – like manufacturing delays and downstream pharmacy deliveries – to provide a holistic view that helps organizations make informed sourcing decisions and respond nimbly as conditions change.

To truly improve supply chain resilience, the real value comes from integrating knowledge graphs with advanced AI and genAI tools that detect patterns and vulnerabilities, opening new possibilities to reduce supply risks and minimize impacts on providers and patients.

Optimizing Processes and Infrastructure

Despite the promise of new technology-driven capabilities, sustainable change hinges on two timeless factors: efficient business processes and strong infrastructure. Distributors refine existing processes before implementing new AI tools or knowledge graphs. Wrinkles in how shipments are tracked could significantly limit the potential of any new technology.

One barrier to broader adoption of advanced analytics across the supply chain is the sheer size of the datasets. Analyzing this vast data often leads to inefficiencies; therefore, greater computational power is necessary.

And, despite the benefits, constructing and integrating knowledge graphs poses even greater challenges because it requires a complete overhaul of existing analytics, as well as significant time, investment and highly specialized expertise. Scaling these solutions poses its own challenges, and there’s always a risk that they’ll fail to deliver a strong return on investment.

Standardizing warehouse management systems and platforms first, thereby paving the way to pursue more efficient and effective uses of these tools, can ultimately enhance the entire system.

Getting Ahead of Drug Shortages

Drug shortages are caused by a variety of factors, but supply chain disruptions, manufacturing delays, demand spikes,and regulatory challenges are the primary causes. The U.S. Food and Drug Administration defines a drug shortage as a scenario in which the demand for a medication exceeds its available supply. That's a good framework for long-term shortages, but it fails to cover shorter-term bottlenecks that also seriously impact patient care. 

This disparity has led to the development of predictive models that capture and help solve both long-term and short-duration shortages. They integrate data from internal datasets, manufacturer reports, and regulatory updates with relevant external factors such as geopolitical events or weather patterns, to provide a more complete picture.

A prime example was when the FDA declared a shortage of Ursodiol tablets, which are used to treat inoperable gallstones. Advanced analytics, having identified several other periods of limited availability of the drug, made rapid response intervention possible, from rerouting shipments to adjusting inventory allocations.

Organizations across the global pharma supply chain are embracing and integrating advanced technologies into their processes and operations. While there are legitimate concerns over how AI could impact the industry as a whole, the technology has clear benefits. One is sharing best practices and collaboration among various health industry stakeholders on data-driven solutions. Most leaders recognize that supply chain resilience requires cooperation and teamwork, and AI is facilitating in new ways to connect manufacturers, logistics providers, regulators and technology partners.

This collaborative approach not only helps knowledge sharing for immediate problems, but also creates a continuous framework that helps hone processes and improve results. Real-world experience and customer collaboration help refine data-driven tools, which then support open communication and ever-more agile decision-making. As more distributors and industry players adopt AI, the collective ability to predict, respond to and even prevent disruptions grows stronger.

The Path Forward

The goal of applying AI in the pharmaceutical supply chain isn’t simply to improve efficiency, build better predictive modeling capabilities, or even make smarter decisions. It’s to ensure that patients and providers have reliable access to medications. While distributors’ work is often done behind the scenes, they play a critical role in achieving this ultimate aim through the increasing use of data-driven and predictive tools that anticipate and shape responses to risks.

As more distributors adopt AI, the pharmaceutical supply chain will become increasingly resilient, efficient and flexible. This journey will continue to pave the way for a future with fewer and less-severe supply interruptions and drug shortages — one in which patient care is prioritized even in the midst of uncertainty.

Kyle Pudenz is senior vice president of enterprise data & analytics at Cencora.