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Improving pharma supply chain visibility with AI technology
AI-driven technology can enhance pharmaceutical supply chains by improving visibility, reducing inefficiencies and predicting shortages for better inventory management.
Supply chain visibility remains a common pain point in the pharmaceutical sector. Despite years of digital transformation, many organizations continue to struggle with locating, tracking and forecasting the movement of medications and other supplies across networks.
This lack of real-time transparency makes the system vulnerable to inefficiencies, duplication and expensive shortages that can disrupt the entire distribution chain.
The problem stems from data silos and legacy inventory systems that weren't designed for today's scale or complexity, experts say.
"Pharmacies have historically been myopic when it comes to inventory management," Valerie Bandy, Vice President of Pharmacy Solutions at supply chain management software company Tecsys, said in an interview. "They often focus on what's at the central location, maybe what's at satellite sites, but miss so much in between."
The challenge of fragmented systems also affects manufacturers and wholesalers, but Bandy suggests that this is where AI can step in.
The need for end-to-end visibility
The biggest blind spot in pharmaceutical supply chain management is its fragmented view of inventory across systems, Bandy pointed out.
Take, for example, pharmacy support areas such as IV rooms, storage and overflow rooms, hazardous drug zones, and compounding areas. "These places often aren't well managed," she admitted.
Without access to real-time inventory data, health systems risk falling into the trap of ordering products that are already sitting in their storage rooms, creating costly redundancies.
Using end-to-end visibility capabilities, AI-powered technology can provide organizations with a real-time snapshot of what is happening in these previously overlooked areas.
"End-to-end visibility means seeing everything -- not just drugs but also sterile gowns, gloves, syringes, needles and general supplies," Bandy explained. "It means knowing what's sitting everywhere across the system."
AI can also connect pharmacy inventory data with manufacturer systems to generate real-time demand insights, replacing reactive ordering with predictive forecasting.
Bandy recalled a time when a health system she worked for struggled with inventory management across multiple buildings. Staff routinely placed new orders without realizing identical products were already stockpiled in secondary storage locations.
This visibility gap can have a ripple effect that extends further into the supply chain. When health systems and distributors make purchasing decisions based solely on what they can see on a surface level, manufacturers receive unreliable demand information.
"Manufacturers don't know what they don't know. They see orders for 20 boxes every month and plan production accordingly," she said. "With limited visibility into downstream pharmacy inventory, they can't see those 60 boxes sitting in a forgotten storage room."
When excess inventory is eventually discovered and ordering abruptly stops, it creates instability throughout the supply chain.
Consequently, this impacts the ability of manufacturers and distributors to forecast, plan production and allocate resources effectively where needed.
True end-to-end visibility, she added, "starts with full systems integration." That means connecting with wholesalers, third-party vendors, Drug Supply Chain Security Act systems, 340B programs and electronic health records.
Once data flows seamlessly, all stakeholders can access a shared, comprehensive dataset to guide business decisions.
"The key is moving from siloed, reactive management to integrated, predictive operations," she explained. "AI makes this possible by processing vast amounts of data quickly and identifying patterns humans would miss."
Optimizing supply chain outcomes with AI
Drug shortages remain a recurring global issue, with data pointing to a steep upward trend in recent years.
"Shortages are the bane of everyone's existence in medication procurement," Bandy stressed. "At any given time, 200–300 drugs face shortages. And it's never as simple as just switching to an alternative."
Manufacturing quality issues are the leading cause of drug shortages, according to the FDA. Other contributing factors include production delays, supply chain disruptions affecting raw materials and components, unexpected demand spikes and product discontinuations
However, during these supply chain disruptions, AI can predict shortages and provide immediate actionable intelligence using predictive analytics.
By deploying machine learning and statistical models, AI-powered analytics can analyze historical shortage data, distributor stock levels, generic drug availability and even weather patterns to spot potential disruptions before they happen.
"Imagine AI analyzing weather patterns to spot a hurricane approaching a manufacturing plant," Bandy posed. "It could show you current inventory levels, calculate days on hand based on recent usage, identify alternatives and check if those drugs have shortage histories."
This kind of insight can allow organizations to make faster, data-driven decisions and prevent shortages.
"AI can quickly identify where drugs sit across a health system, calculate burn rates and recommend redistribution," she continued. "It might say, 'move X volume from your slow-moving main pharmacy to the busy outpatient area.'"
AI also assists in making complex, strategic choices, including determining whether to insource or outsource, benchmarking performance against other locations, making staffing decisions and optimizing contracts.
Bandy shared vaccine contracts as another use case since no single manufacturer produces all vaccines.
"AI can analyze tier volumes, patient types and competitive offerings to find the best solution," Bandy suggested.
Increasing efficiency and competitive growth
From a company standpoint, the advantage of adopting AI-driven supply chain transparency and inventory integration is apparent in its potential to improve efficiency.
"The primary savings come from efficiency gains," Bandy stated. "Tasks that took hours for humans now take seconds with AI. Staff can focus on strategic initiatives and patient care instead of manual inventory management."
The primary focus should be on recognizing high-burden, time-consuming tasks that AI can manage, Bandy suggested.
This not only frees up staff for more valuable work but also enhances accuracy and cuts costs associated with expired medications, duplicate orders and emergency purchases during shortages.
"AI-driven transparency enables the prediction of shortage risks, supports the guarantee of supply for customers and empowers companies to proactively manage drug availability rather than react to problems," Bandy reiterated.
She added that companies investing in these systems gain a long-term competitive edge over those that aren't.
"When pharma companies help customers implement AI-driven supply chain systems, they become a strategic partner rather than just a drug supplier," she noted. "That's the real competitive advantage in today's healthcare environment."
Alivia Kaylor is a scientist and the senior site editor of Pharma Life Sciences.
