In an Xtalks webinar, Blue Yonder’s Simon Bowes discussed how better data, faster planning and connected networks can help life sciences companies manage supply chain disruption.
Recent supply disruptions have shown how quickly life sciences supply chain issues can reach the point of care.
In 2023, shortages of cisplatin and carboplatin meant some cancer teams had to rethink how to use limited supplies of two widely used chemotherapy drugs. The Society of Gynecologic Oncology issued guidance on how to conserve and allocate the drugs during the shortage. In 2024, Hurricane Helene disrupted Baxter’s North Cove facility in North Carolina, a major US source of IV fluids and peritoneal dialysis solutions, leading some hospitals to limit or delay elective procedures.
A supply chain delay is not always a transportation problem. Delays often begin long before a product is on a truck, plane or warehouse shelf. Forecasting gaps, quality issues, partner constraints, inventory decisions and slow handoffs between teams can all shape whether products are available when and where they are needed.
In a recent Xtalks webinar, Simon Bowes, Corporate Vice President, Industry Strategy – Manufacturing at Blue Yonder, discussed how life sciences companies can reduce delays by strengthening visibility, planning and execution across the network.
The challenge, Simon explained, comes down to how quickly teams can identify a problem, understand its ripple effects and decide what to do next.
Simon suggested the issue is more of a network-wide challenge and not a problem any one company can solve alone.
“We’re no longer independent. We’re all running end-to-end supply chains, and the service levels that we offer our customers are really dependent on the whole of the supply chain.”
— Simon Bowes, Corporate Vice President, Industry Strategy – Manufacturing, Blue Yonder
Why Forecasting Still Sets the Tone
Forecasting has always shaped supply chain performance, but in life sciences, the job is becoming more complex.
Simon pointed to several factors that can make demand harder to predict, including changes in disease prevalence, new treatments, clinical trial activity and the shift toward more individualized patient care. These variables can make it harder for companies to know where demand will come from, how quickly it will build and what level of supply will be needed.
“As we all know, the quality of your supply chain performance really is driven by how accurately you can predict the future,” Simon said.
A more accurate forecast cannot stop every disruption. It can, however, give teams more confidence in their plans. Without that confidence, companies may add extra inventory, build in longer lead times or rely on manual workarounds.
Those measures may protect service levels in the short term, but they can also add cost, increase working capital and build more delay into the system.
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The Problem Is Seeing the Delay Too Late
Life sciences supply chains span a wide network of partners. A manufacturer may depend on suppliers, contract manufacturing organizations (CMOs), contract development and manufacturing organizations (CDMOs), logistics providers, warehouses and distributors to get products to market.
As this network grows, spotting a delay early becomes more difficult.
Simon said many companies still struggle to gain real-time visibility into inventory held by external partners, partner capacity, batch release timelines, cold chain movement and transportation constraints. When visibility is limited to what happens inside a company’s own four walls, a problem originating elsewhere can quickly become costly.
“Having a lack of visibility there can really hurt.”
— Simon Bowes, referring to supplier and contract manufacturing relationships
Teams also need to know what has been promised, where bottlenecks are forming and what options remain if a supplier, site or route cannot deliver as planned.
With a clearer view of the full network, teams can respond before delays reach customers, hospitals or patients.
When Monthly Planning Is Too Slow
Traditional sales and operations planning (S&OP) remains an important business process, but monthly planning cycles can be too slow for modern disruptions.
If demand changes, capacity shifts or a shortage appears, teams may not be able to wait until the next planning cycle to respond. Sales and operations execution (S&OE) is becoming more relevant in this context.
Unlike longer-term planning, S&OE focuses on the near term, helping teams make decisions about supply constraints, stock-out risks, changing customer demand and transportation issues in days or weeks rather than months.
Simon said companies are increasingly looking at how to move from monthly planning to daily, weekly or near-continuous planning. The shift also depends on trusted data and processes that enable teams to act when capacity, demand or supply changes in the near term.
Siloed Systems Can Create Delays Before a Disruption Even Happens
Many delays are made worse by the way companies are organized. Planning, manufacturing, quality, warehousing, transportation and customer service teams may each work with different systems, metrics and assumptions.
In life sciences, this fragmentation can be especially challenging because service levels are often tied to patient needs.
“We inherently, particularly in the life sciences industry, need to be providing 100% service level in many cases,” Simon said. “We’ve got patients that are relying on us to deliver products, or there are patients in hospitals waiting to have an operation that need our devices.”
To protect these service levels, siloed teams may create their own buffers. One team may order more material than needed. Another may build longer lead times into the plan. Another may move inventory closer to the point of demand earlier than necessary.
Each decision may make sense on its own. These buffers can increase cost, reduce flexibility and create new inefficiencies.
During the webinar’s Q&A, Simon added that breaking down silos requires both better technology and change management. A common data foundation can help teams see the same information, but companies also need to move away from old, batch-based habits and rethink how planning and execution work together.
More Inventory Does Not Always Mean More Protection
Inventory is often treated as protection against uncertainty. But Simon noted that more inventory does not always mean better service.
In some cases, companies may carry plenty of stock, but it may be in the wrong place. If a product is pushed too far downstream too early, it can become tied to a specific market, language label or channel before actual demand is clear.
Life sciences products may also require country-specific labeling, packaging or other customization.
Simon discussed the role of risk pooling, which helps companies decide where inventory should sit across the network.
Risk pooling can help companies decide where inventory should sit across the network, keeping stock available without duplicating it across every channel. In some cases, holding inventory further upstream can preserve flexibility for later labeling, packaging or market-specific customization.
AI’s Role: Helping Teams Find the Right Fire to Put Out
While interest in AI is high, Simon cautioned that many pilots fail to deliver real value unless they are tied directly to reliable supply chain data and existing processes. Instead of adopting AI for its own sake, companies should focus on where it can support better, faster decisions.
“AI is still probably the greatest opportunity we’ve got to really improve our overall performance in the supply chain and to be able to reduce delays in the first place, and also to be able to react to the delay.”
— Simon Bowes, Corporate Vice President, Industry Strategy – Manufacturing, Blue Yonder
Planners often spend significant time identifying disconnects and exceptions across the network. Integrated AI can help teams prioritize which issues matter most and suggest ways to respond.
When an attendee asked about agentic AI, Simon highlighted how a shipment’s due date can be misleading. A one-day delay might look urgent, but if it only affects safety stock, it may not require immediate attention. In contrast, a shortage several weeks away could be more important if it threatens a critical customer order, tender commitment or patient-facing supply need.
AI agents can also recommend specific fixes and may eventually handle routine issues automatically by learning from how human planners have solved similar problems in the past. Users would still be able to review or reverse changes.
Real-Time Synchronization Means Seeing the Whole Impact Faster
In response to an audience question, Simon outlined the difference between real-time synchronization and faster batch processing.
If a customer asks to double an order, the answer is not simply whether there is enough manufacturing capacity. The company also needs to know how that change would affect other demand, warehouse space, available labor, transportation and existing service commitments.
In a traditional system, those questions may move slowly from one department or system to another. In a more synchronized supply chain, teams can evaluate scenarios faster and understand how one decision affects the broader network. A demand spike, supplier delay or transportation issue can then be assessed before the disruption spreads.
More than speed, real-time synchronization allows teams to test a change across capacity, warehousing, labor, transportation and existing commitments before acting.
Simon closed the presentation by bringing the discussion back to people, process and technology.
Technology can help connect supply chain data across planning, transportation, warehouse management and partner networks. But as Simon cautioned, “it’s no good just providing technology and hoping.”
This article was created in collaboration with the sponsoring company and the Xtalks editorial team.
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