Two themes ran through this year's LogiPharma: agility and resilience. Not as aspirations — those have been on slides for years — but as live questions the industry hasn't yet answered convincingly.
In separate sessions, Mohamed Nouh examined why supply chains are still shaped by assumptions built for a different era, while Richard Ettl and CSL's Robin Doppleb looked at what happens when disruption arrives and systems have to respond in real time. They also looked at AI's role today and where it could help bridge the gap. The conversations were distinct, but they both pointed to a consistent challenge: visibility has improved, but outcomes haven't kept pace.
A recent WBR survey of 120 senior pharma and logistics leaders puts numbers to what many already sense. 92% reported an increase in loss, theft, and temperature excursions. Yet only 18% of pharma respondents and 35% of forwarders see meaningful improvement in supply chain performance over the past decade.
At LogiPharma, this showed up in how the audience assessed their own readiness. Most landed somewhere in the middle: capable, but not confident they could respond effectively when things went wrong in transit. Investment has increased. Tools are in place. But performance isn't moving at the same rate.
Much of that gap concentrates in the moments that are hardest to control: shipments already in motion, handoffs between carriers, windows where timing becomes uncertain and options narrow.
Part of the reason those moments are so difficult is structural. Over time, supply chains have accumulated layers of controls, buffers, and dependencies — each added to manage a specific risk, each making the system a little more rigid and process-heavy. The result is a network that performs well when conditions match the plan, and struggles when conditions change and speed is of the essence.
Nouh's session pushed back on this design logic. Rather than adding more controls, more processes, the focus should shift to understanding where risk actually occurs at the lane and product level — using reliable data and predictive analytics to shape routes, protection, and interventions accordingly. To stop protecting the process and start protecting the product.
The goal is a network that doesn't require every step to perform perfectly, because it's been built with flexibility in mind.
The value of that flexibility becomes visible when disruption hits. Shipments can move across air, land, or ocean without being locked to a single path. Alternative routes are already understood, not improvised under pressure. And decisions get made early enough to actually change the outcome rather than document it.
This is what cold chain agility looks like in practice. It’s not a capability that gets switched on, but a structural property of how the network is designed before anything goes wrong.
The role of digital tools, and AI specifically, came up repeatedly across the conference. During his session, Ettl put it directly to the audience: had AI helped them navigate recent disruption? Only 6% said that it had.
That response reflects a more fundamental issue. AI is only as useful as the data it's built on. Incomplete lane data, unvalidated routes, inputs fragmented across systems — these don't get fixed by a better model. The output reflects the foundation.
Where that foundation is solid, AI’s role becomes clearer. It can process large volumes of network data, surface patterns that wouldn't otherwise be visible, and support faster decisions about what's actually happening across a shipment. From there, predictive and even autonomous interventions become genuinely possible. But the sequence matters: data quality and network structure first, AI capability second.
Across both discussions and the entire conference, one point was consistent. The industry has data. Acting on it remains the constraint.
Cold chain agility is not introduced through a single capability. It emerges when decisions about protection, supply chain design, and interventions are aligned.
At SkyCell, this is where our focus sits. Combining physical protection, network visibility, lane risk intelligence, and predictive, autonomous interventions to drive a more agile cold chain that actually protects products.