Real-Time Control Towers: From Visibility to Action

Published September 2026 | Author: Leading Minds Network 

Key Takeaways 

• One Screen, Total Control: Control towers are becoming decision platforms that unify shipments, compliance, and operations.
• Visibility Isn't Victory: Resilience requires clear ownership, escalation paths, and action plans, not just data.
• From Alerts to Action: Real-time IoT data enables intervention before disruptions become excursions.
• AI Accelerates, Humans Validate: AI enhances analysis and prediction, while people remain accountable for critical decisions.
• Prediction Beats Reaction: Future supply chains will identify and mitigate risks before failures occur.
• Better Together: Data sharing and collaboration can improve reliability, efficiency, and patient outcomes across the industry.

The pharmaceutical cold chain has entered a new era where visibility alone is no longer enough. During a recent expert panel session "Real-Time Control Towers and Exception Management," moderator Ryan Oppel, Chief Commercial Officer at Life Science Logistics, joined Jeff Lander of Moderna, Kunal Punjabi of DSV, and Juan Howlett of Direct Relief to explore how pharmaceutical organizations are transforming shipment monitoring into proactive decision-making and exception management utilizing control towers.

What became apparent was that the industry is moving beyond simply tracking shipments toward orchestrating entire supply chains through integrated control towers, predictive analytics, and increasingly sophisticated AI-powered tools.

Ryan Oppel opened the session with a reminder that resonated throughout the discussion. The industry is not managing pallets, data points, or transportation lanes. It is helping patients receive life-changing therapies. Drawing on personal experiences as both a biologic therapy patient and someone whose family has benefited from advanced medical treatments, Oppel emphasized that every shipment represents a human outcome.

That patient-centric perspective framed the conversation around why improved visibility and exception management matter.

The Evolution of the Control Tower

While “control tower" has become a common industry buzzword, Jeff Lander argued that many organizations still misunderstand what a true control tower should accomplish.

For Moderna, a control tower is not simply a dashboard. It is a single source of truth that consolidates shipment information, logistics provider data, IoT device feeds, and compliance records into one unified ecosystem.

Historically, manufacturers were forced to navigate multiple freight forwarder portals, separate temperature monitoring platforms, and disconnected data repositories. This fragmentation made it difficult to gain comprehensive visibility or extract meaningful insights from shipment data.

Lander explained that Moderna's vision is a "single pane of glass" that provides complete visibility across all shipments while ensuring ownership of operational and compliance data.

The panel agreed that end-to-end visibility has become a baseline expectation.

Pharmaceutical companies increasingly expect logistics partners to provide real-time insights, predictive intelligence, and operational guidance rather than merely executing transportation services.

As Kunal Punjabi noted, the industry has evolved beyond selecting providers solely on cost. Pharmaceutical companies now expect partners who understand GMP requirements, quality systems, regulatory expectations, and supply chain resilience.

Visibility Without Action Is Not Enough 

One of the session's most compelling themes was the distinction between visibility and control.
Many organizations have improved shipment visibility through sensors and monitoring technologies. However, panelists cautioned that visibility alone does not create value unless organizations have clear processes for intervention.

Punjabi highlighted one of the most common failure points in cold chain logistics: a lack of ownership when exceptions occur.

Organizations may receive alerts indicating an issue, but confusion often remains over who is responsible for responding. Is it the freight forwarder? The carrier? The manufacturer? The quality team?

Without predefined escalation paths, even the best technology can fail to prevent shipment disruptions.

Similarly, the panel warned about "information fatigue." As organizations collect increasing volumes of real-time data, teams risk being overwhelmed by alerts and notifications. Minor issues can consume attention while critical events go unnoticed.

The challenge moving forward is not acquiring more data. It is identifying the data that truly requires action.

Building a Proactive Risk Management Model 

According to Lander, effective exception management begins long before a shipment leaves a facility.

Organizations must establish strong foundations through validated transportation lanes, standardized operating procedures, packaging performance data, and route intelligence.
Once those foundations exist, real-time data can support predictive decision-making.

For example, if a shipment deviates from its planned route, organizations can quickly evaluate the impact using packaging performance models, weather conditions, and estimated transit times. Rather than reacting after an excursion occurs, teams can take preventative action while there is still time to save the shipment.

This shift from reactive monitoring to proactive intervention depends heavily on real-time IoT technologies.
Moderna, for example, integrates shipment-level monitoring with transportation and trucking data, receiving updates as frequently as every five minutes. This near real-time visibility enables teams to identify risks while corrective actions remain possible.

The panel agreed that future-ready organizations will focus less on reporting what happened and more on predicting what will happen next.

What Is AI's Growing Role in Cold Chain Operations? 

Artificial intelligence was a major focus of the discussion and generated differing viewpoints among the panelists.

Punjabi argued that human oversight will always remain essential because pharmaceutical logistics ultimately affects patient health and safety. While AI can identify temperature excursions, delays, and anomalies, decisions involving product release, quality investigations, CAPAs, and regulatory interpretation require human judgment.

Howlett echoed this perspective, emphasizing that AI should function as a decision-support tool rather than a decision-maker.

Lander offered a more aggressive vision for AI adoption.

He argued that properly trained AI systems can analyze vast datasets, identify patterns, and model risks far more effectively than humans. While he acknowledged the need for human oversight during validation and implementation phases, he expects AI to dramatically improve productivity and decision-making across pharmaceutical logistics.

The panel generally agreed that AI's most immediate value lies in automating repetitive tasks such as SOP development, reporting, documentation, analytics, and administrative workflows.
Rather than replacing humans, AI is expected to elevate human decision-making by reducing manual work and enhancing access to organizational knowledge.

What Will Success Look Like in Five Years? 

Looking ahead, the panel predicted a future where control towers move beyond monitoring and become predictive orchestration platforms.

Punjabi envisioned systems capable of forecasting disruptions before they occur. Rather than simply reporting delays, future platforms may identify recurring bottlenecks at specific airports, seasonal disruptions, or transportation risks and automatically recommend alternative routes before problems develop.

Howlett noted that organizations like Direct Relief are actively exploring how AI and predictive technologies can support better route planning, partner selection, and customs management.

Lander expanded the discussion further by advocating for greater industry-wide data sharing.
Many of the datasets required to optimize pharmaceutical logistics are not proprietary.

Transportation performance, service reliability, routing information, and delivery outcomes could be shared across organizations to improve decision-making for the entire industry.

In his view, the greatest opportunity is not merely improving visibility within individual companies but creating a collaborative ecosystem where organizations leverage shared insights to improve patient outcomes globally.