How AI Control Towers and Digital Twins Can Restore Pharmaceutical Profit Margins

December 15, 2025

AI-powered control towers and digital twins are emerging as essential tools to prevent costly supply chain failures and restore operational reliability.

According to PwC’s analysis,1 investor confidence in traditional pharmaceutical business models is eroding. Between 2018 and 2024, the median enterprise-value-to-EBITDA multiple for pharmaceutical companies declined from 13.6x to 11.5x, even as broader market multiples expanded.

Pipeline productivity dominates earnings calls, but operational failures quietly erode margins. A single temperature excursion can destroy a batch worth millions. Expedited freight costs $10,000–$15,000 per shipment. Quality investigations consume hundreds of skilled hours. These aren’t rare events. They’re recurring patterns that most companies track after the fact but lack the systems to prevent.

The execution problem in pharmaceutical supply chains

Planning software is designed to be mathematically precise. It balances inventory and models efficient routes. But it doesn’t always know whether those plans are feasible or how to adapt when conditions shift.

Cold chain monitoring is a good example. Many manufacturers already deploy temperature loggers or internet of things (IoT) sensors. They see excursions in real time. When a temperature excursion occurs during transit, transportation teams often receive the alert but lack immediate visibility into which lots are affected, their stability profiles, and where replacement inventory exists. By the time quality teams investigate the excursion data, cross-reference lot numbers, check stability protocols, and determine if the product is salvageable, the shipment may have already been delivered or destroyed.

Meanwhile, hospital systems expecting those deliveries have already scheduled procedures around that inventory. The result is expedited shipments, quality investigations consuming hundreds of hours, and potential patient care disruptions.

In most organizations, the fragmentation of data across silos prevents fast, informed action.

Digital twins: A live picture of inventory

A digital inventory twin creates a continuously updated view of inventory across the entire network. This includes facilities, in-transit shipments, and partner locations. More importantly, it projects forward. By tracking individual shipments against scheduled production runs, outbound commitments, and dock capacity, the twin flags potential disruptions days or weeks in advance. When an inbound shipment carrying active pharmaceutical ingredients gets delayed, the system identifies which batches can’t be manufactured on schedule and which customer commitments are now at risk. This happens before anyone misses a deadline.

The twin continuously ingests feeds from multiple sources. IoT condition monitoring devices provide real-time temperature and handling data. Transportation management systems report shipment status. Enterprise resource planning platforms supply stock positions. Warehouse management systems share facility capacity and labor information. Live supply chain network data captures what’s happening across carriers and partners right now.

Rather than simply aggregating information, the twin creates a continuously refreshed model that mirrors the real world.

When a temperature excursion occurs, the twin can immediately pinpoint which lots are at risk, which customers are expecting those deliveries, and what alternate inventory is available elsewhere in the network. It can trigger a replacement order before the excursion becomes a stockout.

Digital twins also provide visibility into constraints that planning tools don’t account for. Current cold storage utilization versus theoretical capacity. Real dock processing times instead of assumed averages. These nuances are the difference between an executable plan and one that breaks down in practice.

From visibility to orchestration

The control tower acts as an orchestration layer, turning data into coordinated action. The system can automatically reroute shipments, release inventory from an alternate facility, or resequence dock schedules to prioritize at-risk product. Every action is guided by guardrails. Stability data defines what level of excursion is tolerable. Handling requirements dictate which interventions are acceptable for each customer.

Consider a late pickup exception at a contract manufacturer producing biologics. A specialized AI agent detects the delay and cross-references the affected SKUs against temperature stability windows and customer delivery commitments. Purpose-built AI agents can notify the affected customers with revised ETAs, reserve expedited cold-chain capacity with a backup carrier, or adjust the receiving facility’s dock schedule to accommodate the delayed arrival. These agents operate within defined guardrails. They won’t authorize charter freight without human approval but can execute preapproved expedite protocols automatically.

This orchestration happens because alerts, inventory, transportation, and facility data all flow into a single system. That system is then augmented by specialized AI agents, each focused on a domain. Instead of relying on staff to manually reconcile data across spreadsheets and emails, the orchestration layer connects the dots in real time.

Building the business case

PwC recommends that pharma companies maintain core expertise in-house while digitizing and automating wherever possible.

For supply chain leaders, that begins with quantifying the full cost of preventable failures, destroyed product, expedited freight, penalties, investigation time, and the opportunity cost of teams stuck in firefighting mode. That baseline establishes the case for automation.

Adoption doesn’t require ripping out existing infrastructure. Control towers can ingest data in virtually any format, normalize it, and add the intelligence layer that enables orchestration. Early adopters are reporting measurable improvements within a few months. This can result in reduced expedite costs, fewer temperature excursions reaching customers, and quality teams spending less time on avoidable investigations.

The business case becomes clearer when companies quantify the full cost of reactive firefighting. This includes destroyed product and expedited freight, but also the opportunity cost of quality professionals tied up in post-mortems instead of continuous improvement.

Reliability as a profit lever

Every prevented excursion protects both product value and quality resources. Every avoided expedite improves margins. Every stockout avoided protects service levels and customer trust. Lowering these avoidable costs also reduces the need for excessive safety stock, which improves working capital efficiency.

At a time when investors are questioning the sustainability of pharmaceutical business models, reliable execution has become a direct lever for profitability and growth. AI-powered control towers and real-time digital twins are proving to be the practical tools that make this reliability possible.

About the Author

Shana Wray is a principal solutions architect and supply chain analyst with FourKites.

Reference

1. Next in Pharma 2025: The Future Is Now. PwC. January 8, 2025, https://www.pwc.com/us/en/industries/pharma-life-sciences/pharmaceutical-industry-trends.html