According to PwC analysis,1 Investor confidence in traditional pharmaceutical business models is eroding. Between 2018 and 2024, the median multiple of enterprise value to EBITDA for pharmaceutical companies fell from 13.6x to 11.5x, even as broader market multiples increased.
Pipeline productivity dominates profit forecasts, but operational failures are quietly eroding margins. A single temperature excursion can destroy a batch worth millions. Expedited freight costs between $10,000 and $15,000 per shipment. Quality surveys consume hundreds of skilled hours. These are not rare occurrences. These are recurring patterns that most businesses follow after the fact, but don’t have systems in place to prevent them.
The Execution Problem in Pharmaceutical Supply Chains
Scheduling software is designed to be mathematically precise. It balances inventory and models efficient routes. But he doesn’t always know if these plans are feasible or how to adapt when conditions change.
Cold chain monitoring is a good example. Many manufacturers are already deploying temperature loggers or Internet of Things (IoT) sensors. They see excursions in real time. When a temperature excursion occurs during transportation, transportation teams often receive the alert but lack immediate visibility into affected lots, their stability profiles, and where replacement inventory is located. By the time quality teams review excursion data, cross-reference lot numbers, check stability protocols, and determine whether the product is recoverable, the shipment may have already been delivered or destroyed.
Meanwhile, hospital systems waiting for these deliveries have already scheduled procedures based on this inventory. The result is expedited shipments, quality investigations that take hundreds of hours, and potential disruptions to patient care.
In most organizations, data fragmentation across silos prevents rapid, informed action.
Digital Twins: A Live Image of Inventory
A digital inventory twin creates a continually 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, output commitments and dock capacity, the twin flags potential disruptions days or weeks in advance. When a shipment carrying active pharmaceutical ingredients is delayed, the system identifies batches that cannot be manufactured on time and customer commitments that are now at risk. This happens before anyone misses a deadline.
The twin constantly ingests feeds from several sources. IoT condition monitoring devices provide real-time temperature and handling data. Transportation management systems report shipment status. ERP platforms provide inventory positions. Warehouse management systems share facility capacity and workforce information. Live supply chain network data captures what’s currently happening between carriers and partners.
Rather than simply aggregating information, the twin creates a continually updated model that reflects the real world.
When a temperature excursion occurs, the twin can immediately identify at-risk batches, customers waiting for those deliveries, and alternative inventory available elsewhere in the network. This may trigger a replacement order before the excursion becomes out of stock.
Digital twins also provide visibility into constraints that planning tools do not take into account. Current utilization of cold stores compared to theoretical capacity. Actual processing times instead of assumed averages. These nuances make the difference between an executable plan and one that fails in practice.
From visibility to orchestration
The control tower acts as an orchestration layer, transforming data into coordinated actions. The system can automatically reroute shipments, release inventory from another facility, or rearrange dock schedules to prioritize at-risk products. Every action is guided by guardrails. The stability data defines the tolerable excursion level. Handling requirements dictate acceptable interventions for each customer.
Consider a late pickup exception from a contract manufacturer producing organic products. A specialized AI agent detects the delay and cross-references the affected SKUs with temperature stability windows and customer delivery commitments. Specially designed AI agents can notify affected customers of revised ETAs, reserve expedited cold chain capacity with a backup carrier, or adjust the receiving facility dock schedule to account for delayed arrival. These agents operate within defined safeguards. They will not authorize chartered cargo without human approval, but can automatically execute pre-approved expediting protocols.
This orchestration occurs because alerts, inventory, transportation and installation data are all consolidated into a single system. This system is then complemented by specialized AI agents, each focused on one area. Instead of relying on staff to manually reconcile data from spreadsheets and emails, the orchestration layer connects the dots in real time.
Building the business case
PwC recommends that pharmaceutical companies keep their core expertise in-house while digitizing and automating as much as possible.
For supply chain managers, this starts by quantifying the total cost of preventable breakdowns, destroyed products, expedited freight, penalties, investigation time and the opportunity cost of crews stuck in firefighting mode. This baseline makes the case for automation.
Adoption does not require removing existing infrastructure. Control towers can ingest data in virtually any format, normalize it, and add the layer of intelligence that enables orchestration. Early adopters report measurable improvements within months. This can result in reduced shipping costs, fewer temperature excursions reaching customers, and quality teams spending less time on avoidable investigations.
The business case becomes clearer when companies quantify the total cost of reactive firefighting. This includes destroyed products and expedited freight, but also the opportunity cost of quality professionals tied up in post-mortem analyzes instead of continuous improvement.
Reliability as a profit lever
Each avoided excursion protects both product value and quality resources. Each shipment avoided improves margins. Every stock outage avoided protects service levels and customer confidence. Reducing these avoidable costs also reduces the need for excessive safety stocks, which improves working capital efficiency.
At a time when investors are questioning the sustainability of pharmaceutical business models, the reliability of their 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 at FourKites.
Reference
1. Next part Pharma 2025: the future is now. PwC. January 8, 2025,