How digital, AI-enabled supply chains can strengthen clinical trials

The entire supply chain is built on a forecast that is almost always wrong. Variability in patient characteristics and therapeutic response can result in dosing changes or longer courses of treatment, complicating estimates of how much drug each patient will ultimately require. This variability can cause either shortages or stock-outs and result in excess inventory, product expiry-driven waste or delays in trials.

A single Phase 3 trial may run in over 30 countries. Each country has its own complex and evolving import and export laws and specific product labeling requirements. A simple labeling error can lead to a shipment being quarantined in customs for weeks, halting a trial. In addition, sudden policy shifts on tariff or customs rules may disrupt the flow of goods. These constraints slow down the supply chain and limit flexibility. Any mistake can mean regulatory violations or trial holds.

Clinical trial supply chains often span the globe, and logistics orchestration is complex: maintaining cold-chain integrity for biologics (each batch is worth millions of dollars), planning transport routes amid customs clearances, real-time traceability of chain of custody and integrity and delivering the patient-centric drugs just in time to the right patients. Unforeseen events — weather delays, export holdups or a depot fridge failure — can derail the schedule or destroy the product. Furthermore, the new, patient-friendly model of decentralized clinical trials and the increasing diversity of personalized drugs shatter the old supply chain. Instead of shipping in bulk to large hospitals or clinics, the CTSC must manage “last-mile” delivery of individual, temperature-controlled kits directly to thousands of individual patients’ homes.

Unlike commercial manufacturing, which benefits from scale, clinical manufacturing deals with smaller batch sizes and a high mix of products (often unique formulations for each trial). Many pharma companies have limited dedicated capacity for clinical batches and must juggle these in between commercial production or rely on multi-product pilot plants. This can create scheduling hindrances. As trials scale from Phase 1 to 3, batch sizes and facility needs grow, making scale‑up and tech transfer risky and delay‑prone. Early‑phase capacity often cannot support Phase 3, requiring new sites that take time to qualify, and can postpone trial starts. Misalignment between clinical acceleration and manufacturing readiness frequently causes bottlenecks or excess inventory when trials pause or end early.

The above challenges result in billions of dollars in waste from expired or unused drugs. Studies indicate that on average about 70% of drugs made for a trial end up unused and expired.⁶ On the flip side, despite having large inventories overall, stock-outs still occur at local levels if the inventory isn’t in the right place at the right time. Maintaining the availability of comparator drugs and ancillary supply is also a challenge.

The CTSC is a fragmented network of stakeholders — the sponsor, the contract research organization (CRO), central depots and local trial sites. Data is typically siloed in separate, non-communicating systems. Clinical teams, manufacturing and supply chain might use different tools that don’t communicate with each other to bring end-to-end visibility. Manufacturing might be based on an old forecast while clinical teams have updated enrollment targets. These fragmented systems limit visibility and rely on human intervention, and a majority of time is spent coordinating and creating multiple sources of truth. There is a lack of an orchestrated end-to-end value chain from supplier to trial patient.

A major life sciences company has implemented advanced planning capabilities that enable harmonized processes that can link to inventory and enrollment data.⁷ This system provides improved reporting and data aggregation compared to manual processes and allows for advanced analytics, drill-downs, historical data sets and visualizations. By using this system, a supply planner can model multiple scenarios to help plan for potential study changes or create mitigation strategies that facilitate decision-making. It’s also able to evaluate ranges of various study parameters, such as enrollment rates, treatment durations and drug demand variability, to help identify risks and plan for mitigations.

A multinational pharmaceutical and diagnostics company enabled end-to-end visibility of their clinical supplies from planning and production to patient delivery by closely integrating their operations with their contract manufacturers and their third-party logistics providers. Through enablement of this solution, this organization was able to reduce their cycle times, reduce excess inventory and improve efficiency by digitizing information exchange and visibility rather than relying on manual information exchanges.⁸

A global pharmaceutical company redesigned its clinical‑trial procurement processes leveraging digital tools (advanced analytics, AI/ML, robotic process automation, digital‑twin planning, integrated e‑procurement) to manage sourcing of CROs, labs, comparators, ancillaries and logistics partners. This improved the cycle time for selecting and contracting vendors, increased supply continuity, refined risk‑sharing models and supported decentralized or hybrid study designs by accelerating procurement‑dependent steps.^{9, 10}

A global biopharmaceutical contract development and manufacturing organization deployed mid‑infrared spectroscopy (MIR) PAT sensors for real‑time in‑line monitoring of protein concentrations and buffer components during ultrafiltration and diafiltration, which provided instant visibility into critical downstream parameters.¹¹ The real‑time insights reduced deviations, improved purification yields and shortened process‑optimization cycles — accelerating tech‑transfer and GMP readiness for clinical‑trial lots.

A global pharmaceutical organization employed digital orchestration to manage temperature-sensitive logistics. The organization uses IoT sensors to track real-time conditions and advanced analytics to detect spoilage risks in the distribution of temperature-sensitive medicines, which is critical for clinical trials. The system generates alerts that enable organizations to take a proactive approach to reduce spoilage.¹² This information is also used by the upstream planning and inventory management processes to improve supply- and inventory-related decisions and reduce waste or shortages.

A leading pharmaceutical company engaged with global technology providers to leverage AI, blockchain and a hybrid cloud data solution across its mRNA clinical trials. The goal was to improve vaccine supply chain visibility and data sharing across partners, as well as optimize clinical trial visibility and shipment planning, to enable a more comprehensive understanding of trial operations and logistics.^{13, 14}

A leading pharma manufacturer’s COVID-19 vaccine clinical program leveraged a digital supply chain control-tower approach that integrated real-time data from enrollment, manufacturing, depots and logistics partners, enabling rapid scenario planning and dynamic reallocation of investigational product as country restrictions and demand shifted. This digital orchestration helped prevent site stock-outs and supported one of the fastest global clinical development timelines on record.^{15, 16, 17}

A global leader in cell and gene therapies developed a custom digital twin solution to efficiently manage the end-to-end treatment lifecycle for the organization’s clinical trials. This platform enabled the organization to onboard new patients, schedule appointments at apheresis centers for T-cell extraction and track the chain of identity and custody throughout the entire supply chain, driving seamless integration at every stage.¹⁸

Organizations should first assess their existing operating model and identify key business challenges, improvement areas and capability gaps. Then, to support modern clinical trials, organizations should define a future‑ready clinical trial supply chain operating model and blueprint with a clear execution path. This includes aligning on a shared North Star and value objectives, establishing the right operating foundations with humans at the center, and translating the vision into a focused transformation roadmap with clear, prioritized milestones across people, process, technology, systems and data — maintaining focus on value realization.

Strong governance is critical to effectively execute and achieve the expected vision and goals. Organizations should establish clear decision rights, key performance indicators, cross‑functional ownership and governance forums that oversee and manage the journey, including prioritization of initiatives, risk management and value tracking. This promotes alignment across clinical, supply chain, manufacturing and external partners while enabling faster issue resolution and disciplined execution.

Organizations can start achieving results rapidly by implementing quick-win opportunities. They can accomplish this by enhancing and streamlining their business processes internally as well as with their ecosystem partners by leveraging existing capabilities in advance of digitizing them.

Organizations should select the right digital solution and providers based on their roadmap and required digital capabilities. In recent years, supply chain digital solution offerings have exploded in the market. This makes it very difficult for organizations to architect and select the right set of solution providers. Oftentimes, this becomes trial and error, leading initiatives to fail. Each solution needs to be fit for purpose to satisfy a clear value objective and align with the overall transformation roadmap. Evaluations should holistically consider capabilities, cost, sustainability, innovation and fit to the enterprise architecture.

Successful transformation requires disciplined execution and a strong focus on value realization. Organizations should adopt an incremental implementation approach driven by use cases, embedding change management, capability enablement and performance measurement from day one. Continuous value tracking and feedback loops help position benefits to be realized, scaled and sustained over time. A future-proof operating model and a focused roadmap can help organizations prepare to transform their clinical trial supply chain into a proactive, digitally enabled engine that empowers and accelerates clinical developments.