Clinical supply planning has always had to manage uncertainty. A study begins with assumptions about enrollment, site activation, country participation, visit schedules, dose requirements, expiry, and patient retention. Those assumptions are necessary, but they are still assumptions.
For a long time, one of the most common ways to protect against uncertainty was to build in more buffer. More inventory. More shipment. More stock at the site. More supply available “just in case.” Clinical trial supply literature has long recognized the tension between maintaining enough safety stock for patient continuity and avoiding unnecessary inventory, waste, and cost (Peterson et al., 2004).
That approach can protect against shortage, but it can also create waste, cost, and operational noise. As studies became more global, more targeted, more adaptive, and more supply-constrained, the limits of static planning became harder to ignore. Supply planning needed to move closer to what was actually happening in the study.
The 2020 inflection point
Direct-to-patient models were not invented in 2020, but the pandemic made their importance much more visible. When patients could not reliably travel to sites, study teams had to find ways to keep treatment moving while protecting participants and maintaining oversight. During the COVID-19 pandemic, European guidance addressed changes to investigational medicinal product distribution, including direct shipment to participants under defined conditions (European Commission et al., 2022).
That shift exposed a broader truth about clinical supply. The route from depot to site to patient could no longer be assumed as the only operating model. Supply decisions had to account for patient access, site restrictions, shipment timing, accountability, temperature requirements, and the practical reality of keeping a study running under changing conditions. European COVID-19 guidance specifically highlighted practical considerations such as storage at the participant’s home, transit stability, safe custody, accountability, compliance, and temperature control (European Commission et al., 2022).
The lesson was not simply that Direct to Patient was useful. The lesson was that supply models need to be able to respond when trial reality changes.
What changed for supply planning
Clinical supply planning is difficult because the plan is built before the study has fully revealed itself. Enrollment may be slower than expected in one country and faster in another. Sites may activate later than planned. A cohort may open, pause, or close. A dose may change. Expiry may become a bigger constraint than expected. A region may need more supply while another holds inventory that will not be used.
These changes do not happen in isolation. Enrollment affects dispensing. Dispensing affects inventory. Inventory affects resupply. Cohort movement affects demand. Expiry affects what can be used. Country activation affects where stock is needed. Patient access models affect how treatment reaches the participant.
That is why static forecasting can become disconnected from the study. A forecast built only on initial assumptions may remain mathematically tidy while the live trial has already moved somewhere else.
What the live study changes
Modern trial designs make supply behavior harder to predict. Targeted studies may enroll smaller, less evenly distributed patient groups. Biomarker-defined cohorts may create uneven demand across sites and countries. Early-phase oncology studies may change dose levels or expand cohorts based on emerging data. Adaptive designs may open or close treatment arms. Combination studies may require multiple products to be available together.
In these settings, supply risk is not only about having enough product overall. It is about having the right product, in the right place, at the right time, under the right conditions.
This is where the old “just in case” model becomes less attractive. Over-buffering may reduce one kind of risk, but it can increase others: wasted product, unnecessary shipments, expiry exposure, storage burden, reconciliation effort, and poor visibility into what supply is actually needed.
The planning challenge is no longer only how much supply to make. It is how quickly the plan can respond when the study changes.
RTSM impact
RTSM sits close to many of the signals that shape supply demand: randomization, dispensing, site inventory, patient activity, cohort status, country participation, and resupply behavior. If forecasting is disconnected from those signals, supply planning depends too heavily on assumptions and manual reconciliation.
The stronger model is a feedback loop. Live study activity should inform supply planning, and supply planning should remain aligned with the way the study is actually being executed. Simulation work on clinical trial supply has also shown the value of using real-time study data for mid-study monitoring and supply optimization (Peterson et al., 2004).
If enrollment accelerates in one region, supply assumptions should reflect that. If a cohort pauses, demand should not continue as though it is still active. If a dose level expands, forecasting needs to account for the new expected usage. If expiry risk increases, the team needs visibility early enough to act before supply becomes unusable.
This is not about replacing planning judgment. It is about giving supply teams better operational context. Forecasting becomes more useful when it is connected to the same study behaviors that drive actual demand.
The plan has to follow the study
The 2020 acceleration of direct-to-patient models made supply flexibility more visible, but the larger shift is broader than DTP. Clinical supply can no longer be treated as a static plan created before the trial encounters reality. Subsequent research on direct-to-participant investigational medicinal product supply in Europe identified multiple DtP models and highlighted the practical, regulatory, and oversight considerations involved in implementing them (de Jong et al., 2023).
Modern supply planning has to respond to movement: patient movement, cohort movement, dose movement, country movement, and supply movement. The more targeted or adaptive the study becomes, the more important that connection becomes.
For RTSM, the supply question is direct: what is happening in the live study that should change the supply plan?
That is the operational translation required when modern trial execution depends on both continuity and control.
References
de Jong, A. J., Santa-Ana-Tellez, Y., Zuidgeest, M. G. P., et al. (2023). Direct-to-participant investigational medicinal product supply in clinical trials in Europe: Exploring the experiences of sponsors, site staff and couriers. British Journal of Clinical Pharmacology, 89(12), 3512-3522. https://doi.org/10.1111/bcp.15850
European Commission, European Medicines Agency, & Heads of Medicines Agencies. (2022). Guidance on the management of clinical trials during the COVID-19 pandemic, version 5.
Peterson, M., Byrom, B., Dowlman, N., & McEntegart, D. (2004). Optimizing clinical trial supply requirements: Simulation of computer-controlled supply chain management. Clinical Trials, 1(4), 399-412. https://doi.org/10.1191/1740774504cn037oa