Clinical Supply Forecasting: How to Prevent Stockouts and Overage Across Multi-Site Trials

There are two ways a clinical supply chain can embarrass you. The first is a stockout — a patient arrives for a visit and the drug isn't there. The second is the quieter, more expensive one: at the end of the trial you destroy a warehouse full of unused, expired product. Both come from the same root cause. Both are forecasting failures.

In an industry where end-of-trial waste can reach 50–60% of the drug produced under the old "just-in-case" model, getting forecasting right isn't a nice-to-have — it's one of the biggest levers you have on both trial cost and trial risk. This guide walks through how clinical supply forecasting actually works, the methods that matter, and the practical steps to prevent stockouts and reduce overage across multi-site, multi-country trials.

What is clinical supply forecasting?

Clinical supply forecasting is the process of predicting how much investigational drug a trial will need, where it will be needed, and when — then translating that into a manufacturing, packaging, and distribution plan. It's the demand planning engine of the whole clinical supply chain.

The difficulty is that, unlike commercial demand, clinical demand is genuinely uncertain. You're forecasting against assumptions about how quickly patients will enroll, how they'll be randomized across treatment arms, how many will drop out, and whether the protocol will change. Small errors in those assumptions compound into either empty shelves or wasted product.

Why forecasting is so hard in multi-site trials

A single-site study is forgiving. A 60-site, 12-country trial is not. The complexity multiplies along several dimensions at once:

• Enrollment is unpredictable and uneven. Some sites activate fast and enroll quickly; others lag for months. You can't supply them as if they're identical.
• Randomization splits demand. Patients are assigned to different arms and doses, so total demand fractures into many smaller, harder-to-predict streams.
• Drop-out and titration move the target. Patients leave the study or change dose mid-stream, shifting demand after you've already committed supply.
• Lead times are long. Manufacturing, packaging, labeling, and QP/QA release take time, so you're committing to quantities long before you know real demand.
• Expiry and shelf life add a clock. Drug that sits too long expires — so over-supplying early just converts into waste later.
• Borders add friction. Import permits, customs, and country regulations mean supply positioned in the wrong region isn't easily redeployed.

Each of these is manageable on its own. Together, they're why forecasting is the discipline that most often decides whether a supply chain runs smoothly.

The core methods of clinical supply forecasting

There's no single "correct" forecast — there's a progression of methods, each more sophisticated than the last. The right approach depends on the size, complexity, and risk profile of your trial.

Deterministic forecasting

The simplest approach: take your enrollment plan, dosing, and visit schedule, and calculate expected demand as a straight-line estimate. It's quick and useful for early planning, but it assumes everything goes to plan — which it never does. On its own, it tends to push teams toward heavy buffers.

Simulation-based (stochastic) forecasting

A more realistic approach that models the uncertainty directly. By running many simulated versions of the trial — varying enrollment speed, drop-out, and randomization — you get a range of possible demand outcomes rather than a single guess. This lets you supply to a confidence level (for example, enough to avoid a stockout in 95% of scenarios) instead of padding blindly.

Risk-based supply optimization

The most advanced approach pairs simulation with optimization. Instead of asking "how much should we make?", it asks "what's the minimum supply and overage that keeps stockout risk acceptably low, at the lowest cost and waste?" Done well, this kind of optimization can deliver meaningful cost savings — industry analyses have reported overall supply-cost reductions in the range of 20–50%, and comparator-sourcing savings of 20–40%.

How to prevent stockouts and reduce overage: a practical approach

Methods are only useful if they're applied in a disciplined way. Here's the practical sequence we use with sponsors.

1. Start from the protocol, not a spreadsheet template. Visit schedule, dosing, number of arms, and the country/site footprint drive demand. Build the forecast from the actual study design.
2. Model uncertainty explicitly. Use simulation to capture enrollment variability, drop-out, and randomization rather than relying on a single deterministic number.
3. Set overage by risk, not by habit. Decide the stockout risk you're willing to accept and size buffers to that. A blanket "add 30%" rule almost always over- or under-supplies somewhere.
4. Position supply intelligently across depots. Where you stage drug matters as much as how much you make. Smart depot positioning reduces both stockout risk and the waste of stranded supply.
5. Let IRT/RTSM drive resupply. Configure your Interactive Response Technology to trigger site resupply based on real dispensing and inventory thresholds, so replenishment tracks reality.
6. Re-forecast continuously. Treat the forecast as a living model. Feed in actual enrollment and dispensing data and adjust — the early forecast is a starting point, not a commitment for the life of the trial.
7. Plan expiry and resupply campaigns together. Align sourcing and release frequency with shelf life so you're not replacing expiring kits more often than necessary.

The role of IRT and data in modern forecasting

Forecasting and IRT (also called RTSM) are two halves of the same system. The forecast decides how much drug to produce and where to position it; the IRT decides, in real time, when each site needs more. When the two are configured to work together — and fed clean, timely enrollment and dispensing data — you get a supply chain that adapts as the trial unfolds instead of one locked into day-one assumptions.

This is also where advanced analytics earn their place. Better data and modeling let teams shrink buffers safely, because they replace guesswork with quantified risk. The goal isn't to eliminate overage — some buffer is essential to protect patients — it's to know exactly how much you need and no more.

A note on cost: forecasting is a budget decision

It's easy to treat forecasting as a technical exercise, but every forecasting decision is a budget decision. Overage that's never used is money spent on manufacturing, storage, and eventual destruction. Stranded supply in the wrong country is capital you can't recover. A stockout that triggers a protocol deviation can compromise data you've spent millions to collect. Tightening your forecast is one of the few moves that reduces cost and risk at the same time.

Frequently asked questions

How do you forecast clinical trial drug supply? Start with the protocol — enrollment plan, visit schedule, dosing, arms, and site/country footprint. Calculate expected demand, then model the uncertainty (enrollment speed, drop-out, randomization) using simulation. Set overage based on an acceptable stockout risk, position supply across depots, and let IRT trigger resupply. Re-forecast continuously against real data.

What causes overage in clinical trials? Overage is buffer stock built in to protect against uncertain demand. It grows when teams use blanket buffers instead of risk-based sizing, when supply is positioned poorly across depots, or when short shelf life forces frequent expiry replacement. Modeling demand uncertainty directly is the way to keep it low without risking stockouts.

What is the difference between forecasting and IRT? Forecasting predicts total demand and plans production and depot positioning before and during the trial. IRT (Interactive Response Technology / RTSM) manages real-time randomization and triggers site-level resupply during the trial. They work best as an integrated system.

Can you reduce overage without risking stockouts? Yes. Using simulation and risk-based optimization, teams can supply to a defined confidence level — keeping enough buffer to protect patients while cutting the excess that turns into waste. This is exactly where modern forecasting outperforms the old "just-in-case" approach.

The bottom line

Stockouts and overage look like opposite problems, but they're the same problem seen from two sides: a forecast that didn't account for reality. Treat clinical supply forecasting as a continuous, risk-based, data-driven discipline — not a one-time calculation — and you protect your timeline, your data, and your budget all at once.

Want to know how much overage your trials are really carrying, and where it's hiding? Talk to a forecasting expert at Bensen Solutions. We help sponsors build data-driven forecasts that prevent stockouts, cut waste, and keep multi-site trials on track.