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A site enrolls faster than expected for two consecutive months. Nobody adjusted the resupply schedule, because the schedule was built on an average enrollment rate calculated before the study started. By the time anyone notices, the site's investigational product inventory has dropped below what's needed to dose the next scheduled patient, and now there's a choice between delaying a visit or missing a dose neither of which is a decision anyone wanted to make reactively, in the middle of a study, because a number on a spreadsheet stopped matching reality weeks earlier.
That scenario gets labeled a supply chain failure, but the actual failure happened earlier and elsewhere: a fixed resupply schedule kept shipping on the calendar it was built on, while the site's real consumption pattern had already moved somewhere else.
Why Fixed Schedules Break Down Under Real Enrollment Patterns
A fixed resupply schedule ship every four weeks, or ship when a site places a manual request works fine as long as enrollment and dosing stay close to what was originally forecast. Clinical trials rarely stay that predictable. One site enrolls faster than projected. Another has a dosing regimen that changes with a protocol amendment.
A third experiences a screen-failure pattern that eats through kits without producing the enrollment the schedule assumed. Any of these shifts the actual burn rate away from the number the schedule was built on, and a fixed schedule has no way to notice that's happened it just keeps shipping on the calendar until someone manually checks and realizes the gap.
The risk this creates isn't abstract. A site running low on investigational product close to a scheduled dose isn't just an inventory inconvenience it can mean a missed or delayed dose for an enrolled patient, a protocol deviation that has to be documented and explained, and in trials with narrow dosing windows, a genuine safety and data integrity question about what happens to that patient's data in the analysis.
What Threshold Based, Usage Driven Resupply Actually Does
Rather than shipping on a calendar, a threshold-based resupply system continuously tracks each site's actual inventory level against a defined minimum threshold calculated from real enrollment and dosing activity not a static forecast set before the study began. When a site's stock crosses that threshold, the system generates a resupply order automatically, sized and timed to the site's actual consumption pattern rather than an assumption made months earlier.
The schedule adjusts itself as enrollment accelerates, slows, or shifts between sites, instead of requiring someone to notice the drift and manually recalculate.
Fixed Resupply Schedule vs. Manual Threshold Checks vs. Automated Threshold-Based Resupply
| Fixed Schedule | Manual Threshold Checks | Automated Threshold-Based Resupply | |
|---|---|---|---|
| Adjusts to real enrollment/dosing changes | No ships on the original calendar regardless | Only if someone actively checks and recalculates | Yes continuously, based on live inventory data |
| Risk of an unnoticed stockout | High, especially at fast-enrolling sites | Moderate depends on how often checks happen | Low orders trigger automatically at the threshold |
| Effort required to catch drift | None which is the problem | Ongoing manual review across every site | None the system monitors it continuously |
| Response time once a risk is detected | N/A usually discovered after the fact | Days, depending on review cadence | Immediate order generated the moment the threshold is crossed |
| Scales across many sites with different enrollment rates | Poorly one schedule doesn't fit all sites | Poorly manual checks don't scale | Well each site's threshold is tracked independently |
6 Questions to Ask About Your Current Resupply Process
- Is your resupply schedule based on a forecast made before the study started, or on current, actual usage?
- If a site's enrollment accelerated this month, would your resupply plan adjust automatically, or only if someone noticed?
- How would you find out today if a specific site is trending toward a stockout in three weeks?
- Has a dose ever been delayed or missed because a site ran low on investigational product?
- Does every site have the same resupply threshold, regardless of how differently they're enrolling or dosing?
- Who is responsible for catching a drift between forecast and actual usage — a person, or the system?
If more than one of these points to "we'd find out after the fact," the resupply process is running on a calendar instead of on data.
How Cloudbyz RTSM Handles This
Cloudbyz RTSM tracks site-level investigational product inventory in real time and generates resupply orders automatically once a site's stock crosses its defined minimum threshold calculated from actual enrollment and dosing activity at that specific site, not a study-wide average set before enrollment began. Because thresholds are tracked independently per site, a fast-enrolling site and a slow-enrolling site don't share the same static schedule; each gets resupplied according to its own real consumption pattern. Kit dispensing, lot and expiration tracking, and cold-chain monitoring for temperature-sensitive products all run on the same platform, so a resupply decision reflects the full picture of what's actually usable at a site, not just a raw unit count.
For Clinical Operations and CDM teams, that turns stockout risk from something discovered during a site call into something the system is already watching, site by site, every day the study runs.
See This Against Your Own Enrollment Data
If your resupply process is still running on a fixed schedule set before your study's actual enrollment pattern emerged, it's worth seeing what threshold-based resupply looks like against your real site data. Book a demo with Cloudbyz team.
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