Demand Planning is a Closed Loop, Not a Linear Task

A structured, four-step approach to demand forecasting is essential: define parameters, calculate demand, monitor design, and optimize continuously.

Whether planning for Phase 1 or scaling for a global Phase 3, the message is clear—assumptions change, and your plan should too.

Patient demand is only the beginning. True forecasting integrates operational variables (e.g., country-specific shipping lead times, depot seeding) and risk-based factors (e.g., over-enrollment, temperature excursions, IP losses). Especially in Phase 1, flexibility beats precision: never overcommit your first packaging run, and stay closely aligned with the clinical team.

From Static IRT to Predictive Models: Where Supply Planning is Headed

Traditional IRT strategies—based on static buffer values—can fall short in today’s dynamic trials. The conversation pointed toward the need for automated, data-driven resupply models that respond to actual demand behavior in real time.

Modern RTSM systems now support dynamic resupply calculations that blend predictable patient demand with probabilistic models. This “buffer intelligence” reduces waste and improves site-level inventory control—especially when recruitment rates fluctuate or titration schedules shift mid-study.

The concept of “forecasting the unpredictable” came to life through examples of systems that dynamically adjust safety stock based on correlated and uncorrelated patient-level demand variability.

What Global Trials Are Really Up Against

A review of real-world Phase 3 scenarios illustrated the volatility sponsors face: countries drop out, labeling requirements shift, import timelines change, and comparator shelf lives shorten. In one case study, the number of participating countries grew from 15 to 22 over just six months—forcing a complete recalibration of forecasts, depots, and resupply strategies mid-execution.

That volatility highlights the importance of agility in planning systems. IRTs that fail to adapt lead to stockouts, overages, or worse—risk to patient dosing continuity.

Actionable Tips for Clinical Supply Teams

Forecasting is no longer just about predicting kit counts. It’s about integrating supply chain thinking into the earliest moments of trial design—and maintaining a feedback loop throughout execution.

Here’s what smart teams are doing now:

• Collaborating cross-functionally with clinical ops early in the design phase.

• Embedding risk-based buffers in IRT settings, not just averages.

• Using real-time IRT data to recalibrate forecasts during recruitment.

• Understanding country-specific import timelines and regulatory changes as forecast variables—not afterthoughts.

Final Thoughts

Today’s clinical supply environment demands more than just good guesses—it requires responsive systems, resilient strategies, and informed collaboration. Whether you're overseeing a local study or orchestrating a global trial, investing in smarter IRT and forecasting frameworks isn’t a luxury—it’s a necessity.

At OnPoint Trial Supply, we help biotech teams implement forecasting frameworks and IRT strategies that scale with complexity—so you can focus on results, not resupply fires.

Let’s future-proof your clinical supply chain.