How Clinical Supply Teams Pull Better Demand Signals

By Tom Walls, principal and founder, Axon Bridge Consulting

Understanding that demand signal quality drives clinical supply risk is only the first step. The next challenge is operationalizing that insight. Here, in Part 2 of our series, we focus on the specific inputs, questions, and decision frameworks clinical supply teams can use to actively pull better demand signals and make more confident material commitment decisions. [Editor's note: You can read Part 1 here.]

What Clinical Supply Needs From Clinical Operations

Effective demand pull starts with clarity about what information clinical supply requires. The standard enrollment forecast, a single number representing expected patients by quarter, is insufficient.

Instead, site-level enrollment trajectories with activation timelines is needed. Aggregate trial-level forecasts obscure site-by-site reality. Clinical supply needs enrollment broken down by site with activation status: Site A (five patients, currently enrolling), Site B (eight patients, pending ethics approval, expected week 6), Site C (12 patients, conditional on local regulatory clearance, 60% probability by week 12). This granularity allows clinical supply to model site-specific risk and assess whether delays create broader supply constraints. Examine screen failure assumptions by cohort with evidentiary basis. Screen failures consume clinical material and impact enrollment timelines.

Clinical supply needs to understand the screen failure rate assumption embedded in forecasts, how it was derived, and how it compares to historical performance. If the protocol has more stringent inclusion criteria than previous trials, is the screen failure assumption adjusted? If not, why not?

Perform protocol amendment risk assessment with supply impact visibility. Every amendment is a supply chain event. Clinical supply needs visibility into amendments under discussion, probability of adoption, expected timing, and preliminary supply impact assessment. This might include a monthly summary: amendments under consideration, rationale, probability (high/medium/low), and initial supply impact.

Consider patient dropout probability and timing patterns. Dropout affects inventory positioning differently depending on when it occurs. Early dropout primarily impacts manufacturing scheduling. Late dropout creates inventory obsolescence risk. Clinical operations should provide historical dropout data by treatment phase, allowing clinical supply to model dropout scenarios and make informed material commitment decisions.

How Supply Should Explore Enrollment Assumptions

Active demand pull requires clinical supply to help refine enrollment forecasts through structured dialogue. The Five Critical Questions framework provides a practical starting point for collaborative exploration:

• "What's driving this enrollment rate assumption?" Every enrollment forecast embeds an assumption about enrollment rate per site per month. Clinical supply can help strengthen the forecast by exploring the evidence base: "Our planning shows four patients per site per month. The last trial in this indication achieved two and a half. What factors support the higher rate?" This dialogue often reveals whether optimistic assumptions need adjustment or whether legitimate improvements justify the forecast. That distinction matters enormously for material planning.
• "What's your site activation confidence?" Clinical supply can help surface timeline risks: "The plan shows six sites active by week 12. What are the dependencies we should watch? Ethics approvals? Regulatory clearances? Investigator availability?" This conversation helps both teams understand where delays might occur and plan accordingly. Site activation delays cascade through material planning, so early visibility of risk factors allows proactive adjustment.
• "How are you modeling screen failures?" Protocol complexity directly impacts screen failure rates, and clinical supply can help ensure this is reflected in planning: "The protocol inclusion criteria were tightened in the last amendment. Historical screen failure was 40%. Should we adjust our assumptions?" This dialogue helps ensure enrollment plans account for protocol realities, leading to more accurate material commitments.
• "What's the enrollment risk if protocol amendment X is adopted?" Clinical supply can help make amendment risks explicit: "I understand the protocol team is discussing a fourth dosing cohort. If that moves forward, how does it affect the enrollment timeline? What's the probability of adoption?" This forward-looking dialogue prevents amendments from becoming surprises and allows both teams to plan for multiple scenarios.
• "What does enrollment look like if we're wrong?" Clinical supply can help create scenario discipline: "Let's explore the downside case together. If only three sites activate instead of six, how does that change the Q3 enrollment? This helps us understand the range of material commitments we might need." This creates shared understanding of forecast uncertainty and helps both teams make risk-informed decisions.

These questions position clinical supply as a planning partner. The goal is not to second-guess clinical operations' expertise but to bring supply chain perspective to enrollment planning. When framed collaboratively, these conversations strengthen forecasts and create shared ownership of material commitments: "We're committing significant material based on this plan. These questions help us work together to ensure those commitments are grounded in realistic assumptions that we both understand."

Interpreting Protocol Amendments Through A Supply Lens

Protocol amendments are routine in clinical trials. Clinical supply should treat them as supply chain events requiring systematic impact assessment before amendments are finalized.

Dosing changes directly impact material requirements. An amendment increasing dose from 100 ml to 150 ml increases material demand by 50%. Clinical supply should quantify the impact: additional vials required, manufacturing slots needed, inventory obsolescence risk, and financial implications.

Cohort additions introduce new patient populations with different enrollment timelines and supply requirements. Cohorts are often sequential rather than parallel, affecting manufacturing scheduling. Clinical supply should surface these dependencies explicitly. Testing requirement changes affect analytical capacity and sample material availability. These requirements typically originate from analytical development rather than clinical operations, but clinical supply planning must capture them since they directly compete for the same material as patient dosing. For ATMP products, analytical testing often uses the same material as patient dosing. An amendment doubling stability testing sample requirements reduces material available for patient dosing proportionally. Clinical supply should coordinate across both clinical and analytical demand streams to ensure total material requirements are understood.

Timing changes alter treatment duration and follow-up schedules, impacting inventory positioning and manufacturing capacity utilization. An amendment extending treatment from six to nine months increases per-patient material requirements by 50%. The supply impact assessment should be templated and completed for every protocol amendment under consideration. The assessment ensures supply constraints, cost implications, and timeline impacts are visible before amendments are finalized.

Deciding How Much Risk To Carry

The fundamental question is not "can we support the enrollment forecast?" but "how much confidence do we need before committing material, and what level of risk are we willing to carry?" This connects to the risk measurement, monitoring, and mitigation (R3M) approach: risk is quantified, monitored, and actively managed.

Risk tolerance depends on several factors: development stage (Phase 1 faces higher enrollment uncertainty but shorter lead times; Phase 3 has better predictability but longer lead times), material lead times relative to enrollment certainty windows, cost of shortage vs. cost of excess, and manufacturing flexibility.

The Green/Yellow/Red decision framework:

• Green light (commit material now): 80%+ enrollment confidence, lead times accommodate minimal uncertainty, manufacturing slots available, cost of shortage significantly exceeds cost of excess.
• Yellow light (hedge position): 50%-80% enrollment confidence, partial commitment preserves options, manufacturing has expedite capability at acceptable cost, balanced cost trade-offs. Commit 50% of forecasted material now and commit the remainder when confidence improves.
• Red light (delay commitment): Less than 50% enrollment confidence, significant obsolescence risk, manufacturing can expedite with moderate cost penalty. Wait for a better signal even if it means expedite costs later.

This framework is applied at the executive S&OP meeting. It works only if clinical supply has actively pulled better demand signals, explored enrollment assumptions collaboratively, and quantified cost trade-offs.

The objective is not to eliminate enrollment uncertainty but to make it visible and actionable. When clinical supply teams actively pull better demand signals and translate them into clear commitment choices, they help the organization decide what risk to take and when, rather than absorbing the consequences later through shortages, expediting, or write-offs.

About the Author

Tom Walls is principal and founder of Axon Bridge Consulting, specializing in advanced therapy medicinal products (ATMP) supply chain planning. With over 20 years of life sciences supply chain experience, he previously served as head of supply chain planning at Spark Therapeutics and has published in Cell & Gene Therapy Insights. He is the author of A Practical Guide to ATMP Supply Chain Planning and Orchestration Excellence and developed the R3M (Risk Measurement, Monitoring, and Mitigation) framework for ATMP supply chains. Contact: tom@axonbridgeconsulting.net.