In new drug clinical trials, accurately forecasting drug usage is not only the core challenge of research projects but also a critical hurdle for sponsors aiming to reduce costs while enhancing quality. Recognized as an industry-wideconundrum, drug production and procurement forecasting challenges—when marred by delays, disruptions, or errors—inevitably lead to uncertainties in drug supply.

Struggling with:

Inaccurate drug forecasting?

Drug expiration or stockouts at clinical sites?

Irrational drug allocation and exorbitant emergency procurement costs?

Limited research budgets but demanding high efficiency?

Supply AIDrug Forecasting Model

Your antidote to supply chain uncertainty

Still unfamiliar with this game-changer?

Witness transformative outcomes in our flagship case study below

Precision Forecasting: Delivering Outperformance Beyond Expectations

As competition intensifies in autoimmune therapeutics, precision medication forecasting, dosage optimization, and intelligent allocation have becomeincreasinglycritical. A leading pharma's autoimmune program confronted clinical supply bottlenecks, seeking cost-intelligent clinical supply management without compromising trial integrity.

Supply AIswiftly garnered the attention of the sponsor. However, given its lack of prior utilization, there remained skepticism regarding its application outcomes.Ourteam effectively dispelled the sponsor’s doubts and concerns through rigorous methodology explanations, system demonstrations, and case studies. Ultimately, it successfully achieved a well-balanced integration of drug forecasting, cost control, andprotocol adherence, thereby driving measurablefinancialand operational efficiencies.

How reliable areSupply AI's predictive outputs?

How is drug demand determined when enrollment rates and volumes are uncertain?

CanSupply AIguide staged drug blinding allocations?

DoesSupply AIintegrate with IRT and WMS systems?

How doesSupply AImanage disparities in progress and demand across multiple sites?

How to operationalize calculated drug buffer quantities?

Must trials conclude to assess cost savings?

1.Precision Scaling, Management Efficiency Multiplied

Featuring an extended project timeline and a large cohort of subjects, this case study highlights significant uncertainties in enrollment rates and volumes across participating centers. Originally targeting a 30% drug buffer, the client achieved a mere 5% buffer throughSupply AI’s precision forecasting and scientific management, substantially enhancing project management efficiency.

2.Cost Optimization, Research Expenditure Slashed

Given the high unit cost of study medications, fluctuations in drug utilization directly impacted cost control. LeveragingSupply AI’s unparalleled accuracy and reliability in drug consumption management, the study proceeded smoothly while reducing drug-related expenses by millions of yuan compared to the sponsor’s initial projections.

Historical Data + Probabilistic Analytics + Dynamic Calibration

Supply AIForecasting Accuracy: 95%+

Average Drug Buffer: 9%-11%

Aggregate Drug Cost Savings: 16%

Per-Project Savings: Millions to Tens of Millions