Clinical supply forecasting & risk-based optimization: What’s the difference?

As clinical trials grow in complexity, the pressure on clinical trial supply chain management has never been higher. While the goal of delivering the right medication to the right patient at the right time remains constant, the methods used to achieve it are evolving. And not all solutions tackle these challenges the same way. The landscape is primarily defined by two technologies: clinical supply forecasting and risk-based optimization. In this article, we break down the critical differences between these two approaches and identify which solution fits your specific trial phase and complexity.

What is clinical supply forecasting?

Clinical supply forecasting tools aim to predict patient demand in a clinical trial. The outputs are average demand forecasts that clinical trial supply teams use to create their supply plans.

Clinical supply forecasting tools are typically software products that are either built into or integrated with other trial management technologies, such as supply or manufacturing planning.

At their core these tools are complex calculators. They process inputs like anticipated recruitment, titrations, drug expiration, and desired overage through an algorithm in order to produce a forecast.

Clinical supply forecasting tools are very good at working with known variables and producing more accurate forecasts then any spreadsheet will ever allow. However, they’re not intelligent enough to precisely factor in all levels of uncertainty.

Because they don’t consider all this uncertainty, forecasting tools have two main drawbacks.

1. These tools don’t calculate the risk of a plan associated with the forecast (i.e., the likelihood that a missed demand could occur during the duration of the trial). Forecasts may be accurate on average, but resulting plans may lack the robustness needed for high stakes programs.
2. Without calculating risk, these tools can’t calculate required overage. Instead, overage must be included as an input in the calculation (usually using a rule of thumb or comparable past trial data). In most cases, this leads a higher level of drug waste than with a precision algorithm.

The advantage of clinical supply forecasting tools is in their efficiency and agility. They can provide rough estimates of demand early on in the planning stages of a trial or allow to quickly adjust plans in the face of uncertain events.

When should you use clinical supply forecasting?

Clinical supply forecasting tools are most suitable for use:

• To standardize your ways of working across the organization
• During high-level planning of trials well before study start
• For long-term DS/DP planning using studies with protocols that aren’t fully known or stabilized
• For simple Phase 1 trials where variability is low or inexpensive trials

What is risk-based clinical supply optimization?

Unlike clinical supply forecasting tools which only predict average demand, risk-based optimization tools use simulations to anticipate uncertainty and risk to the trial supply.

Risk-based clinical trial supply optimization tools leverage different algorithms to model uncertain parameters and simulate possible outcomes of a trial using Monte-Carlo simulations.

After running thousands of simulations, these tools can predict not only average demand, but minimum and maximum demand (or confidence interval) as well.

The uncertain parameters modeled in these simulations typically include:

• Recruitment speed
• Patient dropout
• Titration probabilities
• Visit intervals
• Patient weight

Whereas forecasting tools require overage as an input, risk-based optimization tools produce overage as an output.

This is accomplished by measuring the actual variability of demand, its amplitude, and when and where it occurs. With this information in hand, clinical trial supply teams can proceed with the lowest amount of drug overage necessary to guarantee zero risk of a stockout.

In addition, risk-based optimization tools are used to optimize IRT setup (e.g. safety stocks, trigger levels, resupply frequency), depot resupply strategy, and comparator sourcing strategy.

Risk-based optimization tools can also be used to monitor risk during ongoing trials. By combining the results of the simulations with actual trial data using machine learning, forecasts are continually improved and verified.

This information can then be used to determine where attention is needed and which actions are required to mitigate risk, reducing stress for clinical trial supply teams.

When should you use risk-based clinical supply optimization?

Risk-based optimization tools are most suitable for use:

• For the most high-stakes trials
• For setting up supply strategy and IRT 6 months from study start
• For assisting with protocol design in order to ensure supply-friendliness
• For ongoing monitoring of trial demand

Conclusion

Forecasting and risk-based optimization solutions are not mutually exclusive. The two approaches to predicting demand are complementary, and each has their advantages during different times in the study lifecycle or for different trials.

Being able to transition smoothly and at the appropriate time from forecasting to risk-based optimization is important. For this reason, it’s considered best practice to use one solution for both forecasting and optimization.

Risk-based management in clinical trials is a growing trend due to the increasing complexity and globalization of the trial supply chain. A supply optimization solution is a necessary component of any future-facing risk-based approach to clinical trial supply.

About the N-SIDE Supply App

Currently, the only available solution that adds risk-based optimization to clinical supply forecasting is the N-SIDE Supply App.

The Supply App has been used to optimize over 10,000 trials of all sizes and complexity, providing 20-50% overall cost savings and 20-60% drug waste reduction while ensuring no missed dispensing.

Because it offers proactive risk management capabilities, the Supply App also reduces stress for the clinical trial supply team throughout the trial lifecycle. How can we ensure reliability in a clinical supply forecasting system? Learn more in the free guide: Simplicity vs. Reliability in Clinical Supply Forecasting.