Pharmaceutical company ranked in the top 10
Objective is to optimize the supply plan of eight clinical trials.
The clinical trials’ supply chain is characterized by considerable uncertainty coupled with demand for a high service level. Each clinical trial is different and has its own particularities, meaning that the supply plan optimization must be specific to each trial. In this case, each of our customer’s trials exhibited one or more of the following challenges:
- High supply costs:
- Expensive comparator drug
- Refrigerated shipments
- A high variability caused by:
- Patient recruitment uncertainty
- A slow enrollment and a variety of trial sites
- Weight-based dispensing
- A lot of different treatment arms and/or dose levels
- Constraints on the drugs:
- Short shelf-lives, limited bulk availability
- Country-specific labeling
- Unexpected changes:
- All of the above could alter at any time
Therefore, a solution was needed that could help meet the requirements in terms of service level while minimizing supply costs, yet that was flexible enough to be adaptable to change.
Models of the eight clinical trials were set up within CT-FAST, N-SIDE’s set of simulation tools. N-SIDE’s consulting team simulated the studies, working hand in hand with managers of the clinical trials at every stage of the study lifecycle:
Before the study began, optimal IRT parameters were recommended.
As the study continued, the consultants frequently monitored the evolution and the potential risk.
The clinical trial managers received advice before every important decision, based on the latest data available.
Throughout the project, the cost and risks of these different trials were minimized.
Optimal IRT parameters were recommended in order to decrease both risk and cost.
In this trial, the IRT strategy was chosen from among the cheapest and safest of all the re-supply strategies simulated. In general, strategy improvements gained from simulation yielded an expected 15% decrease in cost and 75% decrease in risk.
Also, the packaging plan was optimized and frequently adapted to suit the latest data available. In some cases, this meant packaging less than the initial plan. In other cases, it meant more or earlier packaging as simulations revealed some risks of shortfall. In this trial, simulation revealed that packaging more would reduce the expected risk.
Finally, simulation enabled us to anticipate the impact of changes in the trial.
This example shows how shelf-life extension impacted total packaging quantities required.
Would you like to know more about simulation and advanced forecasting and our solution CT-FAST?