Prediction has always been a key aspect of the pharmaceutical industry. With huge amount of money being invested in projects or portfolios, maximizing the possibilities for a return on your investment is not only important, but in many cases crucial for the continued survival of the company. Understanding the risks, what those risks entail and already having a plan for the ”what if” scenarios can very much change a company’s path forward.
The aim of this case study if to highlight the differences between using static values when trying to predict possible launch dates, as opposed to using uncertainties. The case study is based on previous client work, and how the estimation of a potential launch date was affected with using uncertainties instead of static values for predicting the launch date.
The Client Scenario
The client was planning late phase development of a highly profiled project, and the planned launch date for the project was set for May 2025. Being a highly profiled project, it was considered a priority to get the project to market in a fast manor.
To predict submission and launch date for the project, the client was using a static model, meaning that values for time, cost and success were all fixed. Further, the values were siloed, whereas there were no interdependencies between the values at all.
How Captario Helped With introducing uncertainties to the equation, along with values of risk and factoring in dependencies, the results showed that the most likely time of launch would be March 2026, almost a year after the initial prediction. The initial prediction instead showed a very low likelihood of occurring, as displayed by the launch graph.
Furthermore, by adjusting the previous static model and incorporating uncertainties and dependencies, the new model provided by SUM allowed the client to identify the main drivers of potential delays to submission and launch, as shown by the Tornado graph.
The conclusions that can be drawn from this case study is the misguidance of using static values. With using only static siloed values, the estimation runs a high risk of becoming skewed and misguiding, as the values that are inserted in the estimation equation don’t effect each other, when reality is the complete opposite. With adding risk values and constructing interdependencies between the values of time, cost and project success, the model becomes a better representation of reality, resulting in better decisions for continued company growth and survival. For this particular client, the shift from using static values meant that the project leadership was able to focus on the most important risk mitigations to ensure a timely launch, instead of waisting resources on activities of lesser value for the desired end result.