Q1. Tell us something about yourself?

I am an Italian chemical engineer, who’s always dreamt to work in pharma industry but never thought about supply chain! I am passionate about the job I do and I think that clinical supply managers are privileged to work in the R&D, as they have a look-ahead opportunity on the future of their company. I love running, listening music and reading novels. I like travelling and discovering new places with their history, heritage, costumes, traditions. I am a proud father of 2 girls, a toddler and a newborn and my personal mission is to spend as much time possible with them!

Q2. Clinical demand forecasting is crucial in ensuring a seamless supply chain. Could you share some strategies and tools you use to effectively forecast clinical demand across various systems like SAP and N-side?

Clinical demand forecasting is very challenging because it’s affected by several and different variables, which are not always under our control. Proactivity and clear communication to stakeholders are key to use the correct assumptions and reduce as much as possible the inaccuracy that might impact the study. Forecasting system such as SAP or Nside, which I had the opportunity to use, can support our daily job and make it easier, faster and accurate. They also help on calling out potential supply risks, like stock outs, and giving the opportunity to create multiple scenarios. Scenarios assessment is very important as It allows me to evaluate how my supply plan is influenced by specific variables (e.g. shelf life, new enrollment projection etc.) and to engage my team on supply optimization opportunities that can reduce drug wastage globally.

Q3. Managing drug inventory for clinical trials is a critical responsibility. How do you optimize the production and distribution of clinical trial drugs to minimize waste and ensure timely availability for patients in the trials?

In my work experience I have always tried to reduce drug wastage at the minimum level possible. Sometimes that’s a bit of a challenge because it depends on how the trial performs, on the accuracy of enrollment projection. In my opinion, the key is to balance and compare forecasted data with actual data through systems like IRT. Assessing the differences between those will allow to optimize drug distribution, and subsequently to re-evaluate drug production schedule. This will also enable to leverage on stakeholders to further revisit trial strategy to improve it.