To support best-in-class pharmaceutical product launches, supply chain planners must reconcile rapidly shifting demand assumptions with lead times that require production months before regulatory approval. Traditionally, this gap compels static inventory over-buffering based on Months On-Hand (MOH) targets to mitigate the risks of supply shortages and lost market share. However, this heuristic-based approach often leads to supply inertia, trapping excess capital and increasing expiry risk. This project develops a standalone deterministic simulation tool designed to project finished goods inventory across safety stock, cycle stock, and pipeline inventory categories. The tool was applied to a case study of tolebrutinib, a Bruton's tyrosine kinase inhibitor under development for multiple sclerosis indications, to demonstrate its application and estimate the projected benefits of transitioning to a statistical inventory policy. A key contribution is the shift from MOH planning to a statistical methodology that calculates safety stock using Mean Absolute Percentage Error (MAPE)—leveraging historical data from similar on-market products—and target cycle service levels. The tool is designed to support a more dynamic, cross-functional Integrated Business Planning (IBP) process at Sanofi. Rather than anchoring the process to raw commercial inputs, the tool allows stakeholders to assign weights to various demand scenarios to compile a risk-adjusted aggregate forecast. Any gaps between this aggregate forecast and the commercial team’s high-demand scenario are accompanied by immediate visibility into potential lost revenue and profit, facilitating real-time strategic alignment. Furthermore, stakeholder groups can propose live adjustments to input variables, such as service levels and manufacturing lead times, to instantaneously assess inventory impacts. In the case study, the tool projects inventory reductions in excess of 75% relative to MOH heuristics, with corresponding improvements in working capital efficiency.
