This thesis presents the digital transformation pathway of Ferrari Gestione Sportiva’s Mechanics Department (MecSF), aiming to make decisions along the Planning–Scheduling–Execution cycle predictable, measurable, and reproducible in a high-mix, low-repeatability F1 job-shop context. Following the as-is diagnosis—characterized by heterogeneous tools (Excel/ERP extracts), information latency in the PR→PO flow, and limited representation of real constraints (shifts/presence/skills, tools/fixtures, semi-finished parts, metrology/quality checkpoints)—we motivate critical issues around ungoverned saturations, lead-time variance, and dependence on tacit expertise. The proposal defines a to-be architecture based on a Digital Thread, constrained scheduling, and native Discrete-Event Simulation (DES) to support baseline/stress what-ifs, explicitly including TTM specificity (additionalstages and α = 1) and operational visibility via an interactive Gantt.