Class C components management strategies at Ferrari S.p.A.

This thesis is based on my internship experience at Ferrari S.p.A. from February to August 2024 and focuses on optimizing the management of class C components in the company’s production lines. The growth of the company in recent years has highlighted the need for a more robust and secure system for managing small components used in the production lines. An initial exploration of the "as is" process flow assesses the current system’s weaknesses and identifies areas for improvement. A "to be" process flow then is proposed, offering a roadmap for viable strategies and actionable steps to effectively implement an improvement project. The thesis explores the application of lean manufacturing principles, with a particular focus on implementing a Kanban system. Although not a new concept, Kanban is a proven, robust method with numerous and documented benefits. Additionally, the study proposes outsourcing the Kanban service, a decision driven by several factors that will be explained through the thesis, addressing the current weaknesses of the existing process. Finally, a business case over the next five years is presented to assess its economic feasibility and profitability. Four different business case scenarios are explored, starting from the least favourable and progressively finding out solutions to determine the project’s economic viability. Scenario 4 introduces a cost optimization strategy considering the delta between the purchased material from suppliers and the budget value in the same year. This delta reflects the excess stock Ferrari purchased but did not utilize in production. By incorporating this amount in the financial model, scenario 4 yielded a convenient outcome for the company. This project represents a profitable investment opportunity for Ferrari, introducing an integrated logistics strategy that rationalizes stock and warehouse management. This approach reinforces the supply chain, making it more resilient and capable of addressing the growing uncertainties of the next years.