The role of data visibility in the control and automation of modern supply chains - A model predictive control case study in Ferrari

Abstract

Nowadays, many companies still conceive their logistic operations as a simple material replenishment of production plants and don’t invest money to structure their supply chain and make processes more efficient. In addition, the high complexity and the emerging uncertainties that are characterizing a more globalized, dynamic, and interconnected world stimulate businesses to innovate the management of their supplier network. Unexpected events, such as COVID-19 and the semiconductor crisis, have put companies in research for solutions that look to improve and strengthen the partnership with their suppliers. Digitization represents one of the most innovative and disruptive challenges in today’s supply chains. Indeed, the increasing amount of data retrievable from logistic and production processes today is yet not exploited enough in comparison with its potential benefits. Companies still work by silos and prefer to hide their information rather than sharing them with their partners. This research investigates the role of data visibility, in order to demonstrate its benefits in a complex supply chain. By collaborating with Ferrari on a Supplier Relationship Management (SRM) project, this paper presents the design of a supply chain control tower through Model Predictive Control. By simulating a Model Predictive Control (MPC) optimization model on a small part of Ferrari’s supplier network, the coordination, efficiency, and sustainability of the supply chain are assessed through a comparison with the current state and by evaluating the network’s performances in different logistic scenarios. Although this solution is presented as a decision-support tool, it is thought of as a key technology for the future development of autonomous supply chain operations.