The role of data visibility in the control and automation of modern Supply Chains

A Model Predictive Control case study in Ferrari

Master thesis (2022)

Authors

F.M. Dell'Orto Mechanical Engineering

Contributors

W.W.A. Beelaerts van Blokland Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
Nicola Patuelli Ferrari (supervisor 1)
Faculty
Mechanical Engineering
Copyright: © 2022 Federico Dell'Orto
Big Data Model Predictive Control Supply Chain integration Supplier Relationship Management Supply Chain control tower Centralized autonomous agents
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Published Date

15-12-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

Nowadays many companies still conceive their logistic operations as a simple material replenishment of the production plants and do not invest money and time in projects to structure their Supply Chain and bring more eciency to the production process. In addition, the high complexity of the automotive industry and the emerging uncertainties that are characterizing a more globalized, dynamic and interconnected world give companies a huge incentive to research and innovate the management of their supplier network. Over the last years, businesses have experienced several issues along their logistic flows. Unexpected events such as the pandemic 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.
In this research paper, the role of data visibility is put under attention, in order to demonstrate its practical benefits in a complex automotive Supply Chain. By collaborating with Ferrari on a Supplier Relationship Management (SRM) project, this research presents the design of a Supply Chain control tower through Model Predictive Control. By simulating a MPC optimization model on a small part of Ferrari’s supplier network, the coordination, eciency and sustainability of the Supply Chain are assessed through a comparison with the current state and by evaluating the network’s performances in di↵erent logistic scenarios. Although this solution is presented as a decision-support tool, it is thought as a key technology for the future development of autonomous Supply Chain operations.