Boost Strategic Decisions with Supply Chain Digital Twin

Details

Supervisors

Jorge Diana ; Carvalho Relvas Susana Isabel

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

In today’s world, the expansion of e-commerce and the acceleration of home deliveries has outdated the traditional supply chain structure, and new needs of supply chain network are arising. To address this need, this dissertation develops a generic mathematical model implemented in python language optimising a retailer’s supply chain network design to maximise next-day deliveries while minimizing the costs. The global structure would consist of a set of warehouses, a set of small storage locations acting as cross-docking points and the clients. The concept and ideas in application in this work are derived from supply chain management, supply chain modelling, the concept of digital twin, optimizations models and the data-driven clustering process. Worten, a Portuguese retailer, provided the case study for the thesis, as they manage a supply chain for large home appliance house deliveries in Spain. The methodology involved a clustering of the raw data received from Worten. Some assumptions the warehouses, the flows and the costs were made. The result of the optimisation provides the design of the supply chain, the cost of operations, the service level and the truck load efficiency. The findings revealed that the design is mainly sensitive to the price of the rent of the small storage locations. Overall, the tool provides valuable insights over the state and design of the supply chain, while laying the first step of the creation of a digital twin.