Forecast parameter optimization in a multi-echelon supply chain setup – MediaMarktSaturn Retail Group
Overview
| Company Name / Department |
MediaMarktSaturn Retail Group |
| Contact Person |
Bart Schroeijers / Luuk van Rijthoven |
| Location | Eindhoven / Ingolstadt (DE) / Rotterdam |
|
Optional remote work |
Yes, flexible |
| Travel expenses (own account or reimbursed by the company) | Reimbursed by company |
| Housing arranged by company | No |
| Housing expenses (how much per month, own account or subsidized by the company) | No |
| Internship compensation | 350 Eur per month |
| Study program | OML |
| ESCF community |
Full Member |
| Start date |
01.02.2025 (flexible) |
Company Description
MediaMarktSaturn Retail Group is Europe’s leading consumer electronics retailer, operating over 1,000 MediaMarkt and Saturn stores throughout 11 countries in Europe, with over 50,000 employees and a turnover of more than €22 billion. The BeNeLux HQ is located in Rotterdam, and the international HQ in Ingolstadt (DE).
Project Description
Each country has their own central team responsible for the forecasting and replenishment (F&R) process. Every country uses the same planning tool that creates the forecast for our customer outlets and proposes replenishment orders upon this, both for our customer outlets and our central warehouses.
Today we do not use this tool at its full potential. It consists of multiple forecasting models and methods, and has lots of parameters we can change or edit to improve the forecast performance. We want to investigate if and how we can optimize the parameters and models to achieve a better forecast performance.
Goals of the project:
The primary objective of this project is to evaluate and improve the current parameter setup of our F&R tool, both upstream and downstream in the supply chain. This consists of mapping the current parameters and models we have, evaluating and testing their impact on the forecast performance, and proposing improved settings for different product groups. To test the improved settings for the different product groups, the goal is to build a simulation so we can test it under different scenarios. For this, we can use our digital twin software.
Deliverables:
- Clear description of the forecast models and parameters we have in the system and how they influence the forecast performance
- Simulation model to test different forecast settings
- Recommendation on which settings to use for different product groups and scenarios
Essential student knowledge
- Programming
- Simulation
- Forecasting
More information: escf@tue.nl
