Safety Stock Target Level Optimization using AI – NXP Semiconductors N.V.
Overview
| Company Name / Department |
NXP Semiconductors N.V. |
| Contact Person |
Eric Weijers |
| Location | Eindhoven, HTC 60 |
|
Optional remote work |
Yes, preferred to be in office on Thursdays. |
| Travel expenses (own account or reimbursed by the company) | Travel allowance is calculated based on residence location and the number of days in office. |
| Housing arranged by company | No |
| Housing expenses (how much per month, own account or subsidized by the company) | Not covered |
| Internship compensation | €550 per month |
| Study program | Operations Management & Logistics |
| ESCF community |
Full member |
| Start date |
September 2024 |
Company Description
NXP Semiconductors designs, develops and manufactures semiconductors for a wide range of applications. Most notably NXP is the market leader in semiconductors for the automotive industry. The company has over 30.000 employees and operations span the entire globe (over 30 countries).
Project Description
Project description:
NXP has a complex supply chain to manufacture and deliver semiconductors. Currently, NXP uses centralized/top-down LP models to generate supply chain plans based on capacity and demand. Due to the large scale of the models, run times are long and planners often need to make decisions without reconfirmation from the planning models. One of those decisions is the setting of target safety stocks.
In this project, you will develop an AI-based method for safety stock target level settings that optimizes inventory costs and service level.
Goals of the project:
Develop an AI-Based Safety Stock Target Level Optimization Model.
Deliverables:
- Safety stock target level optimization model
2. Improve the current business process using the safety stock target level optimization model as a decision support system
Essential student knowledge:
- Affinity with supply chain management.
- Experience with coding in Python or C++.
- Proactive work attitude.
- Communicative.
- Team player.
- Knowledge of AI-based optimization is a pre but not required
More information: escf@tue.nl
