Optimize forecast accuracy through assessment of forecast adjustments and development of potential forecast enrichments – NXP
Overview
| Company Name / Department |
Van den Bosch |
| Contact Person |
Ana Glaser US / Bibi de Jong NL |
| Location | Eindhoven, NL |
|
Optional remote work |
|
| Travel expenses (own account or reimbursed by the company) | |
| Housing arranged by company | No |
| Housing expenses (how much per month, own account or subsidized by the company) | |
| Internship compensation | |
| Study program | |
| ESCF community |
Full member |
| Start date |
September 2024 |
Company Description
The world leader in secure connectivity solutions for embedded applications, NXP is driving innovation in the secure connected vehicle, end-to-end security and IOT smart connected solutions markets.
As one of the world’s largest chip-maker, the company has 30,000 employees in over 30 countries, with approximately $13B in annual revenue.
Project Description
Project description:
Due to different types of bias, forecast adjustments are made along the forecasting process. After categorizing the source and conditions for forecast adjustments (previous master thesis output), this use case will focus on developing an impact assessment and optimized recommendation of the forecast judgement to enable the demand manager to make an informed decision.
Goals of the project:
As part of this project, we would like to quantify the impact of historical and potential judgments to recommend an optimal forecast adjustment.
Deliverables:
This project extends previous research understanding the dynamics and causes of forecast adjustments and quantifying adjustments, especially when human behavior influenced forecast accuracy.
This project consists of three phases:
- Develop an understanding of the forecast adjustment process and the various stakeholders involved.
- Conduct a sensitivity analysis of the forecast adjustment process to quantify opportunities for optimal forecast improvement.
- Develop a prototype to demonstrate the application of the recommendation system applying the opportunities identified in phase 2.
Essential student knowledge:
Python, Machine Learning methodologies.
More information: escf@tue.nl
