Knowledge graph optimization for supply chain management (2) – Dow Inc.
Overview
| Company Name / Department |
Dow Inc. |
| Contact Person |
Sophie Thijssen & Isis Calonge Roy |
| Terneuzen, The Netherlands | |
| Optional remote work | Yes |
| 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) | No |
| Internship compensation | Yes |
| Study program |
|
| ESCF community |
Full member |
| Start date |
September, 2024 |
Company Description
Dow Inc.’s ambition is to become the most innovative, customer-centric, inclusive and sustainable materials science company. Our goal is to deliver value growth and best-in-class performance. The Company’s portfolio comprised of six global business units, organized into three operating segments: Performance Materials & Coatings, Industrial Intermediates & Infrastructure and Packaging & Specialty Plastics.
Project Description
Project description:
Digital supply chain has the potential to revolutionize the customer and employee experience, give the company a long-term competitive edge, deliver outstanding value to Dow and Dow’s shareholders. Graph science and graph databases can be used to represent and solve supply chain network problems. Knowledge graph algorithms can be utilized to address supply chain design challenges at Dow.
Goals of the project:
This research aims to explore, identify, and test the native graph algorithms to address supply chain design questions. 1) What is the optimal product flow in a vertically integrated network; 2) How can I determine the risk & resiliency in a supply chain design.
Deliverables:
- Literature Review (all algorithms explored)
- Identify, test, and implement the selected algorithms
Essential student knowledge:
- Data analytics
- Supply chain
- Programming skills
More information: escf@tue.nl
