Knowledge graph optimization for supply chain management – Dow Inc.
|Company Name / Department||
|Contact Person||Adriana Vazquez, Dave Metevia, Kyle Harshbarger, Bao Lin, Sophie Thijssen|
|Location||Terneuzen, The Netherlands|
|Optional remote work||Yes|
|Travel expenses (own account or reimbursed by the company)||No|
|Housing arranged by company||No|
|Housing expenses (how much per month, own account or subsidized by the company)||No|
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.
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, as an emerging field, can be used to represent and solve supply chain networks problems. Knowledge graph based optimization methodologies can be utilized to address challenges of supply chain and logistics networks of Dow.
Goals of the project:
This research aims to explore and identify the graph based optimization algorithms to address challenges of supply chains, and develop a systematic approach of graph databased optimization by integrating siloed data to build interconnectivity and enable advanced analytical capabilities to support decision making.
- An overview of graph based optimization methodologies and applications to address challenges of supply chain and logistics networks
- Key components of process for model verification, performance testing, data validation and implementation
- Thesis Report
Essential student knowledge:
- Data analytics
- Supply chain management
- Graph science
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