Neurosymbolic Reasoning for Link Prediction in Supply Chain Knowledge Graphs

This thesis is motivated by recent developments in Supply Chain Management (SCM) and Artificial Intelligence (AI). On one side, as modern supply chains become complex and interconnected with invisible dependencies, we increasingly see disruptions emerging and propagating across the network. This phenomenon, also known as ripple effect, is often difficult to manage given lack of visibility of the supply chain structure. While data-driven approach has recently emerged as solutions to proactively reconstruct and monitor these hidden dependencies, extant literature remains limited. This is an open problem in SCM. Meanwhile, the world is faced with a renewed disruption from the development of Artificial Intelligence (AI). The combination of big data availability and accessible computing power has increased the adoption of AI-based data-driven approaches in many aspects of society. However, as many modern AI techniques are based on black-box approaches such as neural networks, there have been calls for more governance to make AI more trustworthy in performing learning and reasoning. This is an open problem in AI. This thesis investigates the development of trustworthy AI as a data-driven approach to predict hidden dependencies in supply chain. We demonstrate how to utilise a novel methodology called neurosymbolic AI to predict hidden dependencies not only between companies, as used in extant literature, but also with other entities in the supply chain such as products, locations, certifications and many others. We also illustrate how this methodology enables practitioners to inspect the AI model's reasoning process, thus improving trustworthiness. While our works have shown promising results in two real supply chain data in the automotive and energy industry, there remains open questions on developing AI approaches that leverage uncertainty and privacy in order to make the model more trustworthy and adoptable by supply chain practitioners. This thesis will systematically discuss the remaining research gaps based on comparing the result of our systematic literature review with our thesis contributions.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Kosasih, Edward

Advisor dc:contributor.advisor

• Brintrup, Alexandra

• Artificial Intelligence

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a771d089-3b63-4ce0-872e-b3725e676400/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

Author Identifier

0000-0001-5293-2641

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/362487