Leveraging Artificial Intelligence to Enhance Supply Chain Resilience : The Promising Role of AI Tools in Predicting and Preventing Disruptions
Khan, Muhammad Maaz (2025)
2025
Tuotantotalouden DI-ohjelma - Master's Programme in Industrial Engineering and Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
Hyväksymispäivämäärä
2025-05-26
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202505236055
https://urn.fi/URN:NBN:fi:tuni-202505236055
Tiivistelmä
The global supply chain is getting deeper and more complicated every day which necessitates new ways of adaptive and proactive risk management. Thesis — Leveraging Artificial Intelligence to Enhance Supply Chain Resilience: The Promising Role of AI Tools in Predicting and Preventing Disruptions is based on qualitative case studies of two Finnish companies, Valio Ltd (food sector) and Murata Electronics Oy (electronics sector).
This study reveals operational, environmental, and cybersecurity challenges, affecting supply chains based on theoretical models, such as Resource-Based View (RBV), Dynamic Capabilities (DCs), and Technology Acceptance Model (TAM). It also explores the organizational, tech, and ethical challenges faced in AI adoption. Highlights are that AI fosters better supply chain visibility, faster decision-making and improved risk detection, though fragmented data systems, infrastructure constraints and a degree of inertia remain an issue.
This paper contributes to both academic discourse and practical applications by proposing a conceptual framework that integrates AI capabilities with strategic goals. The results highlight the factors that organizations need to overcome, such as regulatory and human-AI collaboration in order to achieve the full value of AI in supply chain management.
This study reveals operational, environmental, and cybersecurity challenges, affecting supply chains based on theoretical models, such as Resource-Based View (RBV), Dynamic Capabilities (DCs), and Technology Acceptance Model (TAM). It also explores the organizational, tech, and ethical challenges faced in AI adoption. Highlights are that AI fosters better supply chain visibility, faster decision-making and improved risk detection, though fragmented data systems, infrastructure constraints and a degree of inertia remain an issue.
This paper contributes to both academic discourse and practical applications by proposing a conceptual framework that integrates AI capabilities with strategic goals. The results highlight the factors that organizations need to overcome, such as regulatory and human-AI collaboration in order to achieve the full value of AI in supply chain management.