Procurement 4.0 : Investigating AI adoption for Intelligent and Resilient Supply Chains

Abstract

The thesis investigates the adoption of AI technologies in procurement of large project-based companies to evaluate their effectiveness in minimizing project disruptions and enhancing supply chain resilience. The study is motivated by the critical impact that supply chain disruptions can have on the supply chain procedures, which in turn affects economic stability, environmental sustainability, and social well-being. This research explores established AI solutions and assesses the internal and external business environmental factors which hinder the adoption of these solutions. The central research problem addresses the shortcoming in technology adoption that can improve the resilience of complex supply chains. The theoretical basis of the study draws on concepts from supply chain resilience, artificial intelligence, procurement and business environment factors. A qualitative research methodology is employed, utilizing open-ended questionnaire for interviews with supply chain managers, project managers, and technology experts. Focus group interviews, complemented by an analysis of industry trends and academic literature, serve as the main approach for data collection. The findings highlight how AI can improve procurement processes and operational responsiveness, which are crucial for minimizing disruptions and optimizing supply chain performance. The study identifies challenges in AI adoption and strategies to integrate AI technologies into the supply chains and procurement processes of project-based companies. In addition to these findings, the study reveals that the successful integration of AI technologies hinges on strategic planning and adaptation to sector-specific challenges. It is demonstrated that existing technologies, when effectively implemented, can significantly mitigate risks associated with supply chain disruptions. The thesis underscores the potential of AI technologies studying in detail two applications i.e. IoT and Blockchain in monitoring procurement processes and mitigating cyber risks. Furthermore, Procurement Maturity Model is evaluated to study the current procurement status following it’s readiness and strategic alignment for AI adoption. Thus, the results highlight the ability of AI-driven systems to enhance real-time monitoring of supply chain operations, enabling organizations to respond swiftly to potential inefficiencies or risks. In conclusion, the research establishes that leveraging existing AI solutions, is essential for developing resilient supply chains in project-based companies with complex supply chain networks. This research thus provides a valuable foundation for future studies aiming to refine and expand the integration of AI in complex, high-stakes environments, along with shaping the future of SCM in global organizations.