Abstract
This thesis investigates how digital technologies can strengthen visibility capabilities and, as a result, support more effective decision-making in supply networks. The research builds on the recognition that digital solutions are a key enabler in the current landscape of Supply Chain Management, as they play a crucial role in generating, integrating, and governing information across interconnected operations. Traditional approaches to Supply Chain Management have shown their limits in coping with the disruptions and shocks that global networks increasingly face, demonstrating how such challenges can severely undermine the performance of the actors involved. To respond to similar pressures, the move toward digitalisation has been proposed as a way to better manage information flows and strengthen communication among partners, primarily through enhanced visibility. More specifically, the study was guided not only by academic gaps but also by the need to understand the expectations of supply chain managers, ensuring that the analysis allowed for deriving value according to real-world needs. In this context, the thesis points out two solutions as increasingly crucial to advancing visibility in practice: the Supply Chain Control Tower and Artificial Intelligence. This research is built on a solid literature analysis that enables a profound understanding of the core topics on the field investigated and guides directions for further study (Part 1). The focus was then expanded through an empirically grounded analysis, which generated new insights based on the perceptions of practitioners managing supply chains in real contexts (Part 2). The analysis outlined a clear understanding of the role of technologies in enhancing supply chain performance and visibility (Phase 1), which emerged as a result of Part 1. Among the wide range of tools examined, two technologies stood out as particularly promising yet still insufficiently explored in both academic and practical contexts: the Supply Chain Control Tower and Artificial Intelligence. These technologies have been deepened in Part 2, deepening the empirical perspective. In particular, the first emerged as a designed enabler of end-to-end visibility, but also as a complex ecosystem of interrelated technologies whose effectiveness depends on data availability, interoperability, and cross-organisational collaboration: these findings motivated a dedicated focus in Phase 2 to clarify its real contribution and the constraints that limit its diffusion. Artificial Intelligence was identified in Phase 1 as a rapidly evolving and disruptive domain, and has been deepened in Phase 3 to understand how it could overcome the limitations of Supply Chain Control Tower to enable a higher level of decision support and performance improvement. This exploration culminated in an in-depth analysis of the emerging class of Agentic AI systems, aimed at providing practical guidelines to maximise their benefits while mitigating associated risks. Taken together, the results consolidated a fragmented theoretical debate by clarifying how different digital technologies (both individually and in combination) support visibility and decision-making in supply chains. The thesis also advances existing knowledge by grounding theoretical concepts in empirical evidence, refining the understanding of Supply Chain Control Tower through practice-based insights, demonstrating the value of Artificial Intelligence, and introducing novel perspectives on human–AI collaboration within decision processes. Beyond theory, it provides actionable guidance for managers seeking to implement digital tools that strengthen visibility and performance across interconnected activities. The outcomes of the research were formalised through a collection of seven scientific publications, submitted to scientific journals and presented at international conferences.