From Provenance to Preference: An IA-Driven Framework for Leveraging Supply Chain Transparency to Build Consumer Trust in the EU Luxury Fashion Sector

Abstract

European luxury giants like LVMH, Prada, and Kering have invested heavily in sophisticated digital supply chain technologies, blockchain, IoT sensors, and AI monitoring that can verify a product's full origin and journey with high precision, yet this data rarely reaches consumers in any meaningful way. This thesis investigates the disconnect across 86 sources spanning luxury consumer psychology, supply chain management, and transparency marketing, using LVMH's AURA Blockchain Consortium and Prada's Re-Nylon programme as its primary case studies. The central finding is that the missing link is not technology or data, but a structured translation mechanism, termed the Narrative Strategy Engine, that converts verified supply chain information into compelling consumer-facing stories. To address this, the thesis introduces the Provenance-to-Preference (P2P) Framework, a four-stage process moving from Data Capture through Authentication and Story Translation to Consumer Delivery. It also proposes the concept of Provenance Scarcity, arguing that a product's authenticated origin story can function as a new form of luxury exclusivity one that cannot be replicated because it is rooted in a specific, documented history. The thesis concludes with a two-year implementation roadmap and positions the P2P Framework as both an academic contribution to Engineering Management and Luxury Marketing and a practical tool for brand managers navigating an era of increasingly conscious consumption.