Characterizing value chain patterns of urban factories: A case study in the grenoble urban area

Urban factories aim to reintegrate manufacturing activities into urban areas as decentralized and sustainable entities that leverage urban properties and proximity throughout their value chains. However, the lack of a systematic characterization of urban factories remains a gap in the literature. This study covers this gap by proposing a structured methodology to characterize urban factory value chains and identify best practices adapted to specific geographic contexts. A case study was conducted on 46 urban factories in the Grenoble urban area. First, a value chain model adapted to urban factories was employed to collect data and structure a dataset. Then, the latter was analyzed using multiple factor analysis (MFA) and principal component analysis (PCA) to identify the most significant variables influencing urban factory value chains. Finally, hierarchical clustering on principal components (HCPC) was conducted, leading to the identification of 16 distinct value chain patterns. The findings reveal that proximity is predominantly utilized at the distribution level rather than at the sourcing level. Key factors influencing urban factory clustering include distances to suppliers and markets and production area sizes. To enhance the practical application of these results, a score-based matchmaking algorithm was introduced to support urban factory designers in selecting optimal value chain configurations. The robustness of the proposed methodology was validated through additional case studies, achieving a clustering accuracy exceeding 94 %. This methodology provides a scalable approach for analyzing urban factories, offering a data-driven tool for engineering designers, urban planners, and policymakers to optimize sustainable manufacturing integration within urban areas.