Neural network prediction of small business competitiveness under a sustainable short supply chain business model for rooftop agriculture in a Bogotá neighborhood

• The model integrates sustainability, short supply chains, and urban rooftop approaches. • Short supply chains with rooftop agriculture can boost access to fresh and local food. • A competitiveness assessment based on the Value Network Level is used. • Neural networks are used to predict the impact of short supply chains on restaurant competitiveness. • The results highpoint the strategic potential of sustainable food supply in urban environments. This study presents a model-based analysis to predict the impact of a sustainable business model, grounded in short supply chains and rooftop agriculture, on the competitiveness of small and medium-sized enterprises (SMEs), specifically restaurants, in Bogotá’s Quesada neighborhood. Unlike previous research that focuses on end consumers or communities, this work analyzes the competitive value generated for intermediate clients within the urban food chain. The proposed sustainable business model is described using a Triple-Layer CANVAS. The study uses the Value Network Reference Model and neural network techniques. It looks at changes in collaboration, innovation, and proximity. The results suggest that adopting this model could increase SMEs' competitiveness by up to 11.8 %, providing access to fresh, differentiated, and environmentally friendly inputs, while also reducing logistics costs. Although the findings are promising, it is important to note that they are derived from analytical modeling and require empirical validation. The study concludes that rooftop agriculture, when integrated into short supply chains, represents a strategic opportunity to enhance the competitiveness of urban restaurants, although its viability will depend on overcoming technical, economic, and social barriers in real-world implementation.