SOLVING SPATIAL DEMAND OPTIMIZATION PROBLEMS USING AUTOMATED WORKFLOWS IN ALTERYX, TABLEAU, AND SQL

Abstract

In today’s data-driven business environment, organizations are increasingly adopting AI and automation to enhance operational efficiency and customer satisfaction. As customer expectations evolve toward faster and more convenient services, there is a growing need for intelligent systems that can optimize operations based on spatial and demand-related data. This project addresses the challenge of solving spatial demand optimization problems using automated workflows built with low-code tools. The study focuses on developing a scalable, automated solution using Alteryx for data transformation and logic, Tableau for clustering and geospatial visualization, and SQL for data storage and integration. Using only minimal inputs store-level latitude, longitude, and demand values the workflow identifies high-demand zones and strategically allocates resources based on clustering analysis and weighted spatial logic. Despite limitations in available data, the solution successfully enables decision-making for spatial demand optimization, offering insights into how low-code platforms can be leveraged for strategic planning. The outcome demonstrates that even with limited datasets, meaningful business insights can be achieved through the right combination of tools and analytical methods.