PREDICTIVE ANALYTICS FOR FOLK ART MATERIAL REQUIREMENTS - Pathak, Abhishek; M. Saravanan; Srishti, Pooja \; Praveena J, Mary; Kurulekar, Mahesh; Taneja, Madhur (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Predictive analytics is a more rational direction of converting material planning to the old folk art manufacturing systems. The use of experiential estimation tends to create shortages in materials, excessive stocking and unviable use of resources further enhanced by seasons and change in demand in the market. To overcome such difficulties, a hybrid forecasting model was created that would have incorporated ARIMA, XGBoost, and LSTM models to forecast the material demand information based on multi-source data, such as past production data, climate indices, and sightseeing event schedules. The ensemble approach is also successful in capturing both linear and nonlinear demand patterns with a high prediction accuracy (R 2 = 0.96) and minimal error in the forecasting process minimized by 14.5 percent relative to the best single model. Pilot tests in Madhubani, Pattachitra and Warli clusters revealed that the number of wastages in procurement was decreased by 22 percent and the Sustainability Efficiency Index (SEI) was also enhanced by 18 percent indicating improved alignment of the production and material availability. The cloud-based device-supported dashboard will help artisans and cooperatives decide in an informed and eco-friendly manner by supplying real-time visualisations and procurement advice, as well as sustainability analytics. The balance between the precision and cultural authenticity of technologies is one of the other gains of the framework, where predictive intelligence is not in conflict with traditional craftsmanship but is rather its complement. The incorporation of sustainability constraints into the optimization layer also makes the model more consistent with the principles of the circular economy and UN Sustainable Development Goals (SDG 12). All in all, the study provides a scalable and culture adaptable model that improves efficiency, heritage value and fosters environmentally friendly development of folk art ecosystems.