Problem Statement As the majority of Singapore's food supply is imported, optimising the supply chain is critical. Implement algorithms that can predict demand, manage inventory and optimise logistics to reduce food waste and streamline supply chain processes.
How does your hack answer the problem statement? We collect data including rainfall, annual precipitation, annual temperature, price of rice grown in Thailand and India, price of rice in Singapore, and the population of Singapore. We then use different algorithms including time series analysis and machine learning models for demand forecasting. This forecasting helps to minimise stock levels by aligning orders with demand forecasts, thus reducing food waste and streamline supply chain processes, ensuring food security.
How did you build your hack?
Our hack was constructed in four parts,data collection, data pre-processing, prediction, and data visualisation.After collecting data on relevant rice information,we reformatted it into a more coherent dataset and ran it through Pycaret’s Regression Library to predict and forecast the demand for rice before visualising it through a simple website we created
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