Retail Supply Chain Predictive Analytics

The project aims predict the demand of customers for small and medium size businesses. Forecasting models will be developed analyze historical data to understand patterns and correlations. Machine learning will be applied to determine how the accuracy can be improved over existing statistical methods, such as Fourier Regression Analysis which is commonly used in retail demand chain management. The demand forecasting model will examine customer behavior and the context surrounding that behavior, including upcoming holidays, the weather, or a recent event such as COVID-19. The key benefit of the project is to help business better navigate many challenges due to demand uncertainty. In particular, it will support businesses to develop effective strategies to improve the management of resources.

Intern: 
Nikhil Bhatia
Faculty Supervisor: 
Michael Zhang
Province: 
Nova Scotia
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