Applying machine learning techniques for demand forecasting in retail

An important component to every growing retail business is demand forecasting which can affect the strategic plans of a business. The impact extends across the business’ function including budgeting, financial planning, price optimization, sales and marketing plans, capacity planning, staff management, risk assessment and mitigation plans.
In this project, we want to apply machine learning technologies to improve the accuracy and granularity of retail demand forecast. ML Models will be built from historical data and enriched with additional external factors using state-of-the-art machine learning techniques. This would result in shortening the company’s inventory age and improving the customer fulfillment rate.

Naghmeh Dezhabad
Faculty Supervisor: 
Sudhakar Ganti
British Columbia
Partner University: