Alouette – ML demand prediction platform for SMEs

Small and medium-sized enterprises (SMEs) face challenges such as inflation, supply chain disruption, and extreme weather, resulting in missed orders, overstocking, or understocking. Traditional forecasting methods based on seasonality and trend cannot provide accurate market demand, leading to inefficiencies and sales losses. The proposed research project aims to develop a machine learning-based demand prediction platform. The platform will analyze large amounts of data from internal and external sources, identifying difficult patterns and relationships to provide accurate market demand predictions in real-time. Apana will integrate with this platform as the “Software-as-a-Service” model. This will enable SMEs to perform real-time analysis of incoming data for their own demand forecasting. The overall objective of this project is to establish Apana as the go-to data hub for SMEs to manage their retail operations. We aim to grow our business based on the scale of users and the extension of different types of data services.

Faculty Supervisor:

Parminder Singh Kang

Student:

Partner:

Apana Technologies

Discipline:

Computer science

Sector:

Information and cultural industries

University:

MacEwan University

Program: