Demand Forecasting for Store Labour Scheduling at Major Canadian Retailer

The SportChek / Atmosphere banners of Canadian Tire Corp. have over 200 corporately operated retail stores across Canada that vary in size and market demographics selling an assortment of footwear, athletic apparel, and sporting goods. The inherent complexity of the business on top of variation of customer demand year to year makes store labour planning a challenging problem that is well suited to machine learning.
Quality scheduling is an important element of a good employee experience and provides a stable foundation for a retail business to drive sales and operate efficiently. The objective of this research is to explore advanced forecasting and optimization techniques that could improve the quality of sales forecasts and integrate into the weekly scheduling processes for every store.

Faculty Supervisor:

Sheng Liu;Opher Baron;Vahid Sarhangian

Student:

Partner:

Canadian Tire Corporation

Discipline:

Business

Sector:

Retail trade

University:

University of Toronto

Program: