| Summary: | Whistler Blackcomb Resort experiences the highest skier visits of any resort in North
America and consequently demand at the ski school is high. Due to various factors, the
daily number of lesson participants is highly variable and the best number of instructors to
staff each day is correspondingly difficult to estimate. The consequences of scheduling
incorrectly could lead to either overstaffing or understaffing. Overstaffing results in
unnecessary costs; understaffing results in lost sales and customer dissatisfaction.
A scheduling tool that can assist the Ski School in staffing decisions, therefore, is developed
to minimize excess costs. Daily demand predictions are made using a forecasting model and
a staffing policy is applied to it to obtain a recommended staffing level. The demand
forecasting model is a regression model that takes into account pre-bookings, day of the
week, holidays, and yesterday's demand. The staffing rules are determined through a
Newsvendor-type model derived from a marginal cost analysis of the trade-off between
overstaffing and understaffing applied to the daily demand forecasts.
The project is intended to formalize a systematic approach to staffing for certain lesson
types (pods) one day in advance. It will assist the Whistler Blackcomb Ski and Snowboard
School, as a decision support tool, in the development of daily instructor schedules that
rninimize any unnecessary costs. === Business, Sauder School of === Graduate
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