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Determining optimal staffing levels at the Whistler Blackcomb Ski and Snowboard School

University of British Columbia

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.

Thesis/Dissertation

application/pdf

2009-07-29

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http://hdl.handle.net/2429/11487

Business Administration

University of British Columbia

UBCV

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