# Forecasting Monthly Sales Case Study Review: The Glass Slipper Restaurant

Decent Essays

Forecasting Monthly Sales
Case Study Review
Embry-Riddle Aeronautical University
Quantitative Analysis for Management

Group One

Background

For years The Glass Slipper restaurant has operated in a resort community near a popular ski area of New Mexico. The restaurant is busiest during the first 3 months of the year, when the ski slopes are crowded and tourists flock to the area.
When James and Deena Weltee built The Glass Slipper, they had a vision of the ultimate dining experience. As the view of surrounding mountains was breathtaking, a high priority was placed on having large windows and providing a spectacular view from anywhere inside the restaurant. Special attention …show more content…

Their dream is to retire and move to a more tropical location. While they understand that full retirement is not an option at this point, they are willing to sell The Glass Slipper and open a bed and breakfast on a Mexico beach which affords them a semi-retirement option for their near future plans. In order for them to have enough profit from the sale to complete their intended lifestyle transition, they are requiring the sale price to include property and equipment as well as future sales projections. Using data from the previous three years, a projection of the following year’s data will be made and evaluated.
Data
Monthly Revenue (In \$1,000’s)

Problems
1. Prepare a graph of the data. On this same graph, plot a 12-month moving average forecast. Discuss any apparent trend and seasonal patterns.

The seasonal pattern shows that through the summer and fall there is reduced sales revenue that can be attributed to the lack of snow covering the resort area, but still being a location people like to visit. As the snow accumulation increases starting in late fall, sales begin to pick up and reach the maximum levels in the early part of the years during January. Sales remain high during this winter time frame until significant decreases in the spring through fall months.

2. Use regression to develop a trend line that could be used to forecast monthly