Question

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 was also given to the lighting, colors, and overall ambiance, resulting in a truly magnificent experience for all who came to enjoy gourmet dining. Since its opening, The Glass Slipper has developed and maintained a reputation as one of the must visit places in that region of New Mexico.

While James loves to ski and truly appreciates the mountains and all that they have to offer, he also shares Deenas dream of retiring to a tropical paradise and enjoying a more relaxed lifestyle on the beach. After some careful analysis of their financial condition, they knew that retirement was many years away. Nevertheless, they were hatching a plan to bring them closer to their dream. They decided to sell The Glass Slipper and open a bed and breakfast on a beautiful beach in Mexico. While this would mean that work was still in their future, they could wake up in the morning to the sight of the palm trees blowing in the wind and the waves lapping at the shore. They also knew that hiring the right manager would allow James and Deena the time to begin a semi-retirement in a corner of paradise.

To make this happen, James and Deena would have to sell The Glass Slipper for the right price. The price of the business would be based on the value of the property and equipment, as well as projections of future income. A forecast of sales for the next year is needed to help in the determination of the value of the restaurant. Monthly sales for each of the past 3 years are provided in the table below.

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

Monthly Revenue (in $1,000s)

Month 2008 2009 2010
January 438 444 450
February 420 425 438
March 414 423 434
April 318 331 338
May 306 318 331
June 240 245 254
July 240 255 264
August 216 223 231
September 198 210 224
October 225 233 243
November 270 278 289
December 315 322 335

Answer 1

The monthly sales data from The Glass Slipper restaurant illustrates seasonal patterns with peaks during the ski season, and a 12-month moving average forecast would smooth out these fluctuations to show the long-term trend. This analysis aids in setting a fair price for the business based on projected incomes.

Step-by-step explanation:

To analyze the business performance of The Glass Slipper restaurant, a graphical representation of the monthly revenues alongside a 12-month moving average forecast will provide insights into trends and seasonal patterns.

The graph plots revenue against time, where each point on the graph corresponds to a month's revenue from the provided data for the years 2008, 2009, and 2010.

Seasonal patterns can be observed, with higher revenues during the winter months, which correspond to the ski season in New Mexico.

This indicates that The Glass Slipper's business peak aligns with the tourist influx to the ski area. A moving average forecast assists in smoothing out fluctuations to reveal the underlying trend. In this case, it would show whether the restaurant's revenue is growing, declining, or stable over the 3-year period.

By analyzing these patterns, James and Deena can estimate the business's future income, which is crucial in determining the right price for selling The Glass Slipper.

The 12-month moving average, in particular, helps to mitigate the impact of seasonality and isolate the trend, which potential buyers and financial analysts would find valuable for making informed decisions.

The calculation of the moving average involves summing up 12 months of revenue data at a time, then dividing by 12, and plotting this value on the graph. With each subsequent month, the oldest month drops off, and the newest month's revenue is included.