Karen Payne, owner of the Vintage restaurant in Ft. Myers, Florida is looking to predict future food and beverages sales with their current sale history. The Vintage restaurant, a high-quality establishment, has just completed its third year in operation and provided us with their monthly food and beverage sales. Upon initial analyzation of our data, we could easily tell that there was a seasonality trend in the data provided to us. Some additional statistics about the data follow:
• September was the lowest month of sales (each year)
• December was the highest grossing month of sales (each year)
• From Sept. to January, sales would grow (each year)
• From January to Sept., sales would decline (each year)
With this in mind, I then began
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With that coefficient irrelevant, the numbers of the above coefficients are slightly different to provide an adjusted forecasting model.
According to the data above, forecasted sales (in thousands) equals 223.667 + the correlating coefficient depending upon the month of the forecast. Furthermore, this indicates that before the business was opened, they were earning $223.667(thousand). Obviously, did not happen so we can disregard this value due to extrapolation.
With this regression we were also provided an R squared value of .948 or 94.8% meaning that 94.8% of the variability in food and beverage sales can be explained by the month coefficient.
With two indicators now showing that the model with a trend is providing more accurate results (graph and the R-squared value), I felt slightly more confident in using that forecasting model. To be completely sure however, I decided to verify with one more test: the mean squared error. The mean squared error measures the average of the squares of errors, - the lower the MSE, the better. Overall, the mean squared error for our data with a trend was 12.6 indicating that the model closely resembles the data that we were provided. For our linear regression model without a trend, the MSE was calculated at 111.96 indicating that the model is not as closely related to
As strategy consultants of McCormick & Associates, we use Porters Five Forces Model as a framework when making a qualitative evaluation of a firm's strategic position (Appendix 1.2). These five forces determine the competitive intensity and therefore attractiveness of a market. These forces affect the ability of a company to serve its customers and make a profit. A change in any of the forces normally requires a company to re-assess the market place.
I will be talking about Carmine’s Southern Italian Restaurant, Specifically the Theater District location where I work. Carmine’s located in the Theater District is a very successful establishment; I have met some of the hardest working individuals in my time of employment and have learned how the company works. They are a family style restaurant where all food is meant to be shared. Carmine’s implements a real good concept and I will be talking about all of things that help this business thrive and be a fan favorite in New York.
John’s wife’s father purchased 3 Kentucky Fried Chicken restaurants in Waterloo Iowa. John and his wife, Marlynn Myers, moved into Marlynn’s parent’s home to manage the restaurants.
These values are based on a number of different assumptions. See Exhibit B. The forecast is not without a level of uncertainty. Specifically, there are regulatory decisions where the outcome is not clear at this time. This could impact profit margins plus or minus seven percentage points.
For example, if Y is the cost of goods sold and X is the sales, and α = 2 and β = 0.75, and if the sales are 100, i.e., X = 100, the cost of goods sold would be, on average, 2 + 0.75(100) = 77. However, in any particular year when sales X = 100, the actual cost of goods sold can deviate randomly around 77. This deviation from the average is called the “disturbance” or the “error” and is represented by “e”. Also, in the equation Y = 2 + 0.75X + e, i.e., Cost of goods sold = 2 + 0.75 (sales) + e, the interpretation is that the cost of goods sold increases by 0.75 times the increase in sales.
Charles Chocolate’s sales revenue decreased -1.176% between the years 2010 and 2011. The equation that as used to get that was Revenue Growth= 100 × (Current Value-Prior Value/Prior Value) 100 × (11,850,480-11,991,558/11,991,558). The change in the sales revenue could have happened for very many reasons. Being a premium chocolate making company, their product may not have been very high in demand. Also forecasting the demand for their product was not a very easy thing to do either. Another issue that Charles Chocolate’s faced their competitors, such as Godiva and Lindt, are more of a well known brand then they are.
Total retail (users+non-users)MM $65.98 $39.68 Total factory sales (2/3 of retail)MM $43.99 $26.45 NRFC hurdle (factory sales)MM $45 $45 ? Pizza kit and topping: 43.99 MM, reach company's projected factory sales of 45MM ? Pizza kit only: 26.45 MM, Fail to reach company's projected factory sales of 45MM Exhibit 2 Sensitivity analysis Change in penetration rate Change in pizza sales Percentage change in pizza sales 25% to 15% - 7.11M
Jansen, H. A. F. M., & Heise, L. (2011). What factors are associated with recent
The next model is the Quadratic Trend Model. The quadratic formula uses the least-squares method to forecast and can be written as Yi =b_0+ b_1 X_1+ b_2 X_2. In this formula the only difference is b_2 X_2 represents the estimated quadratic effect on Y. Figure 1-6 represents the comparison between the linear and quadratic
Dinner Bell Hotel is a Michigan resort, with large meals, farm animals, petting zoo, lake for swimming and much more. As suggested by the name, the hotel holds the tradition of ringing the bell to announce mealtime. July through early November is the busiest time for the hotel as all summer and fall guest enjoy the atmosphere of an old-fashioned resort with a comfortable environment. The weather gets too cold by early November for most outdoor activities thus in order to attract customers, the hotel has also built an indoor pool and developed long theme weekends like classic movies.
b. To show the proportion of your budget spent on each of the four fixed costs for your company during the year.
2. Explain what has happened to the data for Urban Run. What are the consequences of continuing to use seasonal exponential smoothing? What models would you use? Generate a forecast for the 4 quarters of the 4th year using your model. Determine your forecast error and the inventory consequences.
Since model selection could not be based upon econometric tests, Frontier Economics employed combinations of the following output variables:
The newer steer’s stores are built so that customers can see the kitchen where food is made. This creates transparency and interaction with staff and allows customers a glimpse of behind the scenes
Therefore, the elasticities for each independent variable will need to be computed as follow because this will provide the breakdown of how each variable will represent within