Forecasting Monthly Sales Case Study Review: The Glass Slipper Restaurant

Target is the second biggest retail company after Walmart. Native New Yorker, George Draper Dayton first built a company named Dayton Dry Goods Company in 1902 in the Minneapolis area which is now known as target headquarter. Walmart faced the out of stock issue problem last year and now their biggest competitor, Target, also has faced the same problem this year. Target has a problem keeping the availability of the product in their stores in Canada. It resulted in a huge loss of money and closing down their stores. The CEO of Target said that this is a serious problem and must been solved.

The rise in revenue was rapid starting from the year of operations. The key period of business was from April to September were revenues were equal to 65% of total revenue as the product was seasonal. The basis of forecasting for the year 1981 & 1982 is the expectations of sales by Mr. Turner & Mr. Rose. It is given that total sales were $ 15.80 million in first half of year 1981 and the total sales in 1981 to reach $ 30 million. Profit after tax was expected to be $ 1 million for 1st half and we assumed for the next half, profit will be in proportion to first half & expected to be amounting to $ 0.90 million. For year 1982, the sales expectation by Mr. Rose was around more than $ 71 million &

Finally we got all our number and determine the slope, and the intercept in order to find out the forecast for the next

The case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential, and for this purpose, you will need to develop a multiple regression model to predict sales. Specific case questions are given in the textbook, and the necessary data is in the file named pamsue.xls.

Pam and Susan’s department stores are in the process of opening a new business unit. There are two locations that are being considered for the new store and decision is based upon estimates of sales for both of them. My job is to use data gathered from each store as well census data in store’s trading zones to predict sales at both of the sites that are being consider for their newest store.

* Now, assume you have acquired some time series data that would enable you to make short, medium, and long term forecasts. Ascertain the quantitative technique that will provide you with the most accurate forecast. Provide a rationale for your responses

The purpose of this case is to determine which key variables drive Crusty Pizza Restaurant’s monthly profit and then forecast what the monthly profit would be for potential stores. Based off of this information we will be able to make a recommendation to Crusty Dough Pizza Restaurant on which stores they should open and which they avoid. The group was provided 60 restaurants’ data that included monthly profit, student population, advertising expenditures, parking spots, population within 20 miles, pizza varieties, and competitors within 15 miles. For the potential stores we were given all of this

Forecasting is the methodology utilized in the translation of past experiences in an estimation of the future. The German market presents challenges for forecasting techniques especially for its retail segment. Commercially oriented organizations are used to help during forecasting as general works done by academic scientists are not easy to come across (Bonner, 2009).

The internal sales data showed that the business would need $45,000 in monthly revenue to break even. The sales forecast which have been prepared keep in mind a 65% gross margin, however, based on actual figure for 2009, this target has not been reached, and the forecasted sales have fallen.

3. Refer to the monthly sales forecasts given in the first Table. Assume that these amounts are realized and that the firm’s customers pay exactly as predicted.

The sales forecast is based on projected levels of demand. All the numbers are expressed

We will be using the seasonal trend above to forecast the 5 Saturday home game in 2011

Seasonality, particularly when dealing with surf trips, may cause problems with supply and demand. If the weather shows that certain areas will not be ideal spots at certain times, the demand under those circumstances will be low. Based on the principles of supply and demand and the pricing policy, this means the cost must also lower to entice more customers. Furthermore although the reverse would suggest that then during times when the weather, time and location are going to produce results conducive to certain sports that the demand will be high and therefore the cost can be raised also. However, due to the weather being somewhat unpredictable, even the times that should be peak seasons will have days where people do

The procedure for this model is to collect several periods of history relating to the independent and dependent variables themselves, establish the relationship that minimizes mean squared error of forecast vs actual using linear or non-linear and singular or multiple regression analysis.

The personal selling process is a continuously revolving cycle of stages that assist the professional sales person of today in developing basic selling strategies and tactics that help them improve and prefect their own personal selling styles. As listed in the text, “there are countless small tasks in the personal selling process that are generally organized into seven major stages that overlap and interact which are: