Locating New Pam and Susan’s Stores

analyze sales data to find ways to improve store performance. Over time, however, Wal-Mart had

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 is a chain of discount department stores. There are currently 250 stores, mostly located throughout the South. As the company has grown and wants to expand, Pam and Susan’s is in the search of the most profitable location for the new stores. Store locations decisions are based upon estimates of sales potential. The company is currently considering two sites A and B for the next store opening. Using the information gathered on demographics and economic trading zones, size, composition and sales of the 250 existing stores we will built a regression model to provide the best estimate of sales from the two sites and recommend the most profitable one for the next store opening.

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

* Our company’s sales forecast has been based on performance from previous years along with market circumstances. We are looking at the future of the business objectively which we then can evaluate past to

The new owner of a beauty shop is trying to decide whether to hire one, two, or three beauticians. She estimates that profits next year (in thousands of dollars) will vary with demand for her services and has estimated demand in three categories low, medium and high.

Greaves provided five years and two months of annual sales data. Using Stat Tools, the following analysis were run: Moving Average, Exponential Smoothing Simple, Exponential Smoothing Holt’s, and Exponential Smoothing Winter’s. Following a comparison on the average on all models, the Exponential Smoothing Winter’s was found to be the most suitable model for the case. A graph analysis is captured below.

These number will be used for predicting future financial statements later in this case study.

Using MINITAB run the multiple regression analysis using the variables CALLS, TIME, and YEARS to predict SALES. State the equation for this multiple regression model.

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

|change | | |£000s |% ** |£000s |% ** |% | |Total Till Roll |27,906,191 | |28,605,449 | |2.5 | |Total Grocers |20,060,291 |100.0% |21,227,518 |100.0% |5.8 | |Total Multiples |18,605,858

Using the sample data given in Table 2-20, make a recommendation for how many units of each style Wally should make during the initial phase of production. Assume that all of the 10 styles in the sample problem are made in Hong Kong and that Wally’s initial production commitment must be at least 10,000 units. Ignore price differences among styles in your initial analysis.

As a result, this paper makes an assumption that only data from wholesalers 1 and 2 will fit the model best, in terms of finding the correct diamond for the professor.

1. Sales forecast – (at $ 30 retail price with the assumption of $15 whole sale price)