MERRIWELL BAG COMPANY Case Analysis | Coursepaper
Because of the seasonal nature of the demand facing Merriwell Bag Company, an appropriate forecasting tool is the classical decomposition method (discussed in the supplement to Chapter 11). The data in the case is provided on the website for the textbook. Only the data is provided on the Excel template from the website. The user must enter the formulas and analysis.
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This case talks about the success on a small, family-owned company — Merriwell Bag Company. Merriwell Bag Company targeted a right market niche, which had no intensive competition. Several strategies also contributed to Merriwell Bag Company’s success. One strategy is that Merriwell Bag Company does not rely on one customer too much. No one can gain bags over 15 percent of sales so that the risk of breakdown is decreased. Merriwell Bag Company does not pursue new customers aggressively.
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Merriwell Bag Company’s case illustrates a realistic forecasting problem. And Chapter 11 mainly talks about the topic of forecasting. After studying this chapter, we can compare different forecasting techniques to help Merriwell Bag Company find the best forecasting method. Merriwell Bag Company’s difficulty is that there is a seasonal factor influencing the company’s forecast. Merriwell Bag Company found a forecasting method that anticipates the growth patterns of their customers. This will enhance Merriwell Bag Company’s ability of market profitability.Merriwell Bag Company applied a highly accurate demand forecast, which allows Merriwell Bag Company to meet the special needs of customer, through its own warehouse facility and routing schedule of truck line. It is difficult for Merriwell Bag Company to forecast the demand because of the seasonal nature of the product. Merriwell Bag Company needs to consider the seasonal factor and find the right method.Merriwell Bag Company is a small, family owned corporation located in Seattle, Washington. The stock of the company is equally divided among five members of the Merriwell family (husband, wife, and three sons), but the acknowledged leader is the founder and patriarch, Ed Merriwell. Ed Merriwell formed the company 20 years ago when he resigned as a mill supervisor for a large paper manufacturer. Ironically, the same manufacturer formed a container division five years ago and is presently one of Merriwell’s competitors.Merriwell Bag Company manufactures and distributes stock bags to many small chain stores scattered over a wide geographical area. Presently, due to growth in the business, forecasting demand has become more difficult. As a result, the company would like a forecasting system developed. Monthly data from the past five years is provided in the case.To improve the accuracy of the forecast, Merriwell Bag Company needs to identify the magnitude and form of each component on the basis of available past data. These components are then projected forward into the future. A reliable forecast will be obtained after this is done. This company needs to invest in better forecasting methods, build more flexibility into the company, and reduce the lead time over which forecasts are required to also improve the accuracy of the forecast.Analysis Because of the seasonal nature of the demand facing Merriwell Bag Company, an appropriate forecasting tool is the classical decomposition method (discussed in the supplement to Chapter 11). The data in the case is provided on the website for the textbook. Only the data is provided on the Excel template from the website. The user must enter the formulas and analysis. Sixty months of data are provided on the template, see Exhibit 1. The first step in classical decomposition is to develop a 12-month moving average which is done in the 3rd column on the worksheet. Then a 2-month moving average is developed in the 4th column which is centered on the original data. The 4th column contains data which is deseasonalized, since 12 months has been used as a base in the moving average. At this point the upward trend in the moving average in column 4 is apparent. In column 5 seasonal ratios are computed by dividing the sales data for each month by the moving average in column 4. The data indicates that high seasonal demand occurs before Christmas each year in September, October and November. Low seasonal demand occurs in the January, February, and March time frame. In column 6 average seasonal ratios are computed. These ratios are obtained by averaging the seasonal ratios from the same month in successive years. For example, the July 2011 seasonal ratio is obtained by averaging the July 2007, July 2008, July 2009, and July 2010 seasonal ratios. When the resulting twelve seasonal ratios are added the total is 11.8977. The sum of these ratios...