Case: Sport Obermeyer
Vijayakrishnan Krishnamurthy, Kara Maruszak, John Mulka
University of Toledo
July 4, 2014
Sport Obermeyer, Ltd. was founded by Klaus Obermeyer to provide U.S. skiers with the same protective and stylish clothing and equipment available in Germany. With a history of innovative new products and leading styles, Sport Obermeyer developed into a prominent competitor in the U.S. skiwear market selling its products in U.S. department stores and specialty ski shops. Based out of its Aspen, Colorado headquarters, Obermeyer currently offers a broad line of fashion ski apparel, including parkas, vests, ski suits, shells, ski pants, sweaters, turtlenecks, and accessories.
Although Sport Obermeyer has a global supply network, most production is done in Hong Kong and China by its partner, Obersport Limited. Established in 1985 as a joint venture between Klaus Obermeyer and Raymond Tse, Obersport is responsible for fabric and component sourcing for Sport Obermeyer’s entire production in the Far East. The materials are then cut or sewn either in Raymond Tse’s own “Alpine” factories or by independent subcontractors. Sport Obermeyer’s orders represent about 80 percent of Alpine’s annual production volume.
+ Paragraph – highlight of answers
Overview of Relevant Facts
+ Add paragraph – need results/answers from other group partners
Forecasting is arguably the most difficult and also most important part of the supply chain management process. Common factors used to make future sales forecasts include historical sales data, data from competitors of similar products, and estimation of future demand and economic conditions, and statistical averages and variations from this data collected. Forecasts are almost never 100% accurate, but supply chain managers must do their best to make as accurate projections as possible in order to be efficient with inventory, logistical costs, and production costs to ensure an overall profitable business endeavor for the company. According to our textbook, forecasting can be defined in to four general categories: Judgment Methods, Market Research Methods, Time-Series Methods, and Casual Methods. The first category, Judgment Methods, directly applies to the first question in this case study. Judgment Method involves the collection of expert opinions in a systematic way to attempt to accurately forecast future sales. Figure 2-20 in the Obermeyer case study shows sales projections for 10 Obermeyer women’s parka’s by a six person projection committee. Each committee member lists a sales estimate for each of the 10 different parka’s, with standard deviations listed to show an estimated variance of what the actual production will be from the estimated production. In order to make an accurate forecast, we will take the approach of minimizing risk as much as possible for Obermyer. Our projections are listed in the attached excel sheet. The method we used was to take the average demand from each of the six committee members for each product, subtract the standard deviation, and multiply in a variable (k) to get to the desired production level of 10,000 units. This strategy takes in to account demand uncertainty and expected demand. 2.
A quantifiable measure of risk that we will use with our ordering system is coefficient of variation. This is calculated by dividing standard deviation by average demand. Standard deviation is defined as a measure of how much demand tends to vary around the average, and coefficient of variation is the ratio of standard deviation to average demand. Coefficient of variation measures variability relative to average demand. Our calculations of coefficient of variation can be seen in the attached excel spreadsheet. The various coefficients of variation listed illustrated the risk involved with each projection. 3.
There are five...
Please join StudyMode to read the full document