Sport Obermeyer started to make firm commitments for producing its 1993-1994 line of fashion skiwear with scant information about how the market would react to the line. Inaccurate forecasts of retailer demand had become a growing problem at Obermeyer: in recent years greater product variety and more intense competition had made accurate predictions increasingly difficult. Also, another issue Sport Obermeyer faced was how to allocate production between factories in Hong Kong and China. They need consider all aspects in a short-term period and also a long-term period.
1. 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.
In order to decide how many units of each style Wally should make, we should think about their order’s range, we ignore price differences among styles so we need to think about the maximum order and minimum order for each style. First of all, we need to calculate the stock out probability based on benefit percentage and risk percentage. According to the case, Obermeyer earned 24 percent of wholesale price on each parka it sold, and that units left unsold at the end of the season were sold at a loss that average 8 percent of wholesale price. Therefore, the stock out probability equal to 8%/(24%+8%)=25% so there are 75% probability of being less than mean+0.67*SD (standard deviation). According to z table, z equal to 0.67 when probability is 0.75. Therefore, we can calculate quantity for each style include the risk of stock out by using formulate Q*=mean+z*SD. Therefore, we can get the maximum order units for each style in order to avoid stock out.
|Style |Price |Average |Standard |2*standard |P=1-8%/(24%+8|Z |Q*=Average+z*SD | | | |forecast |deviation |deviation |%) | | | |Gail |110 | |194 |388 |0.75 |0.67 | | | | |1,017 | | | | |1,277 | |Isis |99 | |323 |646 |0.75 |0.67 | | | | |1,042 | | | | |1,475 | |Entice |80 | |248 |496 |0.75 |0.67 | | | | |1,358 | | | | |1,690 | |Assault |90 | |340 |680 |0.75 |0.67 | | | | |2,525 | | | | |2,981 | |Teri |123 | |381 |762 |0.75 |0.67 | | | | |1,100 | | | | |1,611 | |Electra |173 | |404 |807 |0.75 |0.67 | | | | |2,150 | | | | |2,691 | |Stephanie |133 | |524 |1048 |0.75 |0.67 | | | | |1,113 | | | | |1,815 | |Seduced |73...
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