Case Study: Sport Obermeyer

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Sport Obermeyer have to make the decision on the initial quantity for the production in Hong Kong. This decision should be based on their yearly average demand and the service level they want to offer to customers. They are currently not able to satisfy the demand for their most popular items and the analysis below shows that their optimal order quantity is higher than average demand.
No.
Style
Price
(in $)
Average Forecast
Revenue
1
Gail
110
1,017
111,870
2
Isis
99
1,042
103,158
3
Entice
80
1,358
108,640
4
Assault
90
2,525
227,250
5
Teri
123
1,100
135,300
6
Electra
173
2,150
371,950
7
Stephanie
133
1,113
148,029
8
Seduced
73
4,017
293,241
9
Anita
93
3,296
306,528
10
Daphne
148
2,383
352,684

Total

20,001
2,158,650
Table 2: Aggregated Demand
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At the optimal service level (OSL) the expected marginal cost and expected marginal benefit should be equal for a firm (Chopra & Meindl, 2013).

At the optimal service level,
Expected Marginal Benefit = Expected Marginal Cost

The optimal service level of 75% returns a Safety Stock Coverage Factor Z-value as 0.674. We can calculate optimal order quantity for each style of parka as,

This gives us the following optimal order quantities which are adjusted for an initial order of 10,000 and minimum quantity of 600 parkas.
Style
Avg. forecast
Standard deviation
2 * σ
Minimum sales
Optimal order quantity
Adjust. order
Minimal order quantity
Final
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1. Risk Measure

The risk associated with the above mentioned orders can be measured based on their coefficient of variation (CV) which is defined as the ratio of Standard Deviation and Average forecast. It quantifies the variation in each style of Parka which facilitated to leave out the style ‘Stephanie’ from the initial orders due to its very high CV of 0.94 (Table 4). It results in a huge difference between the average forecast and minimum expected sales for this style and Sport Obermeyer would be better off ordering this style later in March, once better predictions for this style can be garnered.
Style
average forecast
2 x standard deviation
Minimum expected sales
CV
Stability order with stability corrected stability
Order with corrected stability expected coverage of forecast assault 2,525
680
1,845
0.27
3.71
1,632
3.71
1,712
0.68 seduced 4,017
1,112
2,905
0.28
3.61
1,588

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