Case Study – SuperFun Toys QNT/561 Version 9 University of Phoenix Material Case Study – SuperFun Toys SuperFun Toys, Inc., sells a variety of new and innovative children’s toys. Management learned the preholiday season is the best time to introduce a new toy because many families use this time to look for new ideas for December holiday gifts. When SuperFun discovers a new toy with good market potential, it chooses an October market entry date. To get toys in its stores by October, SuperFun places one-time orders with its manufacturers in June or July of each year. Demand for children’s toys can be highly volatile. If a new toy catches on, a sense of shortage in the marketplace often increases the demand to high levels and large profits can be realized. However, new toys can also flop, leaving SuperFun stuck with high levels of inventory that must be sold at reduced prices. The most important question the company faces is deciding how many units of a new toy should be purchased to meet anticipated sales demand. If too few are purchased, sales will be lost; if too many are purchased, profits will be reduced because of low prices realized in clearance sales. This is where SuperFun feels that you, as an MBA student, can bring value. For the coming season, SuperFun plans to introduce a new product called Weather Teddy. This variation of a talking teddy bear is made by a company in Taiwan. When a child presses Teddy’s hand, the bear begins to talk. A built-in barometer selects one of five responses predicting the weather conditions. The responses range from “It looks to be a very nice day! Have fun” to “I think it may rain today. Don’t forget your umbrella.” Tests with the product show even though it is not a perfect weather predictor, its predictions are surprisingly good. Several of SuperFun’s managers claimed Teddy gave predictions of the weather that were as good as many local television weather forecasters. As with other products, SuperFun faces the decision of how many Weather Teddy units to order for the coming holiday season. Members of the management team suggested order quantities of 15,000, 18,000, 24,000, or 28,000 units. The wide range of order quantities suggested indicates considerable disagreement concerning the market potential. Having a sound background in statistics and business, you are required to perform statistical analysis and the profit projections which is typically done by the product management group. You want to provide management with an analysis of the stock-out probabilities for various order quantities, an estimate of the profit potential, and to help make an order quantity recommendation. SuperFun expects to sell Weather Teddy for $24 based on a cost of $16 per unit. If inventory remains after the holiday season, SuperFun will sell all surplus inventories for $5 per unit. After reviewing the sales history of similar products, SuperFun’s senior sales forecaster predicted an expected demand of 20,000 units with a 95% probability that demand would be between 10,000 units and 30,000 units. Copyright © 2017 by University of Phoenix. All rights reserved. 1 Order Quantity Purchase Cost per unit 15,000 $ 16.00 Sales Pessimistic Likely Optimistic Order Quantity Purchase Cost per unit Order Quantity Total Cost 10,000 20,000 30,000 Pessimistic Likely Optimistic $ Order Quantity Total Cost 10,000 20,000 30,000 Pessimistic Likely Optimistic 18,000 18,000 18,000 Order Quantity Total Cost 10,000 20,000 30,000 Total Revenue @ $24.00 @ $5.00 20,000 20,000 20,000 24,000 $ 16.00 Sales Pessimistic Likely Optimistic Order Quantity Purchase Cost per unit Total Revenue @ $24.00 @ $5.00 20,000 16.00 Sales Order Quantity Purchase Cost per unit 15,000 15,000 15,000 18,000 $ 16.00 Sales Order Quantity Purchase Cost per unit Total Revenue @ $24.00 @ $5.00 Order Quantity Total Cost 10,000 20,000 30,000 Total Revenue @ $24.00 @ $5.00 24,000 24,000 24,000 28,000 $ 16.00 Sales Pessimistic Likely Optimistic Order Quantity Total Cost 10,000 20,000 30,000 28,000 28,000 28,000 Total Revenue @ $24.00 @ $5.00 Profit Profit Profit Profit Profit Order Quantity Purchase Cost per unit $ 16.00 Sales Pessimistic Likely Optimistic Order Quantity Total Cost 10,000 20,000 30,000 Total Revenue @ $24.00 @ $5.00 Profit Order Quantity Order Quantity Order Quantity Order Quantity Order Quantity Mean Standard Deviation 15,000 18,000 20,000 24,000 28,000 21000 5099.02 Middle 68% Middle 95% Middle 99.7% 15900.98 15900.98 10802 5702.94 26099.02 26099.02 31198 36297.1 10801.96097 31198.03903 5702.941459 36297.05854 Rule 1 Rule 2 Rule 3 Standard Deviation 0.00009 0.00008 0.00007 0.00006 0.00005 0.00004 0.00003 0.00002 0.00001 0 Mean Standard Deviation 21000 5099.02 Standard Deviation Increments Mean 21,000.00 Stdev 5,099.02 -Middle 68% -Middle 95% -Middle 99.7% -4 STDEV -3 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 Your Data Normalize 5702.94 8.69157E-07 6212.842 1.16739E-06 6722.744 1.55235E-06 7232.646 2.04371E-06 7742.548 2.66384E-06 8252.45 3.43758E-06 8762.352 4.39193E-06 9272.254 5.55539E-06 9782.156 6.95714E-06 10292.06 8.62589E-06 10801.96 1.05885E-05 11311.86 1.28683E-05 11821.76 1.54834E-05 12331.67 1.84445E-05 12841.57 2.17534E-05 13351.47 2.54005E-05 13861.37 2.9364E-05 14371.27 3.36081E-05 14881.18 3.8083E-05 15391.08 4.27243E-05 15900.98 4.74544E-05 16410.88 5.21836E-05 16920.78 5.68132E-05 17430.69 6.1238E-05 17940.59 6.53507E-05 18450.49 6.90457E-05 18960.39 7.22237E-05 19470.29 7.47963E-05 19980.2 7.66898E-05 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 20490.1 21000 21509.9 22019.8 22529.71 23039.61 23549.51 24059.41 24569.31 25079.22 25589.12 26099.02 26608.92 27118.82 27628.73 28138.63 28648.53 29158.43 29668.33 30178.24 30688.14 31198.04 31707.94 32217.84 32727.75 33237.65 33747.55 34257.45 34767.35 35277.26 35787.16 36297.06 7.78488E-05 7.8239E-05 7.78488E-05 7.66898E-05 7.47963E-05 7.22237E-05 6.90457E-05 6.53507E-05 6.1238E-05 5.68132E-05 5.21836E-05 4.74544E-05 4.27243E-05 3.8083E-05 3.36081E-05 2.9364E-05 2.54005E-05 2.17534E-05 1.84445E-05 1.54834E-05 1.28683E-05 1.05885E-05 8.62589E-06 6.95714E-06 5.55539E-06 4.39193E-06 3.43758E-06 2.66384E-06 2.04371E-06 1.55235E-06 1.16739E-06 8.69157E-07 
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