Superfun toys case study

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SuperFun Toys

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SuperFun Toys Case Study

SuperFun Toys Case Study

Children's toys have the opportunity for a high volume of sales during the months leading up to the holidays. SuperFun Toys is an innovative company looking to sell Weather Teddy, a teddy bear who predicts five different weather options based on a built-in barometer with surprising accuracy. SuperFun Toys is looking to order an appropriate quantity of Weather Teddy units directly correlated with demand using the company's sales forecaster predictions. The correct number of units can be ordered for the holiday season this summer and will be in stock by October, based on a normal probability distribution used as the demand distribution for the product. Adequate units must be ordered to avoid stock-outs and for the company to remain profitable. Multiple scenarios have been applied to ascertain profitability in opposition to stock-out.

Normal Distribution

The normal distribution is a widely-used distribution that plays an essential role in statistical process control, in this case for the purchasing department of SuperFun Toys. Using the normal distribution and accompanying probabilities to approximate the demand distribution, SuperFun Toys will purchase a reasonable amount of Weather Teddy Units (Black, 2017). While reviewing sales history of similar products, SuperFun's senior sales forecaster predicted the demand for 20,000 units with a 95% probability that product need would range between 10,000 and 30,000 units. When we assume that the sales distribution is normal, a mean of 20,000 units, and 95% falling between 10,000 and 30,000 units, a normal distribution table tells us that 1.96 standard deviations from the mean will give us 95% of the values needed. The calculation will be as follows:

Using this formula, we find that the ms 20,000 units with a standard deviation of 5,102

Probability of Stock-Out

The NORM.DIST function in Excel is used to figure the out-of-stock likelihood. The NORM.DIST function is (x, mean, standard deviation, cumulative). The x is the percent switched to a decimal; cumulative is always TRUE in samples and FALSE in mass. SuperFun's senior sales forecaster predicted an expected demand of 20,000 units with a 95% probability that needs would be between 10,000 units and 30,000 units. The possibility of a stock-out for the order quantities suggested by members of the management team including 15,000; 18,000; 24,000; 28,000 are as follows:

Units

Chance of Stock-out

15,000

69.15%

18,000

57.93%

20,000

50%

24,000

34%

28,000

21.19%

This information alone cannot be used to determine the number of units that SuperFun Toys should purchase. It is merely a small piece of the puzzle. There is a 69% probability that they would run out of the merchandise if they only bought 15,000 units of Weather Teddy. On the other hand, the likelihood of running out if they purchased 28,000 is 21%. If the company has extra toys, they run the risk of losing money, and 21% is a substantial risk of losing money. If the company selects to purchase the mid-range at 20,000 units, they have a 50% probability that they will run out of stock. However, they also have a 50% chance of selling all the toys they ordered. Consumer demand increases when a shortage of product exists which will bring customers into the store and increase the likelihood that they will buy another a toy.

Projected Profit

The projected profit varies based on the order quantities suggested by the management team under the three scenarios:

1. Pessimistic in which sales are 10,000 units

2. The most likely case in which sales are 20,000 units

3. Optimistic in which sales are 30,000 units.

The number of units ordered set the basis for the three sales amount scenarios; the cost of the units requested, and the profit earned depend on sales of all the toys. With 15,000 units ordered and the pessimistic outlook of 10,000 units sold the gain would be $25,000. In the most likely sales scenario where 20,000 units are sold the profit would be $160,000 and in the most optimistic case for 15,000 units ordered and 30,000 sold the advantage would be $240,000. For 18,000 units ordered and 10,000 units sold the profit is -$8,000. The most likely sales at 10,000 are profitable at $160,000, the same as ordering 15,000 units. For the optimistic sales of 30,000 units, the profit would be $240,000, again, same as the smaller order of 15,000 units. For the order of 20,000 units, the pessimistic sales amount of 10,000 units would yield a negative $30,000. The most likely sales of 20,000 units will generate $160,000 the same as if the lesser amounts if order size is 15,000 or 18,000 toys. The optimistic sales of 30,000 for the quantity of 20,000 units ordered remains the same at $240,000. With 24,000 units ordered and the pessimistic sales forecast of 10,000 units, the loss $74,000. In the most likely case, selling 20,000 units generates a $116,000 profit. Optimistic sales at 30,000 units would lead to a gain of $240,000. Purchasing 10,000 units, the pessimistic scenario, would result in a loss of $118,000. The purchase of 20,000 generates $72,000 whereas the ever-optimistic 30,000 units sold would produce the same advantage of $240,000 despite the order quantity.

Sales Under Three Scenarios

One of SuperFun's managers feels that profit potential is so great, having a 70% chance of meeting demand and a 30% chance of any stock-outs. With these figures, there are different order quantities that under this policy will result in different profits. Under this policy, a total of 22,622 units should be ordered to help reach this 70% chance of meeting demand goal. To calculate this, use the equation shown below:

K = 0.5244* 5000 + 20000= 22622

Under the pessimistic sales policy of 10,000 units, the total cost would be $361,920 with the profit hitting in the negative at -58,820. Under the likely sales policy, the total cost would stay the same, but sales would reach 20,000 units, resulting in profits of $131,180. Finally, if the optimistic sales policy were reached selling 30,000 units our best profit yet would be reached totaling $180,960.

Conclusion

After reviewing the different ideas of the management team and analyzing the various purchase and sales scenarios for SuperFun Toys, the research determines that sometimes less is more. The company must take into consideration: profitability, holiday season quantities bought, the cost and the forecasted sales predictions, and the possibility of stock-out to determine the optimal purchase quantity. The number of units ordered does not always increase profits. The analysis reveals that purchasing 24,000 units provides a lesser percentage of stock-out than buying a smaller quantity and generates the same high optimistic profit of $240,000; that is the same profit as if a higher quantity scenario, without the risk of having overstock after the holiday.

Reference

Black, K. (2017). Business Statistics for Contemporary Decision Making (9th ed.). Hoboken, NJ: Wiley.

demand 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 27000 28000 29000 30000 31000 32000 33000 34000 35000 36000 37000 38000 39000 40000 41000 42000 43000 44000 45000 46000 47000 48000 49000 50000 3.6003511391949702E-8 7.6152694155420297E-8 1.55003520501324E-7 3.0360836145972299E-7 5.7227104161600904E-7 1.03801992115952E-6 1.81186352788381E-6 3.0434148381289199E-6 4.91940638758298E-6 7.6520909625091303E-6 1.14541646973038E-5 1.6499189066123999E-5 2.2870604891943801E-5 3.0507637643477801E-5 3.9161145229594498E-5 4.8374675473323001E-5 5.75038101307132E-5 6.5779573701707703E-5 7.2410469604294395E-5 7.6705684474707604E-5 7.8193312505180901E-5 7.6705684474707604E-5 7.2410469604294395E-5 6.5779573701707703E-5 5.75038101307132E-5 4.8374675473323001E-5 3.9161145229594498E-5 3.0507637643477801E-5 2.2870604891943801E-5 1.6499189066123999E-5 1.14541646973038E-5 7.6520909625091303E-6 4.91940638758298E-6 3.0434148381289199E-6 1.81186352788381E-6 1.03801992115952E-6 5.7227104161600904E-7 3.0360836145972299E-7 1.55003520501324E-7 7.6152694155420297E-8 3.6003511391949702E-8 1.6380242316712401E-8 7.17152738622417E-9 3.02147410512345E-9 1.2250165841512599E-9 4.7794831628422502E-10 1.79446825594801E-10 6.4834549931303697E-11 2.2542033816606399E-11 7.5421570203152102E-12 2.4283647428719701E-12

z = (x - mu)/sigma = 1.96

sigma = (x - mu)/z

Sigma = (30,000-20,000) / 1.96 = 5,102 units.

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SuperFun Toys Case Study

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SuperFun Toys Case Study

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