Question

Please help! especially part c,d,e.

part a answer:

Excel   = Norm.Inv (0.889, 2500, 400)

            = 2989

part b answer:

Excel               = Norm.inv (0.95, 2500, 400)

                        = 3158

ABC Sportswear company designs and sells wetsuits to the U.S. market. The designs of the wetsuits are updated each year. The process for updating the design typically starts in January the year before the designs are to be released. At this time, the purchasing, design, and sales departments have a two-day meeting to discuss the product portfolio for the upcoming year, including design, functionality, and price. In March, the designs are finalized, and the purchasing department starts negotiating with suppliers across the world. Production usually starts in September or October the year before the designs are to be released, and lasts until December or January depending on the supplier. In February, retailers start placing their orders to ABC, with retail sales of the new designs typic ally starting in early April. The season stretches from April to September. As the season progresses, retailers place replenishment orders to ABC, who supplies the retailers from a central warehouse. Sales peak in early summer. Towards the end of the summer, sales decline dramatically. When the season ends in September, ABC pushes out any remaining inventory to the retailers by offering all models at only 25% of the original selling price Consider a specific SKU# 001237 (Model A200, size L) For the upcoming year, ABC has negotiated with a supplier to have SKU# 001237 produced and delivered to ABC at $175 per unit. SKU# 001237 is sold to retailers at $225, for a unit margin of $50 Suppose total demand for SKU# 001237 for the upcoming season (the time during which the product is sold at full price) is estimated to follow a Normal distribution with mean 2,500 and standard deviation 400 a) How many A200, size L, should ABC produce to maximize expected profft? Please round to closest integer (5 marks) Last year, SKU# 001237 turned out to be extremely popular and sold out quickly. Since ABC makes a good margin on the product, management has decided they do not want the same situation again. "We cannot have the probability for out-of-stock during the season to be more than 5% on this model", said the sales manager recently b) How many SKU# 001237 should ABC produce to ensure that the stock-out probability is exactly 5% during the season? Assume the same demand distribution as in Part 1. Please round to closest integer. (5 marks) Consider the policy from Part b. c) With your suggested production quantity, how many units of SKU#001237 can ABC expect to be short during the regular season? Please answer with two decimals. (5 marks) Consider the policy from Part b. d) What is the expected contribution to profit from SKU# 001237? Please round to closest integer dollars. Answer without the dollar symbol (5 marks) The policy suggested by the sales manager leads to a lower expected profit than the "optimal" policy calculated in Part a. The sales manager motivates this by claiming that the policy in Part a is not taking into account future lost profits by being out of stock. "So", he argues, "the expected profit implied by the 'optimal policy' is biased upwards." Suppose there is an additional cost per unit out-of-stock, y. Suppose further that the policy suggested in Partb is the policy that maximizes expected contribution when this additional cost is taken into account. e) What is the additional cost y? Please round to closest integer dollars. Answer without the dollar symbol. (5 marks)

Answer 1

ASWER:

Given that,

a)

Underage cost, Cu = unit margin = $ 50

Overage cost, Co = Cost - selling price at 25% off the original retail price = $ 175 - 225*(1-25%) = $ 6.25

Critical ratio = Cu/(Cu+Co) = 50/(50+6.25) = 0.8889

z value = NORMSINV(0.8889) = 1.2206

Optimal quantity ABC should purchase = 2500+1.2206*400 = 2,988

b)

z value = NORMSINV(.95) = 1.6449

Optimal quantity ABC should purchase = 2500+1.6449*400 = 3,158

c)

For z = 1.6449, L(z) =NORMDIST(1.6449,0,1,FALSE)-1.6449*NORMSDIST(-1.6449) = 0.0209

Expected number of units short, L = L(z)*SD = 0.0209*400 = 8.36

d)

Expected Sales, S = 2500 - 8.36 = 2491.64

Expected inventory, V = Q - S = 3158 - 2491.64 = 666.36

Expected contribution to profit = S*Cu - V*Co

= 2491.64*50 - 666.36*6.25

= $ 120,417.25

e)

For ordering policy in part b to be optimal,

Cu/(Cu+Co) = 0.95

Substituting Co = 118.75, in the above equation, and solving for Cu, we get

Cu = 0.95*6.25/(1-0.95)

= $ 118.75

Additional cost per unit out of stock, $\gamma$ = 118.75-50

= $ 68.75