Practice Exam Questions

ISyE 6203

Spring 2007

Vande Vate

1. In our discussion of the Sport Obermeyer case, we used return on investment as a surrogate measure of risk with the idea that riskier investments demand a higher return.

  1. Discuss the strengths and weaknesses of this measure of risk for Sport Obermeyer.
  2. Propose a better measure of risk that can be computed from data available in the case.
  3. Show how to determine how much of which products to source from Hong Kong and China in the first phase, i.e., the first 10,000 units. (Data available below)

Individual Forecasts
Style / Price / Laura / Carolyn / Greg / Wendy / Tom / Wally / Average / Std. Dev / 2X Std Dev
Gail / $ 110.00 / 900 / 1,000 / 900 / 1,300 / 800 / 1,200 / 1,017 / 194 / 388
Isis / $ 99.00 / 800 / 700 / 1,000 / 1,600 / 950 / 1,200 / 1,042 / 323 / 646
Entice / $ 80.00 / 1,200 / 1,600 / 1,500 / 1,550 / 950 / 1,350 / 1,358 / 248 / 496
Assault / $ 90.00 / 2,500 / 1,900 / 2,700 / 2,450 / 2,800 / 2,800 / 2,525 / 340 / 680
Teri / $ 123.00 / 800 / 900 / 1,000 / 1,100 / 950 / 1,850 / 1,100 / 381 / 762
Electra / $ 173.00 / 2,500 / 1,900 / 1,900 / 2,800 / 1,800 / 2,000 / 2,150 / 404 / 807
Stephanie / $ 133.00 / 600 / 900 / 1,000 / 1,100 / 950 / 2,125 / 1,113 / 524 / 1,048
Seduced / $ 73.00 / 4,600 / 4,300 / 3,900 / 4,000 / 4,300 / 3,000 / 4,017 / 556 / 1,113
Anita / $ 93.00 / 4,400 / 3,300 / 3,500 / 1,500 / 4,200 / 2,875 / 3,296 / 1047 / 2,094
Daphne / $ 148.00 / 1,700 / 3,500 / 2,600 / 2,600 / 2,300 / 1,600 / 2,383 / 697 / 1,394
Total / 20,000 / 20,000 / 20,000 / 20,000 / 20,000 / 20,000 / 20,000
Cut and Sew Capacity
3000 / Units/month
7 / month period
First Phase Commitment
10,000 / units
Second Phase Commitment
10,000 / units

2. Another approach BMW might take to deciding how much of each engine type to ship to the plant via ocean each week is to view the decision as a News Vendor problem. This perspective on the problem is a blend of the two versions we discussed in class since it is intended to trade off the costs of carrying inventory if we send too much against the costs of expediting if we send too little. For simplicity assume we make weekly ocean shipments.

  1. The first issue in this context is to develop a distribution of demand at the plant recognizing that:
  2. we can only estimate what inventory will be when this shipment arrives and
  3. we can only estimate what demand will be in the week after it arrives

How would you go about developing an estimate of this distribution? What data

would you require?

  1. The second issue in this context is to estimate the relative costs of inventory and expediting. From past experience we pay $200 in airfreight for each engine we expedite. The value of an engine is about $2,000 and we estimate our inventory carrying charge to be about 20%. Given these values, suggest the appropriate target probability P, i.e., we should choose the shipment quantity Q so that the probability that the demand in the weeks after the shipment arrives is less than Q equals P:

Prob(demand <= Q) = P.

  1. The final issue is implementation. Discuss how to estimate the expected level of inventory in the plant when the shipment arrives and how to incorporate this quantity into the decision of how much to ship by ocean this week.

3. GE Appliances faced the decision of whether to use rail or truck to for transport from its plant in Tennessee to its distribution center on the east coast. The company is trying to maintain a 98% level of service at the east cost DC.

Relevant Information

Rail / Truck / Units
TransAverage Transit Time / 14 / 2.5 / days
Std Deviation in Transit / 2 / 0.5 / days
Operates / 7 / 5 / days per week
Freight Cost / $10 / $15 / per unit
Frequency / 5 / 5 / Days per week
Average Production/Demand / 100 / units per week day
Std Deviation in Demand / 5 / units per day
Product value / 500 / $ per unit
Holding cost / 20% / per year
Week days per year / 250 / r
Service Level Target / 98%

Since this is but one of many products we ship between these two locations, assume the quantity we ship each weekday of this product matches the daily demand for this product. In other words, do not try to calculate cycle inventory from the capacity of the conveyance (truck or rail).

  1. Estimate the total cost involved in using each mode exclusively.
  2. Which source of variability is a more significant driver of total cost for Rail? Variability in demand or variability in transit time (specifically, if each were to increase separately by 10%, which would have the greater impact on total cost)
  3. If rail were only available one day a week, how would this impact the calculations?