Lockheed Martin has been approached by the Department of Defense to prepare a bid for
Starlight rocket launchers.
1. Use learning curve theory and prepare a cost bid for 15 Starlight rocket launchers given the following data for the prototype (i.e., the model). You must prepare a separate bid using (a) the cumulative average-time learning model, and (b) the individual unit-time learning model.
The prototype took 2,000 hours to produce and has the following cost information. The prototype can be sold as a part of the contract.
Direct materials $600,000
Direct labor (2,000 hours @ $200 per hour) $400,000 Variable direct manufacturing overhead (2,000 hours @ $100) $200,000 Other manufacturing overhead (20% of direct cost) $240,000
$1,440,000(i.e., 20% of direct materials, direct labor, and variable overhead)
In preparing each bid, integrate the learning curve into the bid by relying on the following historical data for the production of 16 Sky rocket launchers, which was the previous generation of the Starlight launchers.
|Unit Number||Labor Hours|
2. Prepare another bid that has no learning. Comment on the difference. Discuss the implications of these results for the company with respect to its labor policy.
Continue to Q2….
Question 2 (Please use Excel Dataset labeled “Marks and Spencer International (MSI)_Final Exam Data” for this question)
MSI, a department store chain, is trying to upgrade its customer service in order to compete with a rival chain which has recently moved into its territory and has a very strong customer-service reputation. MSI management knows that customer service is currently high in some of its stores but low in others. On average, its current reputation for service is less than outstanding. In order to build support for better customer service throughout the chain, MSI management decides to analyze existing data to show how much more profitable its own high-service stores are than its low-service stores.
MSI has created a customer-service indicator which is composed of a combination of ratings from “mystery shoppers” and surveys of customers by an independent organization. The scale for this indicator ranges from 1 to 60, which is a continuous variable with higher numbers indicating higher quality. MSI also has data on a number of factors that are likely to influence store profits. These include store size, rural versus urban location, manager performance rating (1 to 5 scale, where 5 is high), per capita income in the surrounding region (low to high ranges, summarized on a 1 to 5 scale, where 5 is high), non-managerial employee skill index (a measurement the Human Resources department has created, which ranges from 1 to 20; high numbers are better) and age of the store (which implies how long it has been in operation).
Based on regression analysis, what can you tell MSI about customer service? For example:
a. How big an effect on profit does customer service have?
b. Does customer service have a bigger effect on profits in some portions of the customer- service range than others? That is does the effect of customer service on profit have diminishing or increasing returns?
c. Is the effect of customer service on profit similar for large versus small stores?
d. Is the effect of customer service on profit similar for urban versus rural stores?
e. What are the factors that influence the level of customer-service quality?
Continue to Q3….
The RBC case describes two methods for computing the lifetime value of a customer. One method (Markov Chain and Transition matrices) takes into account the expected likelihood that a customer holding a particular product portfolio will migrate to another portfolio or leave the bank in the future.
Assume that RBC has only two products: Car Loan (CL) and Credit Card (CC). The annual profitability for each of the two products is (-$100) (i.e., $100 loss) for CL and +$1000 for CC.
RBC has made the following observation for customers in the 25-30 year segment:
· If they have a car loan at the end of a given year, the probability of also acquiring a credit card during the following year is 50%
· The probability of losing even this one product during the following year is 20%
· The probability that the customer retains only the car loan during the following year is 20%
· The probability that the customer drops the car loan but acquires a credit card during the following year is 10%
Similar observations can be made for individuals who begin the year with only a credit card, both products, or neither product. These observations are summarized in the following matrix of probabilities.