Question 1 (20 points) (Please use the excel data file “Q1 FAB Final Exam 2021 Data” to answer this question)

Fab Machines produces a light weight, highly reliable laser color printer. The technology for this printer was developed in an in-house R&D program, and no competitor has been able to match   the quality of Fab printers. Demand has been growing rapidly and the estimated production for   2021 is 1,000,000 units. The excel file “Q1 FAB Final Exam 2021 Data” contains data on the     number of printers produced per year and the unit cost. Fab wants to provide an analyst forecast for the 2021unit cost per printer. Additionally, Fab wants to develop a cost budget for 2022         based on the data. Based on the data:

a.   Use learning curve theory prepare an analyst forecast of unit cost per printer for 2021   given the data in the excel file. You must prepare a separate forecast using (a) the         cumulative average-time learning model, and (b) the individual unit-time (incremental) learning model.

b.   Use learning curve theory prepare a budgeted unit cost per printer for 2022 given the data in the FAB excel file model. You must prepare a separate forecast using (a) the                cumulative average-time learning model, and (b) the individual unit-time (incremental)    learning model.

c.   Express the learning curve for the company in percentage terms under (a) the cumulative average-time learning model, and (b) the individual unit-time (incremental) learning       model

d.   Discuss the implications of these results for the company with respect to its human       resource policy (e.g., retention, turnover, training, incentive bonus, etc.). Be concrete in your suggestions and show numbers where possible.

Question 2 (20 points) (Please use the Excel data file labeled “Q2 MSI Store Data_Final Exam 2021 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? Be concrete and specific and provide numerical support for your logic based on your estimation results in the previous parts of the question.

Question 3 (20 points)

PART A

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.

Probabilities for year t+1 product mix for all possible combinations of product mixes

for year t.

Answer the following questions using a 8% discount rate:

a.   Consider a customer who has only a credit card in year t.  What is

i.      The expected profit generated by this customer in year t+1?

ii.      The present value of expected profits from this customer for both years combined?

b.   Consider a customer who has both a car loan and credit card in time t.  What is

i.      The expected profit generated by this customer in year t+1?

ii.      The present value of expected profits from this customer for both years combined?

c.   Discuss how these results will be useful for you to design a market strategy. Be specific in your suggestions.