Quantitative Methods for Management (QMM) MBA 2011/12 Section C,
Prof. Miguel Angel Ariño The main task of a manager is to make decisions. To make decisions managers need information. Information is spread in a great variety of forms: experience of the managers, judgements and opinions, and mainly information is stored in form of data. The goal of this course is t learn to extract information contined in data that can help managers to make better decisions. The course is a half credit course with 12 sesions. But for the first one, all the sessions are case dicussions. Follow the instructions for each case and come to class having worked on the case. Data for the cases are in the “QMMdata2012C.xls” excel file The evaluation of the course is based on class participation, individual assignaments, some possible quizz, team assignment and the final team project‐exam. Distribute yourself on teams of 3 at your choice, and inform me about the composition of teams no later than Tuesday April 17th. Pay attention that individual assignaments have to be presented at the begining of the 4th and 8th class. A last assignaments is due at the begining of the 10th class. It is a (3 members) team assignament. At the end there will be a (3 members) team based project‐exam. Outline of the Course 1. Tuesday 27/MAR/12: Introduction to the course This first session will be a lecture. Readings:  After the class you can read the technical note ADN‐248‐E ‘Forecasting in companies’.  At some moment during this first week of the course, it would be useful to read and work through the document ADN‐251‐E ‘The normal distribution’ (only pages 1 to 6).  To understand better some statistical concepts, read the note ADN‐258 ‘Measuring uncertainties: Probability Functions’. 2. Wednesday 28/MAR/12: Forecasting Sales I Case: AD‐317‐E ‘Forecasting Sales of The Gap’. Assignment: Prepare a forecast of the sales of The GAP Co. for 1996 with an explanation of how you got that forecast. After the class you may start reading the note TMN‐154‐E ‘Time Series: Classical Decomposition’ 1
3. Thursday 29/MAR/12: Forecasting Sales II Case: AD‐317‐E ‘Forecasting Sales of The Gap’. Assignment: Prepare a forecast of the sales of The GAP Co. for each quarter of 1996 with an explanation of how you got that forecast. After the class you may continue reading the note TMN‐154‐E ‘Time Series: Classical Decomposition’. 4. Tuesday 10/APR/12: Forecasting Sales III Case: AD‐318‐E ‘RapidTent’. Assignment: Prepare a forecast of the sales of RapidTent for 2009 and for each month of that year. Which model have you used to make these forecasts? 1st individual assignment to hand in at the beginning of the session: Follow instructions given in the second worksheet (called 1st Assignment) of the data set “QMMdata2012C.xls” excel file 5. Wednesday 10/APR/12: Forecasting Sales IV Case: AD‐319‐E ‘Hausdorf Company’. Assignment: How would you organize forecasts in Hausdorf Company? 6. Monday 16/APR/12: Linear regression I Case: AD‐306 ‘Iron Industries, Inc.’. Assignment:  Is there any relationship between the cost of processing a ton of residuals and the capacity of a plant?  Is there any way to predict cost from other variables?  What price would you quote for recycling a ton of residuals in a 500.000 tons capacity plant? At the end of the class you can read the Note ADN‐252‐E ‘Simple linear regression’ 7. Tuesday 17/APR/12: Linear regression II Case: The Hot Dog Pricing [QMM‐4]. Assignment: Answer the questions proposed at the end of the exercise 2
8. Monday 23/APR/12: Linear regression III Case: Fridge pricing [QMM‐3]. Assignment:  Find the factors that affect Energy costs and Price of refrigerators  Answer the questions proposed at the end of the exercise 2nd Individual assignment to hand in at the beginning of the session: Present the best possible model that explains price of a refrigerator as a function of other variables. 9. Tuesday 24/APR/12: Classification I Case: The Churn Model [QMM7] In this case we pretend to anticipate what clients that will churn in the future and to think about what we can offer them for them not to churn.  Develop a profile of the potential churner, i.e. identify the characteristics of the customers most likely to churn.  Develop a regression model for predict churn Readings: You should read Chapter 8 of the document ‘Quantitative methods in management’ 10. Wednesday 2/MAY/12: Classification II Case: The Churn Model [QMM7] (Cont.) In this session we will continue with ‘The churn model’ case. Follow the instructions given at the end of the previous session. 3rd assignment to hand in at the beginning of the session (elaborated by teams of three members): Follow instructions given in the previous session. 11. Thursday 3/MAY/12: Linear regression IV Case: HBS 9‐171‐248 ‘Harmon Foods, Inc.’. Assignment:  Which model do you think makes more sense?  What do you learn from the coefficients of model selected?  What improvements would you recommend to the product manager in terms of timing and amount of promotions? 3
12. Friday 4/MAY/12: Linear regression V Case: Customer fidelity at By‐Smart [QMM‐6]. Assignment: Answer the questions proposed at the end of the exercise Evaluation The evaluation will be based on the subjective assessment made by the professor about your performance in the course. This assessment will based on:  Class participation.  Assignments.  Possible quizzes.  The final project. 4