MGT 391 Quantitative Business Analysis Lecture Notes PDF

MGT 391 MGT 391 Quantitative Business Analysis Motivation Managers would often face complex problems and must analyze the problems in order to make decisions. In dealing with complex problems, why each people’s approach, analysis, and decisions are different even in the same problems? Bei...

MGT 391 MGT 391 Quantitative Business Analysis Motivation Managers would often face complex problems and must analyze the problems in order to make decisions. In dealing with complex problems, why each people’s approach, analysis, and decisions are different even in the same problems? Being different means that someone might make wrong analysis and decision in dealing with problems. How can we decrease (or minimize) the chance that people make wrong analysis and decision in dealing with problems ? We thus need a structured approach and analysis in order to reduce the chance to make wrong decisions. The main purpose of this course is to is to suggest a structured approach(es) to help students (i.e., decision makers) deal with multiple objectives and uncertainty, leading them to make better decisions. (Let’s first see two problems and see why people can choose different approach, analysis and decision in the same problems.) Why each people’s approach, analysis, and decisions are different even in the same problems? What kinds of problems are we dealing with at MGT 391? Example 1 ‒ A small printing and photocopying business must move from its exiting office because the site has been acquired for redevelopment. ‒ The owner of the business is considering seven possible new offices, all of which would be rented. ‒ While the owner would like to keep his costs as low as possible, he would also like to take other factors into account – size, closeness to potential customers, visibility (much business is generated from people who see the office while passing by), comfort for staffs, etc. ‒ The owner is unsure about how to make his choice, given the number of factors involved. ‒ Which offices would be best choices for the owner? What kinds of problems are we dealing with at MGT 391? Issues in Example 1 ‒ The owner want to not only minimize costs, but also maximize benefits. He may put more weight on costs over benefits or vice versa. ‒ In addition, there are diverse attributes in the benefits. What would be weight for each attribute? ‒ For example, What kinds of problems are we dealing with at MGT 391? Issues in Example 1 ‒ The owner want to not only minimize costs, but also maximize benefits. He may put more weight on costs over benefits or vice versa. ‒ In addition, there are diverse attributes in the benefits. What would be weight for each attribute? ‒ For example, some owners may put weight like ‘A’ , some owners may put weight like ‘B’, ‘C’, etc Case A Case B Case C 10 30 70 100 ‒ One single problem is dealing with multiple objectives, which potentially lead to different analysis process and accordingly results. What kinds of problems are we dealing with at MGT 391? Example 2 ‒ A businesswoman who is organizing a business equipment exhibition has to choose two venues – the Luxuria Hotel and the Maxima Center. ‒ Attendance is uncertain for each location. ‒ For example, if she chooses the Luxuria Hotel, she may have a 60 % chance of achieving a high attendance and hence a profit of $30,000. ‒ However, there is a 40% chance that attendance will be low; her profit will be just $11,000. ‒ If she chooses the Maxima Center, she may have a 50 % chance of achieving a high attendance and hence a profit of $60,000. ‒ However, there is a 50% chance that attendance will be low, leading to a loss of $10,000. What kinds of problems are we dealing with at MGT 391? Issues in Example 2 Probably, an initial idea for this problem is a simple expectation approach. ‒ Based on a simple expectation approach Luxuria  0.6*30,000 + 0.4*11000 = 22,400 Maxima  0.5*60,000 - 0.5*10000 = 25,000 ‒ You may want to choose Maxima over Luxuria. ‒ Really? ‒ Image again that you can lose $ 10,000 with a 50% chance. ‒ $ 10,000 loss with a 50% chance is not a big deal in reality ? What if we change the unit to ‘Million’ dollar? What kinds of problems are we dealing with at MGT 391? ‒ Based on a simple expectation approach Luxuria  0.6*30,000 + 0.4*11000 = 22,400 Maxima  0.5*60,000 - 0.5*10000 = 25,000 ‒ You may want to choose Maxima over Luxuria. Really? ‒ Image that you can lose $ 10,000 with a 50% chance. ‒ $ 10,000 loss with a 50% chance is not a big deal in reality ? $3 million ‒ What if the profit and loss is 6 digit (i.e., million dollar)? Luxuria  0.6*30,000 + 0.4*11000 = 2.24 Million Maxima  0.5*60,000 - 0.5*10000 = 2.50 Million $1.1million ‒ Still losing $ 1 million loss with a 50% chance is not a big $6 million deal in reality ? ‒ Expectation approach may not be fully consider your concern. ‒ Many problems includes uncertainty so thus in reality we should - $1 million include your attitude to risk, different analysis process and accordingly results. Course Schedule Main Issues Multiple Objectives Uncertainty Chapter 2 Chapter 5 ‒ How decision makers make ‒ Probability introduction intuitive decisions involving multiple objectives (without Chapter 6 and 7 a structured approach). ‒ Decision making under uncertainty. Chapter 3 ‒ Structured approach Chapter 9 involving multiple objectives. ‒ Value of Information 10