1. 1 1 Slide © 2001 South-Western College Publishing/Thomson Learning Anderson Sweeney Williams Anderson Sweeney Williams Slides Prepared by JOHN LOUCKS QUANTITATIVE METHODS FOR BUSINESS 8e QUANTITATIVE METHODS FOR BUSINESS 8e

2. 2 2 Slide Chapter 1 Introduction n Body of Knowledge n Problem Solving and Decision Making n Quantitative Analysis and Decision Making n Quantitative Analysis n Models of Cost, Revenue, and Profit n Quantitative Methods in Practice n The Management Scientist

3. 3 3 Slide Body of Knowledge n Quantitative methods for business involve rational approaches to decision making based on the scientific method of problem solving. n This body of knowledge is often referred to as management science, operations research or decision science. n It had its early roots in World War II and is flourishing in business and industry with the aid of computers.

4. 4 4 Slide Problem Solving and Decision Making n 7 Steps of Problem Solving (First 5 steps are the process of decision making) Identify and define the problem.Identify and define the problem. Determine the set of alternative solutions.Determine the set of alternative solutions. Determine the criteria for evaluating the alternatives.Determine the criteria for evaluating the alternatives. Evaluate the alternatives.Evaluate the alternatives. Choose an alternative.Choose an alternative. --------------------------------------------------------------------- --------------------------------------------------------------------- Implement the chosen alternative.Implement the chosen alternative. Evaluate the results.Evaluate the results.

5. 5 5 Slide Quantitative Analysis and Decision Making n Potential Reasons for a Quantitative Analysis Approach to Decision Making The problem is complex.The problem is complex. The problem is very important.The problem is very important. The problem is new.The problem is new. The problem is repetitive.The problem is repetitive.

6. 6 6 Slide Quantitative Analysis n Quantitative Analysis Process Model DevelopmentModel Development Data PreparationData Preparation Model SolutionModel Solution Report GenerationReport Generation

7. 7 7 Slide Model Development n Models are representations of real objects or situations. n Three forms of models are iconic, analog, and mathematical. Iconic models are physical replicas (scalar representations) of real objects.Iconic models are physical replicas (scalar representations) of real objects. Analog models are physical in form, but do not physically resemble the object being modeled.Analog models are physical in form, but do not physically resemble the object being modeled. Mathematical models represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses.Mathematical models represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses.

8. 8 8 Slide Advantages of Models n Generally, experimenting with models (compared to experimenting with the real situation): requires less timerequires less time is less expensiveis less expensive involves less riskinvolves less risk

9. 9 9 Slide Mathematical Models n Cost/benefit considerations must be made in selecting an appropriate mathematical model. n Frequently a less complicated (and perhaps less precise) model is more appropriate than a more complex and accurate one due to cost and ease of solution considerations.

10. 10 Slide Mathematical Models n Relate decision variables (controllable inputs) with fixed or variable parameters (uncontrollable inputs). n Frequently seek to maximize or minimize some objective function subject to constraints. n Are said to be stochastic if any of the uncontrollable inputs is subject to variation, otherwise are said to be deterministic. n Generally, stochastic models are more difficult to analyze. n The values of the decision variables that provide the mathematically-best output are referred to as the optimal solution for the model.

11. 11 Slide Transforming Model Inputs into Output Uncontrollable Inputs (Environmental Factors) Uncontrollable Inputs (Environmental Factors) ControllableInputs (Decision Variables) ControllableInputs Output (Projected Results) Output MathematicalModelMathematicalModel

12. 12 Slide The End of Chapter 1