Management Science - Modules 1 and 2

• Management science is an approach to decision making based on the scientific method, makes extensive use of quantitative analysis
• Management science, operations research, decision science

  Widely known and accepted names for the body of knowledge involving quantitative approaches to decision making
• Two developments that occurred during the post–World War II period led to the growth and use of management science in non-military applications
• Developments that led to growth of management science

  • Continued research resulted in numerous methodological developments
  • Discovery by George Dantzig, in 1947, of the simplex method for solving linear programming problems
  • Computers enabled practitioners to use the methodological advances to solve a large variety of problems
• Problem solving


  1. Identify and define the problem

  2. Determine the set of alternative solutions

  3. Determine the criterion or criteria that will be used to evaluate the alternatives

  4. Evaluate the alternatives

  5. Choose an alternative

  6. Implement the selected alternative

  7. Evaluate the results to determine whether a satisfactory solution has been obtained
• Decision making


  The first five steps of the problem-solving process
• Decision making ends with the choosing of an alternative, which is the act of making the decision
• Alternatives for the decision problem

  • Accept the position in Rochester
  • Accept the position in Dallas
  • Accept the position in Greensboro
  • Accept the position in Pittsburgh
• Single-criterion decision problems


  Problems in which the objective is to find the best solution with respect to one criterion
• Multi-criteria decision problems


  Problems that involve more than one criterion
• Qualitative analysis is based primarily on the manager's judgment and experience
• Quantitative analysis encompasses most of the subject matter of this module
• Reasons why a quantitative approach might be used in the decision-making process

  • The problem is complex, and the manager cannot develop a good solution without the aid of quantitative analysis
  • The problem is especially important (e.g., a great deal of money is involved), and the manager desires a thorough analysis before attempting to make a decision
  • The problem is new, and the manager has no previous experience from which to draw
  • The problem is repetitive, and the manager saves time and effort by relying on quantitative procedures to make routine decision recommendations
• Iconic models


  Models that are physical replicas or scalar representations of real objects
• Analog models


  Models that are physical in form but do not have the same physical appearance as the object being modelled
• Mathematical models


  Models that include representations of a problem by a system of symbols and mathematical relationships or expressions
• Decision variables (controllable inputs)


  Inputs that are controlled or determined by the decision maker
• Uncontrollable inputs (fixed or variable parameters)


  Environmental factors that are not under the control of the manager or decision maker
• Objective function


  A mathematical expression that describes the problem's objective
• Constraints


  The set of restrictions in the mathematical model
• Stochastic or probabilistic models

  Models where any of the uncontrollable inputs is subject to variation
• Deterministic models


  Models where the uncontrollable inputs are not subject to variation
• Data refer to the values of the uncontrollable inputs to the model
• Model solution


  Identify the values of the decision variables that provide the "best" output for the model
• Optimal solution


  The specific decision-variable value or values providing the "best" output
• Report generation


  Includes the recommended decision and other pertinent information about the results
• Infeasible


  A decision alternative that does not satisfy one or more of the model constraints
• Report Generation


  1. Recommended decision

  2. Other pertinent information about the results (e.g. sensitivity of model solution to assumptions and data)
• Some of the most basic quantitative models arising in business and economic applications are those involving the relationship between a volume variable—such as production volume or sales volume—and cost, revenue, and profit
• Fixed cost


  The portion of the total cost that does not depend on the production volume; this cost remains the same no matter how much is produced
• Variable cost


  The portion of the total cost that is dependent on and varies with the production volume
• Cost-Volume Model


  Total cost = Fixed cost + Variable cost
• If the decision is to produce 1200 units, the total cost would be $5400
• Marginal cost


  The rate of change of the total cost with respect to production volume; the cost increase associated with a one-unit increase in the production volume
• Revenue-Volume Model


  Total revenue = Price per unit * Sales volume
• Marginal revenue


  The rate of change of total revenue with respect to sales volume; the increase in total revenue resulting from a one-unit increase in sales volume
• Profit-Volume Model


  Total profit = Total revenue - Total cost
• If sales are expected to be 500 units, a loss of $1500 is predicted
• If sales are 1800 units, a profit of $2400 is predicted
• Breakeven point


  The volume that results in total revenue equaling total cost (providing $0 profit)