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- MNG 2201 – Management Science

- MNG 2201 – Management Science. Troy J Wishart. Assume the Position. Management Science. Lecture Times Mondays 5:15 – 7:10 Wednesdays 8:15 – 9:10 Tutorial Times – 5 hours. Management Science. Lecture Notes

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- MNG 2201 – Management Science

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  1. - MNG 2201 – Management Science Troy J Wishart

  2. Assume the Position

  3. Management Science • Lecture Times • Mondays 5:15 – 7:10 • Wednesdays 8:15 – 9:10 • Tutorial Times – 5 hours

  4. Management Science • Lecture Notes • Main Text - Taylor, W.B. (2007) Introduction to Management Science. 9th edition. Pearson Prentice Hall. • Power Point Presentations • Can be downloaded from: • www.troywishart.wordpress.com

  5. We Believe that Death and Life is in the Power of the Tongue… I am getting an ‘A’ in this Course

  6. Introduction to Management Science Lecture 1

  7. Definition • Management Science (MS), an approach to managerial decision making that is based on the scientific method and makes extensive use of quantitative analysis.

  8. Definition • A Scientific Approach to solving management problems • Help managers make better decisions. • Known as Operations Research (OR) • Both terms are used interchangeably.

  9. History • The Scientific Management Revolution began in the early 1900s, • Initiated by Frederic W. Taylor, • It provided the foundation for MS/OR.

  10. History • Both originated during the World War II period. • Operations Research Teams were formed to deal with Strategic And Tactical Problemsfaced by the military.

  11. History • These teams, which often consisted of people with Diverse Specialties • (e.g. Mathematicians, Engineers, And Behavioural Scientists), • After the war, - continued their research on quantitative approaches to decision making.

  12. Problem Solving • Problem Solving can be defined as the process of identifying a difference between some actual and some desired state of affairs and then taking action to resolve the difference.

  13. Problem Solving Steps to Problem Solving… • Identify and Define the Problem • Determine the set of alternative solutions • Determine the criterion or criteria that will be used to evaluate the alternatives • Evaluate the alternatives

  14. Problem Solving Steps to Problem Solving… • Choose an alternative • Implement the selected alternative • Evaluate the results, and determine if a satisfactory solution had been obtained.

  15. Decision Making • Decision Making is the term generally associated with the first five steps of the problem-solving process. • Decision making ends with the choosing of an alternative, which is the act of making a decision.

  16. Problem Solving & Decision Making

  17. Problem Solving & Decision Making Example – Step 1 & 2 • Problem: Graduated & looking for a job -satisfying career • Alternatives: Job Offers.. • Company located in Rochester, New York • Company located in Dallas, Texas • Company located in Greensboro, North Carolina. • Company located in Pittsburgh, Pennsylvania

  18. Problem Solving & Decision Making • Step 3 - determine the Criterion or Criteria to evaluate alternatives • 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.

  19. Problem Solving & Decision Making Example – Criterion Criteria • Single Criterion - “Best” criterion is the starting salary of the each job . • Multi-Criteria - Location of Job & potential for advancement

  20. Problem Solving & Decision Making Example – Step 4 Evaluation

  21. Problem Solving & Decision Making Example – Step 5 – Choose • It is now time to make a choice among the alternatives – Decision. • Alternative 3 seems the best and is therefore referred to as the decision • This completes the decision making process.

  22. Problem Solving & Decision Making Define Problem Identify the Alternatives Determine the Criteria Decision Making Evaluate the Alternatives Problem Solving Choose an Alternative Implement the Decision Evaluate the Results

  23. Quantitative Analysis & Decision Making

  24. Quantitative Analysis & Decision Making • The decision-making process may take on two basic forms: • Quantitative • Qualitative.

  25. Quantitative Analysis & Decision Making • Qualitative Analysis- based primarily on the manager’s judgement and experience; • It includes the manager’s intuitive “feel” for the problem • It is more an Art than a Science. • It used when the problem is Relatively Simple.

  26. Quantitative Analysis & Decision Making • Quantitative Analysisis used if the problem is sufficiently complex. • Analyst will: • Concentrate on the quantitative facts or data associated with the problem • Develop mathematical expressions - • that describe the objectives, constraints, and other relationships that exist in the problem • Make a recommendation using one or more quantitative methods,.

  27. Quantitative Analysis & Decision Making • The manager must be: • Knowledgeable of both in qualitative and quantitative decision-making sources of recommendations • Able to ultimately combine the two sources and to make the best possible decision.

  28. Quantitative Analysis & Decision Making Reasons why the quantitative approach might be used in decision making: • The Problem is Complex • The manager cannot develop a good solution without the aid of quantitative analysis. • The Problem is Very Important • (e.g. a great deal of money is involved), and the manager desires a thorough analysis before attempting to make a decision.

  29. Quantitative Analysis & Decision Making Reasons why the quantitative approach might be used in decision making: • The Problem is New, • The manager has no previous experience to draw on. • The Problem is Repetitive, • the manager saves time and effort by relying on quantitative procedures to make routine decision recommendations.

  30. Quantitative Analysis & Decision Making • Qualitative Analysis

  31. Quantitative Analysis & Decision Making • Quantitative Analysis

  32. Quantitative Analysis & Model Development

  33. Quantitative Analysis & Model Development • Models are representations of real objects or situation. The various forms are: • Iconic Models – physical replicas of real objects. • E.g. scale model of an airplane or a child’s toy truck. • Analog Models – Models that are physical in form but do not have the same physical appearance as the object being modeled. • E.g. the speedometer & thermometer

  34. Quantitative Analysis & Model Development • Mathematical Models – a model that represents a problem by a system of symbols and mathematical relationships or expressions. • It is a critical part of quantitative approach to decision making. • E.g. A profit function for the sale of a product $10: - P = 10x

  35. Quantitative Analysis & Model Development Purpose of Models • It enables us to make inferences about the real situation by studying and analyzing the model. Example… • An iconic model of a new airplane can tested in a wind tunnel, • A mathematical model can be used to calculate profit with specified quantity of a product. • P = 10x for 3 units - P = 10(3) = $30

  36. Quantitative Analysis & Model Development Advantages of Models • Requires less time and is less expensive than experimenting with the real object or situation. • It reduces the risk associated with experimenting with the real situation, • E.g. A Large Investment.

  37. Quantitative Analysis & Model Development • However, the value of model-based conclusions is dependent on how well the model represents the real situation. • The Problem Definition phase leads to a: • Specific Objective, such as Maximization of Profit or Minimization of Cost, • Restrictions or Constraints, such as Production Capacities.

  38. Quantitative Analysis & Model Development • The success of a mathematical model and quantitative approach will depend heavily on accurately expressing : • The Objective and Constraints in terms of a mathematical equations or relationships. • A Mathematical Expression that describes a problem’s objective is referred to as the Objective Function,

  39. Quantitative Analysis & Model Development • Example - P = 10x. • A production capacity constraint could be that 5 hours are required to produce each unit and there are only 40 hours available per week. • Let x indicate the number of units produced each week so that: 5x≤40.

  40. Quantitative Analysis & Model Development • A complete mathematical model for this production problem is • MaximizeP = 10x • Subject to (s.t.) • 5x≤40 • x≥0 • This model is an example of linear programming Objective Function Constraints

  41. Quantitative Analysis & Model Development • Models can contain environmental factors that are Controllable or Uncontrollable: • Controllable Inputs – Inputs that are controlled or determined by the decision maker – e.g. Quantity x. • Controllable inputs are the decision alternatives specified by a manager and are also referred to as Decision variables.

  42. Quantitative Analysis & Model Development • Uncontrollable Inputs– Environmental factors which can affect both the objective function and the constraints. • E.g. Profit per unit $10, 5 hours and production capacity -40hrs per week. • Uncontrollable inputs can either be known exactly or be uncertain and subject to variation.

  43. Quantitative Analysis & Model Development • Deterministic Model If all uncontrollable inputs to a model are known and cannot vary, e.g. Corporate Income Tax. • The distinguishing feature of a deterministic model is that the uncontrollable inputs are known in advance.

  44. Quantitative Analysis & Model Development • Stochastic or Probabilistic Model, If any of the uncontrollable inputs are uncertain or subject to variation e.g. Demand. • The distinguishing feature of a stochastic model is that the value of the output cannot be determined even if the value of the controllable input is known, because the specific values of the uncontrollable inputs are unknown. In this respect, stochastic models are often more difficult to analyze.

  45. Quantitative Analysis & Model Development Flowchart showing the process of transforming (Production) Model Inputs into Output

  46. Quantitative • Analysis Data Preparation

  47. Data Preparation • Data in this sense refer to the values of the uncontrollable inputs to the model. • All uncontrollable inputs must be specified before analyzing model and recommend decision/solution. • E.g. $10 per unit, 5 hours per unit for production time, and 40 hours for production capacity. • Analysts combines model development and data preparation into one step if the model is relatively small and the uncontrollable inputs are few.

  48. Data Preparation • If the uncontrollable inputs or data is unavailable the analyst will usually develop a General Notation: c= profit per unit a = production time in hours per unit b = production capacity in hours • As such the following general model is developed: Max cx s.t. ax ≤ b x ≥ 0

  49. Quantitative • Analysis Model Solution

  50. Model Solution • The analyst will attempt to identify the values of the decision variables that provide the “best” output for the model. • This is referred to as the Optimal Solution for the model.

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