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Introduction to Managerial Decision Modeling

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  • What is decision modeling?
    Decision modeling is a scientific approach to managerial decision making, involving the development of a mathematical model of a real-world problem to avoid bias and provide insights.
  • Other names for decision modeling
    Decision modeling is also called quantitative analysis, management science, or operations research.
  • Purpose of decision modeling
    To produce solutions that are timely, accurate, flexible, economical, reliable, easy to understand, and easy to use for managerial problems.
  • Two main types of decision models
    Deterministic models assume all input data are known with certainty; probabilistic models incorporate uncertainty using probabilities.
  • What defines a deterministic model?
    A model where all relevant input data values are known and fixed with certainty.
  • Example of a deterministic model
    Dell's production planning where resource requirements and profit contributions per unit are known and fixed.
  • Most common deterministic modeling technique
    Linear programming (LP) is the most widely used deterministic modeling technique.
  • What defines a probabilistic model?
    A model where some input data values are unknown or uncertain and are represented using probabilities.
  • Example of a probabilistic model
    Deciding to start a new business venture with uncertain future success and returns.
  • Why are probabilistic models valuable despite uncertainty?
    They provide a structured approach to incorporate uncertainty and evaluate decisions under different expectations.
  • Probabilistic modeling techniques covered
    Includes decision analysis, queuing, simulation, and forecasting.
  • Decision modeling process starts with what?
    It starts with data, which are processed into meaningful information for decision making.
  • Difference between qualitative and quantitative data in decision modeling
    Quantitative data are numerical and measurable; qualitative data are descriptive and harder to quantify but important for decisions.
  • Role of qualitative factors in decision modeling
    Qualitative factors like legislation or technology can influence decisions and must be considered alongside quantitative data.
  • When can decision models automate decisions?
    When qualitative factors are minimal and the problem, model, and data are stable over time.
  • Use of spreadsheets in decision modeling
    Spreadsheet software like Microsoft Excel is widely used to set up and solve decision models efficiently.
  • Excel add-ins useful for decision modeling
    Add-ins such as Data Analysis and Solver help implement various decision modeling techniques.
  • Historical origin of decision modeling
    Rooted in scientific management principles pioneered by Frederick W. Taylor in the early 1900s and expanded during WWII.
  • Why is understanding model limitations important?
    Because correct use requires knowing the assumptions, applicability, and limitations of each decision model.
  • Examples of organizations using decision modeling
    Companies like American Airlines, IBM, Google, UPS, and FedEx use decision modeling to solve complex problems.