水晶球软件使用Crystal Ball ppt课件
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Project Management Session 9 Crystal Ball
OPIM 5270
ppt课件
OPIM 5270 – Spring 2015
Session 9 Goals
Understand why risk must be analyzed Know pros / cons for three ways to analyze risk Identify random variables in models Know the four steps of a simulation process Generate random numbers with Crystal Ball Use the four steps of a simulation process Explain how Crystal Ball supports Proj. Mgmt.
• Simulation can be used to analyze these types of models
ppt课件
OPIM 5270 – Spring 2015
Random Variables & Risk
• A random variable is any variable whose value cannot be predicted or set with certainty.
• Suppose an $1,000 investment is expected to return $2,000 in two years. Would you invest if...
– the outcomes could range from $1,060 to $4,000? – the outcomes could range from $0 to $2,100?
• Decisions made using uncertain information often involve risk. What risks?
ppt课件
OPIM 5270 – Spring 2015
Why Analyze Risk?
• Using expected values for uncertain cells tells us nothing about the variability of the performance measure.
• This is in contrast to analytical methods, which obtain exact solutions to highly stylized problems
• Tradeoff between rigor and relevance
ppt课件
OPIM 5270 – Spring 2015
ppt课件
OPIM 5270 – Spring 2015
Best-Case/Worst-Case Analysis
• Best case - plug in the most optimistic values for each of the uncertain cells.
• Alternatives with the same expected value may involve very different levels of risk.
ppt课件
OPIM 5270 – Spring 2015
Methods of Risk Analysis
• Best-Case/Worst-Case Analysis • What-if Analysis • Simulation
• How to deal with randomness?
➢ Ignore it ➢ Simplify problem to make it analytically tractable, get
solution, then ignore real-life complications ➢ Find a way to obtain an approximate solution to real-
ppt课件
OPIM 5270 – Spring 2015
Dealing with Randomness
• Most real-world business situations today are probabilistic, but the decision models used to deal with them are deterministic.
Introduction to Simulation
• What is this?
Y = f(X1, X2, …, Xk)
• Often, the values for one or more "input" cells are unknown or uncertain
• This creates uncertainty about the value of the "output" cell
world problems
ppt课件
OPIM 5270 – Spring 2015
Monte Carlo Simulation
• Monte Carlo simΒιβλιοθήκη Baidulation is a method by which approximate solutions are obtained to realistic (and therefore complicated) problems
• Many “input cells” in spreadsheet models are actually random variables. For example: ➢ the future cost of raw materials ➢ future interest rates ➢ future number of employees in a firm ➢ expected product demand
OPIM 5270
ppt课件
OPIM 5270 – Spring 2015
Session 9 Goals
Understand why risk must be analyzed Know pros / cons for three ways to analyze risk Identify random variables in models Know the four steps of a simulation process Generate random numbers with Crystal Ball Use the four steps of a simulation process Explain how Crystal Ball supports Proj. Mgmt.
• Simulation can be used to analyze these types of models
ppt课件
OPIM 5270 – Spring 2015
Random Variables & Risk
• A random variable is any variable whose value cannot be predicted or set with certainty.
• Suppose an $1,000 investment is expected to return $2,000 in two years. Would you invest if...
– the outcomes could range from $1,060 to $4,000? – the outcomes could range from $0 to $2,100?
• Decisions made using uncertain information often involve risk. What risks?
ppt课件
OPIM 5270 – Spring 2015
Why Analyze Risk?
• Using expected values for uncertain cells tells us nothing about the variability of the performance measure.
• This is in contrast to analytical methods, which obtain exact solutions to highly stylized problems
• Tradeoff between rigor and relevance
ppt课件
OPIM 5270 – Spring 2015
ppt课件
OPIM 5270 – Spring 2015
Best-Case/Worst-Case Analysis
• Best case - plug in the most optimistic values for each of the uncertain cells.
• Alternatives with the same expected value may involve very different levels of risk.
ppt课件
OPIM 5270 – Spring 2015
Methods of Risk Analysis
• Best-Case/Worst-Case Analysis • What-if Analysis • Simulation
• How to deal with randomness?
➢ Ignore it ➢ Simplify problem to make it analytically tractable, get
solution, then ignore real-life complications ➢ Find a way to obtain an approximate solution to real-
ppt课件
OPIM 5270 – Spring 2015
Dealing with Randomness
• Most real-world business situations today are probabilistic, but the decision models used to deal with them are deterministic.
Introduction to Simulation
• What is this?
Y = f(X1, X2, …, Xk)
• Often, the values for one or more "input" cells are unknown or uncertain
• This creates uncertainty about the value of the "output" cell
world problems
ppt课件
OPIM 5270 – Spring 2015
Monte Carlo Simulation
• Monte Carlo simΒιβλιοθήκη Baidulation is a method by which approximate solutions are obtained to realistic (and therefore complicated) problems
• Many “input cells” in spreadsheet models are actually random variables. For example: ➢ the future cost of raw materials ➢ future interest rates ➢ future number of employees in a firm ➢ expected product demand