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Risk by Palisade

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_at_Risk by Palisade. Janggam Adhityawarma. Matt Rhinehart. Brandon Richardson. Craig Soper ... Any method - qualitative and/or quantitative for assessing the ... – PowerPoint PPT presentation

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Title: Risk by Palisade


1
_at_Risk by Palisade
  • Janggam Adhityawarma
  • Matt Rhinehart
  • Brandon Richardson
  • Craig Soper
  • Don Yap

2
Introduction
  • Risk Analysis
  • Any method - qualitative and/or quantitative for
    assessing the impacts of risk on decisions.
  • Many Risk Analysis methods blend both qualitative
    and quantitative techniques.
  • The goal of these methods is to help the
    decision-maker choose a course of action.

3
Features
  • Uses Monte Carlo simulation to take all possible
    outcomes into account.
  • The result is a distribution of possible outcomes
    and the probabilities of getting those results.
  • One of the strengths of Monte Carlo simulation is
    that it produces enough data to create accurate
    graphs. Histograms, cumulative curves, area and
    line graphs, etc.

4
Features
  • Create a summary report of results using the
    Quick Report command.
  • Report in Excel containing a histogram,
    cumulative curve, Tornado graph, and summary
    statistics.
  • Sensitivity Analysis
  • Determines which input distributions have the
    biggest impact on the outputs.
  • Scenario Analysis
  • Identifies combinations of inputs - or scenarios
    - which lead to output target values.

5
_at_Risk Features
6
Risk Analysis with _at_RISK
  • Risk Analysis in _at_RISK is a quantitative method
    that seeks to determine the outcomes of a
    decision as a probability distribution.
  • In general, Risk Analysis with _at_RISK encompasses
    four steps
  • 1. Develop a Model - Define the problem or
    situation in an Excel worksheet format.
  • 2. Identifying Uncertainty - Determine which
    model inputs are uncertain, and represent those
    using ranges of values with _at_RISK probability
    distribution functions. Identify which result or
    output of the model to analyze.
  • 3. Analyzing the Model with Simulation - Run
    the simulation to determine the range and
    probabilities of all possible outcomes for the
    outputs identified.
  • 4. Make a Decision - With complete information
    from analysis and preferences, make a decision.

7
Developing a Model
  • Model construction is based on spreadsheet data
  • Examples
  • Launching a new product - impact of potential
    profits
  • Potential pollution effects of a new factory on a
    river
  • Effectiveness of a new drug therapy on an illness
  • Exploratory site for oil wells

8
Identifying Uncertainty
  • Determine which inputs in your model are
    uncertain
  • Probability distribution functions to represent a
    range of possible values
  • _at_RISK takes uncertainty into account

9
Risk Distribution
10
RISKview
11
Best Fit
12
Analyzing the Model with Simulation
  • Capabilities
  • Specify the number of iterations
  • Update the spreadsheet in real-time numerically
    or graphically
  • Control the convergence
  • Use the default settings - _at_RISK will automate
  • Provides a range of possible outcomes and
    probabilities of occurrence

13
Simulation-Optimization
14
Sensitivity Analysis
15
Make a Decision
  • Allows multiple simulations to be run
    back-to-back.
  • Correlate Inputs for More Accurate Models.
  • Statistics and Graphing
  • Custom Applications

16
Correlation
17
Graphing and Reporting
18
Model - Results
19
Risk Applications
20
Enhanced Editions
  • _at_Risk Professional
  • Integrated BestFit
  • Goal Seek
  • Stress Analysis
  • Advanced Sensitivity Analysis
  • _at_RISK Industrial
  • RISKOptimizer to Optimize Your Simulation Results
  • Accelerator

21
_at_Risk Pricing
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