Is Oracle Crystal Ball still relevant?

Is Oracle Crystal Ball still relevant?

Are Excel Simulation Add-Ins like Oracle Crystal Ball the right tools for decision making? This short blog deliberates on the pros and cons of Oracle Crystal Ball.
Author: Eric Torkia
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Decision Science Developper Stack

Decision Science Developper Stack

What tools should modern analysts master 3 tier design after Excel?

When it comes to having a full fledged developper stack to take your analysis to the next level, its not about tools only, but which tools are the most impactful when automating and sharing analysis for decision making or analyzing risk on projects and business operations. 

Author: Eric Torkia
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The Need For Speed 2019

The Need For Speed 2019

Comparing Simulation Performance for Crystal Ball, R, Julia and @RISK

The Need for Speed 2019 study compares Excel Add-in based modeling using @RISK and Crystal Ball to programming environments such as R and Julia. All 3 aspects of speed are covered [time-to-solution, time-to-answer and processing speed] in addition to accuracy and precision.
Author: Eric Torkia
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Article rating: 3.8
Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data

Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data

How I Learned to Think of Business as a Scientific Experiment

Imagine playing a game in which someone asks you to infer the number of sides of a polyhedron die based on the face numbers that show up in repeated throws of the die. The only information you are given beforehand is that the actual die will be selected from a set of seven die having these number of faces: (4, 6, 8, 10, 12, 15, 18). Assuming you can trust the person who reports the outcome on each throw, after how many rolls of the die wil you be willing to specify which die was chosen?
Author: Robert Brown
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Bayesian Reasoning using R

Bayesian Reasoning using R

Gender Inference from a Specimen Measurement

Imagine that we have a population of something composed of two subset populations that, while distinct from each other, share a common characteristic that can be measured along some kind of scale. Furthermore, let’s assume that each subset population expresses this characteristic with a frequency distribution unique to each. In other words, along the scale of measurement for the characteristic, each subset displays varying levels of the characteristic among its members. Now, we choose a specimen from the larger population in an unbiased manner and measure this characteristic for this specific individual. Are we justified in inferring the subset membership of the specimen based on this measurement alone? Baye’s rule (or theorem), something you may have heard about in this age of exploding data analytics, tells us that we can be so justified as long as we assign a probability (or degree of belief) to our inference. The following discussion provides an interesting way of understanding the process for doing this. More importantly, I present how Baye’s theorem helps us overcome a common thinking failure associated with making inferences from an incomplete treatment of all the information we should use. I’ll use a bit of a fanciful example to convey this understanding along with showing the associated calculations in the R programming language.
Author: Robert Brown
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Crystal Ball vs ModelRisk in Discrete Distribution Fitting and Correlation/Copulas (8/8)

Feb 22 2011

Is there a winner in this battle between Crystal Ball and ModelRisk? To quote that way-too-often-quoted reply: It depends. Some users will value certain technical capabilities over others. Some users will value user-friendliness over accuracy. If there is to be a group deployment of a MCA spreadsheet package, usability may trump technical capabilities overall. Does it matter if one package has more distributions to choose from if there are only three that are of interest for your particular class of stochastic problems? Would it matter what kind of correlation enforcement method is used if, as in many manufactured assemblies, there is practically no correlation between separate components? Probably not. But if they do (as in financial and insurance applications), there will be a clear winner.

Based on the discrete distribution-fitting and correlation/copula-fitting capabilities of CB and MR in previous posts, I have contrasted the capabilities in the following table with pros and cons. Even though I am an engineer by nature, I have attempted to step back and objectively consider the much larger class of risk analysts. That would include the fields of scientific research, finance, insurance, and transactional processes, to name a few.

 

 

 

 

 

 

 

 

PROS

 

 

 

 

 

 

 

 

 

 

 

 

CONS

 

 

 

 

 

 

 

 

 

 

 

 

Discrete Distribution Fitting

 

 

 

 

 

 

   

 

 

 

 

 

 

CRYSTAL BALL

 

 

 

 

 

 

  • Ability to rapidly select classes of PDFs for fitting operation.
  • Fitting operation requires at least 9 clicks.
  • Provides hypothesis test p-values (null = fitted PDF).
  • Fits at most 6 PDFs.
  • Only 1 fitting criterion available.
  • Cannot fit to data with less than 15 samples.
  • Allows for PDF parameter uncertainty via cell-referencing other PDFs.
  • No penalty for over-fitting.

 

 

 

 

 

 

MODELRISK

 

 

 

 

 

 

  • Fits up to 11 PDFs. (Does not include Zero-Inflated or Zero-Truncated.)
  • 3 fitting criteria available.
  • Can fit to minimum number of samples required by PDF MLE calculations (less than 15).
  • Allows for uncertainty in fitted PDF parameters.
  • Over-fitting penalties.
  • Some PDFs fitted are adaptations or combinations of other PDFs. (Not really a negative but should be considered when comparing # of PDFs available for fitting with other MCA codes.)
  • Fitting operation requires up over 18 clicks.
  • No hypothesis test p-values provided.

 

 

 

 

 

 

Correlation / Copula Fitting

 

 

 

 

 

 

   

 

 

 

 

 

 

CRYSTAL BALL

 

 

 

 

 

 

  • Implements Spearman's coefficient to allow accurate simulation between PDFs of different nature.
  • Adding fitted correlation to existing PDFs requires up to 15 clicks.
  • Ability to easily turn off defined correlations prior to simulation.
  • No immediate flexibility to adjust for defined patterns observed in scatter plots.
  • No fitting criteria employed.

 

 

 

 

 

 

MODELRISK

 

 

 

 

 

 

  • Implements Copulas to allow more flexibility in modeling interacting variables.
  • 5 different bi-variate copulas to select from.
  • 3 fitting criteria available.
  • Correlation behavior can be manipulated directly in spreadsheet.
  • Ability to link data to Empirical copulas, allowing immediate adjustment.
  • Correlation behavior must be connected to simulated variables via Vose formula arguments.
  • Adding fitted correlation behavior requires over 20 clicks plus necessary manual modification of simulated variables.
  • Correlation behavior can be turned off via spreadsheet manipulation techniques.

 

 

 

 

 

 

Usability of Discrete PDFs and Correlations

 

 

 

 

 

 

   

 

 

 

 

 

 

CRYSTAL BALL

 

 

 

 

 

 

  • Organizes PDFs & correlations in hidden worksheet, preventing unintentional modification (error-proofing).
  • Automatic color-coding during CB Assumption creation.
  • All CB Assumptions and Correlations must be manually re-created if fitted data is modified or added to.
  • Necessary to create new CB Assumption to simulate same PDF in two-or-more transfer function input cells.

 

 

 

 

 

 

MODELRISK

 

 

 

 

 

 

  • Vose function call-outs to data allow instant updating when fitted data is modified or added to.
  • Not necessary to create new Distribution Object in order to simulate same PDF in two-or-more transfer function input cells.
  • User must organize both Distribution and Copula MR Objects for easy recognition and operation.
  • Less protection from unintentional modification of PDFs and correlation behavior.

A few caveats should be mentioned: The data set utilized to demonstrate the CB and MR capabilities has a sample size of 34. Unfortunately, if the sample size goes below 15, there is no facility in CB for distribution fitting. Why? Statistically speaking, a small sample size provides less confidence about the distribution fit. The creators of CB saw it fit to prevent this from happening by placing a lower limit on the sample size. But savvy statisticians know this relation very well. MR assumes the user is one of these types. And to counteract the greater range of confidence around a fitted PDF parameter, MR can incorporate an Uncertainty element that we have yet to describe.

We have also not attempted to fit continuous distributions, which are probably of more interest to the reader than discrete ones. In addition, we have ignored particular classes of MR distributions that are really neat (zero-inflated, zero-truncated, aggregate, combined, sliced and diced, etc…). There are also interesting empirical-fitting options for both distributions and copulas in MR. I hope to return to these in future posts.

Certainly, discrete distribution-fitting and correlation enforcement are not the only two capabilities that one should base a software purchasing decision on. I would urge the reader to place an importance ranking on the summarized feature list being judged and make an informed decision with your priorities in place. For my money, MR is the clear winner in this battle. Your winner may be different.

Karl Luce is a Solutions Specialist who has a deep passion for using Monte Carlo analysis as a Six Sigma tool and quantifying uncertainty in the universe. His past roles included training Crystal Ball and quality concepts as a Design for Six Sigma (DFSS) Master Black Belt and a Six Sigma Transactional Black Belt.  He received his BS in Aeronautical/Astronautical Engineering from M.I.T. in 1985. Prior to joining Oracle in 2005, Karl held analytical and managerial positions within Fortune 500 corporations in the defense aerospace and automotive industries.  

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