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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Article rating: 2.5
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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Using the CB.GetForeStatFN function in your Crystal Ball Models.

Aug 25 2010

Crystal Ball utlizes several powerful functions and features to extract information and descriptive statistics. We are going to review these techniques and present the CB.GetForeStatFN in full detail, including Six Sigma Capability Metrics.

Often when we are modeling a model, we may want to monitor or focus on a particular statistic. Traditionally we always look at the statistics of the forecast of interest.

Figure 1: Viewing forecast statistics in Crystal Ball

Fortunately Crystal Ball offers several other powerful methods to extract data from a forecast:

Technique

Pros

Cons

Copy Stats table from forecast windows (Manual Approach)

  • Easy
  • Function is directly available from Edit Menu
  • Data is static
  • Process is manual

Auto – Extract Values from forecasts at the end of a simulation

  • Will produce a wide array of data and descriptive statistics.
  • Is easily configured in the forecast in dialog box in the Forecast Preferences
  • Is visible to all non-Crystal Ball users
  • Can overwrite portions of your model resulting in broken formulas
  • Extractions are generated only when the simulation stops

CB.Get Functions (CB.GetForeFN, CB.GetForeStat)

  • Dynamic data
  • Can be used in Forecasts i.e. to see how a mean shifts
  • Will produce a wide array of stats
  • These functions tend to force CB to run in Normal speed.
  • These functions are not available to non-cb users
  • Will display 0 until 1 trial has been run

Crystal Ball Reports

  • Easy to generate
  • Very complete and visual
  • Easy to customize

 

  • Are static
  • Are in a separate file
  • Not designed to re-incoporate into models.

 

In this article, we will present the CB.GetForeStatFN function using the European Put Model (see Figure 2 ) developed by John Charnes for his book Financial Modeling with Crystal Ball and Excel (By the way, the model is available in the download section of this site.).

Figure 2: Black Sholes model using Crystal Ball

When we run this model for 10,000 trials, we obtain 2 forecasts: One for the present value of the option(see Figure 3) and the other is the Mean Present Value (Figure 4).

Figure 3: Present value of the option

The above forecast chart and statistics was generated using the first forecast cell in Figure 1. The chart below presents the distribution of the potential means associated with this simulation. It is worth noting that the mean shifted as the trials increased. Nevertheless, the usefulness of the knowing that the mean could also follow a distribution is very powerful but should be used with caution - generally because it will change each time a trial is added.

Figure 4: Distribution of the option's mean using the CB.GetForeStat function

HOW TO USE THE CB.GetForeStatFN

In Excel, you would use the following formula:

=CB.GetForeStatFN(ForecastCellLocation, Index)

=CB.GetForeStatFN(B13, 2) generates 9.83, the forecast's mean using the below forecast.

In order to extract the other parameters available in each forecast, use the following index parameters.

CB.GetForeStat (CellRef, Parameter)

1

Returns the number of trials

2

Returns the mean

3

Returns the median

4

Returns the mode

5

Returns the standard deviation

6

Returns the variance

7

Returns the skewness

8

Returns the kurtosis

9

Returns the coefficient of variation

10

Returns the range minimum

11

Returns the range maximum

12

Returns the range width

13

Returns the standard error

14

Returns the base case (initial value) of the forecast cell

50

Returns the Cp for short-term data (or Pp for long-term data)

51

Returns the Cpk-lower for short-term data (or Ppk-lower for long-term data)

52

Returns the Cpk-upper for short-term data (or Ppk-upper for long-term data)

53

Returns the Cpk for short-term data (or Ppk for long-term data)

54

Returns the Cpm for short-term data (or Ppm for long-term data)

55

Returns the Z-LSL

56

Returns the Z-USL

57

Returns the Z-st

58

Returns the Z-lt

59

Returns the p(N/C)-below LSL

60

Returns the p(N/C)-above USL

61

Returns the p(N/C)-total

62

Returns the PPM-below LSL

63

Returns the PPM-above USL

64

Returns the PPM-total

 

There are many other functions that use these indecies in Crystal Ball... to get a full list, please see the complete list here

 

If you have any questions on how to incorporate this function in your models and VBA macro's, please don't hesitate to drop me a line at [email protected]

 

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