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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The Virtual Organization and Information Technology (Part 5/5)

 

Collaboration and TechnologyOrganizations seeking to develop a virtual business model must also be in a position to effectively implement it on a business level and on a technological level. (Venkatraman, 1994; Venkatraman & Henderson, 1993,1998).
 
One of today’s hottest IT topics is how to cheaply and effectively inter-connect processes. Collaboration emerged out of the relative cheapness and ubiquity of Internet technologies. Champy (2002) states ”E-business is a natural reaction to today’s competitive environment[i]. But e-business means a lot of things to a lot of people. In current literature, e-business has taken on several definitions over time i.e.:
 
·         Strategic approach
·         A set of enabling technologies (Porter, 2001),
 
Since technology is a critical success factor to any virtual organizing strategy, the analysis of e-business is interesting due to its business focus and its ability to flexibly and rapidly support changing business needs and requirements. In essence, e-business is a composite of the above-mentioned perspectives and whose definition can be used inter-changeably with virtual organizing because of its open technologies and collaborative strategies.

 

Collaborative modeling using predictive analytics

When building models we are often confronted with assumptions that evolve over time. In most cases it is important to capture these changes to keep our model relevant. Over the last decade, Business Intelligence solutions have created a culture of self-service IS information.  Given this democratization and decentralized access to data has created many opportunities and pitfalls for business analysts and decision-makers. We are going to outline some opportunities and pitfalls relating to shared modeling and a few strategies to get started.

This post presents the opportunities and challenges stemming from moving towards a distributed modeling paradigm in the organization. Also presented is a high-level integrated predictive/collaborative planning process.

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