Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.  

This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover the role of analytics in the organization and how it has evolved over time to give participants strategies to mobilize analytics talent within the firm.  

We will discuss these topics as well as present practical models and applications using @RISK.

A detailed comparison of the top Monte-Carlo Simulation Tools for Microsoft Excel

There are very few performance comparisons available when considering the acquisition of an Excel-based Monte Carlo solution. It is with this in mind and a bit of intellectual curiosity that we decided to evaluate Oracle Crystal Ball, Palisade @Risk, Vose ModelRisk and Frontline Risk Solver in terms of speed, accuracy and precision. We ran over 20 individual tests and 64 million trials to prepare comprehensive comparison of the top Monte-Carlo Tools.

 

Business analytics stratifies into three levels of inquiry and findings beginning with descriptive, followed by predictive, and finally prescriptive methods as follows:

Dr David Berlinski (2000) makes the historical observation that two great ideas have most influenced the technological progress of the Western world:

The first is the calculus, the second the algorithm. The calculus and the rich body of mathematical analysis to which it gave rise made modern science possible; but it has been the algorithm that has made possible the modern world. (Berlinski, p. xv)

Dr Berlinski concludes that:

The great era of mathematical physics is now over. The three-hundred-year effort to represent the material world in mathematical terms has exhausted itself. The understanding that it was to provide is infinitely closer than it was when Isaac Newton wrote in the late seventeenth century, but it is still infinitely far away…. The algorithm has come to occupy a central place in our imagination. It is the second great scientific idea of the West. There is no third. (Berlinski, pp. xv-xvi)

Source: Berlinski, D (2000). The Advent of the Algorithm: The 300-Year Journey from an Idea to the Computer. San Diego, CA: Harcourt.

Related Posts: Enter the Algorithm

According to Prof Ronald A Howard (1992):

Three of the warranties that I would like to have in any decision situation are that:

1. The decision approach I am using has all the terms and concepts used so clearly defined that I know both what I am talking about and what I am saying about it;
2. I can readily interpret the results of the approach to see clearly the implications of choosing any alternative, including of course, the best one; and
3. The procedure used to arrive at the recommendations does not violate the rules of logic (common sense).

Plain and simple... Source: Howard, R A (1992), Heathens, Heretics, and Cults, Interfaces, 22(6), 15-27.

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.

Prof Frank H Knight (1921) proposed that "risk" is randomness with knowable probabilities, and "uncertainty" is randomness with unknowable probabilities. However, risk and uncertainty both share features with randomness. The illustration below explains the relationship of the concepts better than words...

Source: Knight, F H (2002/1921), Risk, Uncertainty and Profit, Washington, DC: BeardBooks.

 

 Quick overview of the parts of an optimization model in MS Excel.

When using tools such as Excel, Crystal Ball or ModelRisk, it is very important to be able to translate a mental model to a mathematical one. Let me illustrate, when you think about your business, you often will think of abstract notions such as profit or margins. These are mental constructs because their are no physical representations of profit or margins (except a pile of cash) only mathematical ones.