Tolerance Analysis is the set of activities, the up-front design planning and coordination between many parties (suppliers & customers), that ensure manufactured physical parts fit together the way they are meant to. Knowing that dimensional variation is the enemy, design engineers need to perform Tolerance Analysis before any drill bit is brought to raw metal, before any pellets are dropped in the hopper to mold the first part. Or, as the old carpenter's adage goes: Measure twice, cut once. 'Cause once all the parts are made, it would be unpleasant to find they don't go together. Not a good thing.

The recent and ongoing disaster in the Gulf of Mexico has raised some questions as to the preparedness of Big Oil to respond to an emergency spill.  An examination of their emergency spill plans has garnered criticism but is it justified?  How much effort should companies spend on risk identification and mitigation?

This post presents 2 popular learning curve methods for estimating how a person or organization benefits from repeat learning.

This technique is key for the project risk analyst.

Overview of  techniques to accelerate Excel Models and custom applications.... I have included a few fun user defined functions as examples.

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.

Oracle Crystal Ball has finally released its latest version of its industry leading Monte-Carlo tool. Essentially 3 features were added to the Fusion Edition.

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.

 A brief review of Oracle and Microsoft database/application integration with Excel and Crystal Ball for comprehensive modeling