Crystal Ball Articles

Modeling and simulation has shown tremendous value during product development by validating that the product design fulfills customer requirements. Key design risks can be identified and mitigated early on in the product lifecycle, when changes are the least expensive. Learn practical applications of modeling and simulation for DFSS projects.

Applying Crystal Ball Professional Edition to new product development processes

<P>New York Air Brake of Watertown, NY manufactures products for the railroad industry that include pneumatic and electropneumatic brake system components for freight cars an...

The challenge of implementing new technologies in portable products requires a skillful application of engineering tools and sound design judgment. At Motorola Labs, one of the sim...

In this paper, we will see how a designed experiment approach combined with simulation can be used to efficiently provide insights into the engineering design of a classic device, ...

<P>Jonathon Andell is President of Andell Associates and a certified Six Sigma Black Belt. He writes here on Crystal Ball Software and how it is used as a significant weapon ...

The most common weaknesses we have seen in DFSS implementations are in the area of mathematical prediction. In this paper I will show how modeling and simulation can be used to ove...

Applying Crystal Ball Professional Edition to process phases, to deliver responsiveness to the customer and financial results to the business: In this DMAIC case study, we examine ...

Tolerance Design is the science of predicting the variation in system performance caused by variations in component values or the environment. This article shows how Monte Carlo si...

The explosive growth of Six Sigma methods in diverse industries has created a huge need for software to automate statistical analysis. Selection of the best tools for your company ...

Jim Franklin discusses how simulations are helping organisations in Europe improve their project selection success and reduce their testing overheads by taking the uncertainty out of the process.