Are there discrete univariate probability distribution functions (PDFs) that can be used to simulate college basketball scores? Do we, as avid basketball observers, know enough to suggest one discrete PDF is better than another? In fitting distributions to data in your business problems, the analyst will be asking the same types of questions. If the analyst is not an expert on the inputs and their behavior, he or she should seek out a subject-matter expert (SME) who can provide insight. Putting experience and theoretical knowledge together this way is a best practice for distribution selection.

Let us assume we have a batch of historical data in a spreadsheet. Our mission-of-the-moment is to use this data and fit probability distributions that describe its past variability (or uncertainty). Consider using either Crystal Ball or ModelRisk to do this task. We offer free trials of both to registered users. If you register here, you can get yours too. Try fitting the same data using these two different packages. Let us know how and why one is better than the other. In demonstrating these capabilities, we gain first-hand experience on the usability and capabilities of the alternatives and which features compared have more priority. The best way to judge is to try them out for yourself.

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.

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.

This article provides a highlevel overview of how and why to integrate VBA in Crystal Ball spreadsheet models. Given that Oracle Crystal Ball runs on Excel, it is worth exploring how VBA can create behaviors that are NOT natural to Excel!