On-Demand Webinar: “Introduction to Risk and Decision Analysis using the DecisionTools Suite”

Project and Pipeline Valuation

»Watch now: “Introduction to Risk and Decision Analysis using the DecisionTools Suite”

This webinar is designed to provide an entry-level introduction into probabilistic analysis and will show how Monte Carlo simulation and other techniques can be applied to your everyday business analyses. If you build models in Excel then Palisade solutions can almost certainly help you to make more informed decisions, right from your desktop.

The webinar will explore some of the ways in which organisations are applying Palisade tools. From oil and gas, insurance and finance through to healthcare, defence and construction, @RISK and the other tools in the DecisionTools Suite enhance the decision making capabilities of some of the world’s most successful companies.

For more than 30 years, Palisade software and solutions have been used to make better decisions. Cost estimation, NPV analysis, operational risk registers, portfolio analysis, insurance loss modeling, reserves estimation, schedule risk analysis, budgeting, sales forecasting, and demand forecasting are just some of the ways in which the tools are applied. The webinar will demonstrate how easy – and necessary – it is to implement quantitative risk analysis in any business.

» Watch “Introduction to Risk and Decision Analysis using the DecisionTools Suite” now

» Download The DecisionTools Suite – try it for yourself!

VIDEO: Introduction to the DecisionTools Suite

Do you use @RISK but haven’t explored the other Palisade Tools? It’s time you branched out to explore the DecisionTools Suite, which, in addition to @RISK for Monte Carlo simulation,  includes PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis. In addition, the DecisionTools Suite comes with StatTools for statistical analysis and forecasting, NeuralTools for predictive neural networks, and Evolver and RISKOptimizer for optimization. All programs work together better than ever before, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.

To learn more, check out this introduction video to the Suite,  given by Palisade expert trainer Rishi Prabhakar:

The video will give viewers a good understanding of of each of these separate products and how they work together.




NeuralTools Assists Airline to Determine the Most Profitable Price Points

How much is too much when it comes to plane ticket prices?  This is a key question for commercial airlines, and one CommercialAirlinethat Palisade helped answer.

Fernando Hernandez, a Senior Risk Consultant and Trainer at Palisade, was tasked with developing a model for predicting demand and price elasticity for a domestic airline catering to the tourism industry in Costa Rica. This predicting model uses neural networks technology powered by Palisade’s NeuralTools application, a sophisticated data mining application that makes new predictions based on the patterns of known data. By imitating brain functions to “learn” the structure of data, NeuralTools can take new inputs and make intelligent predictions.

The objective of this trained network was to adequately predict how many passengers would purchase tickets on a particular flight and the optimum price point, based on a wide range of condition combinations. Once the data set was created, the neural network was set to be trained and tested. Upon training and testing, a sensitivity analysis on 21 variables was performed to predict the passenger demand for a particular flight. A sensitivity analysis showed which factors held the greatest weight in determining the number of passengers per flight. The most impactful factors included mean fare, distance and competitor strength.

A data entry table was created utilizing fare increments of $5.17, starting at $50, and all the way up to $200. The model predicted that occupancy would remain more or less steady—between 17 to 23 passengers— regardless of the fare, until the $175 threshold was reached. Once this mean fare was surpassed, passenger demand abruptly decreased driving down total expected revenue for the route. At a mean fare of above $185—a mere increase of $10 per ticket—occupancy dipped to less than five passengers.

While demand and price elasticity is not an exact science, NeuralTools helped the airline utilize data it already had to determine the most profitable price point and adjust it based on the numerous factors that impact air travel.

To see the full case study, click here.

NeuralTools Nets More Dollars for Business Financing Firm

Fensterstock_BusinessBackerLending companies are only able to make a profit if their clients pay back their loans. But how do you know which client to lend to? And how much to lend? These are the type of questions that can make or break a financing firm, and that’s why Business Backer, a lending firm for small businesses, decided to tackle these questions head-on. While the company has had considerable success—they have secured $130 million in funding to more than 4,000 small businesses across the United States–they wanted to take guesswork out of the equation.

To answer their questions, they turned to Albert Fensterstock, Managing Director at Albert Fensterstock Associates, who specializes in improving risk analysis capability and collection department efficiency. Fensterstock, in turn, relied on Palisade’s NeuralTools software to help Business Backer begin refining their decision-making process. “I’ve used Palisade’s products for years,” says Fensterstock. “And I used it this time to answer a two-fold problem: How to make a loan with less risk, and how to come up with an appropriate credit limit.”

Specifically, Fensterstock used NeuralTools and StatTools to discover the most predictive variables when evaluating a potential client, as well as to determine how likely a debtor was to pay back a loan, and how much they were best-suited to borrow.

Overall, the models Fensterstock developed for Business Backer set an impressive precedent. “I’ve been doing this kind of work for 22 years, and I’ve never before built models that were able to do this in one task,” he says. “This model is better than anything I’ve ever developed.”

Read the full case study here.