FAQ

The following questions have frequently been asked about probabilistic technology:

1. What is probabilistic technology?
2. What is the difference between statistical approaches and probabilistic technology?
3. How do Monte Carlo simulations relate to probabilistic technology?
4. Why do we need probabilistic technology?
5. Is probabilistic technology mature enough for practical applications?
6. At what stage of a project should probabilistic technology be implemented?
7. Which industries have potential applications in probabilistic technology?
8. How do I become a registered user in the Probabilistic Technology Leadership Council Network?
9. How do I become a member of ASQ?

1. What is probabilistic technology?
Probabilistic Technology is one of several predictive technologies aimed at allowing one to predict outcomes. The outcomes could range from how fast a plane can fly, how much a company will earn next year, how reliable a part or system is, what is the most likely failure mode for a system, etc. Sometimes these predictions can be made with statistics, where you operate the systems and gather data on the results. This approach inherently takes into account uncertainties in the variables that drive the outcome, but unless one is careful, there may be unknown biases or other inadequacies in the data.  In any case, the statistical method can be very expensive.

Another way of predicting outcomes is by creating a mathematical or rule-based model, both of which are deterministic models. For these, you pick a value for each independent variable and calculate a value for the dependant variable, or outcome. The problem with deterministic models is that they ignore uncertainty, in that the independent variables are usually not single-valued, but are random. We might use safety factors to try to compensate for the uncertainties, but that may either lull us into thinking the safety factors are sufficient, or cause us to over design the system.

The preferred way of predicting outcomes is to use Probabilistic Technology. In Probabilistic Technology, if there is data, we use statistics to characterize the uncertainties in the independent variables. If there isn't any data, we can use engineering judgment to estimate those uncertainties. These random variables then become the input to a deterministic model. Only now the outcome will not be a single-value. Picking from a toolbox full of probabilistic methods, one of the results can be the pdf/cdf curves for the desired outcome.  We may also identify the most likely failure conditions. We also will have sensitivity data which will tell us which random variable is critical. If one of the critical variables were evaluated using only engineering judgment, we might decide to create a test to gather data, and then run the analysis again. And this is only some of the benefits of Probabilistic Technology. The bottom line is that Probabilistic Technology has all the benefits of statistics and deterministic models, and yet provides much more information.

2. What is the difference between statistical approaches and probabilistic technology?
Statistics uses past performance data to predict future performance. For example, if one was interested in how long it takes to get to the airport from some office, he or she might collect elapsed time data from people traveling to the airport from the office to determine how long it took them to make the trip. The data could then be fitted to a statistical distribution, and one could use this to predict the probability of getting to the airport in a given time. The emphasis here is on the output time, while the input variables such as route and speed (as well as uncertainties in those variables) are ignored.

In contrast, probabilistic technology uses physics or rules to describe the process that yields the desired outcome, and applies statistics to quantify the input (as opposed to output) variables. Thus, in our example, we might collect data on the input variables such as speed and distance, as well as data on other variables such as delays due to accidents, effect of time and date on traffic flow, whether the driver is aggressive or passive, etc. Using that data, we can then create a model to calculate the time to the airport. If we don't have the data available, we can use experts to estimate the missing variable's distribution parameters.

After applying probabilistic technology to this problem, the output will be a much more accurate estimate of the probability of reaching the airport before a given time on a given date. In addition, this technique provides the sensitivity of the output variables to changes in the input variables or the distribution parameters for the input variables. We can find out if the output is significantly affected by lack of data or any assumptions we might have made.

The greatest benefits of probabilistic technology over statistics are that: (1) predictions are more accurate; (2) it is often easier to obtain data on input variables that output results;, and (3) more information is available.

3. How do Monte Carlo simulations relate to probabilistic technology?
Monte Carlo simulation is a rudimentary form of probabilistic technology. While Monte Carlo simulation provides the most accurate results when they work and are practical, the problem is that they often are impractical. They are time intensive, often prohibitively so, and they cannot be performed unless the model has a closed form solution. Other probabilistic methods such as First Order Reliability Method and Second Order Reliability Method are available which get around the limitations of Monte Carlo simulations and still provide accurate results.

4. Why do we need probabilistic technology?
Because much more accurate information is available, probabilistic technology allows organizations to make more informed decisions, to improve their bottom line and product performance, to design safer products with high reliability, to ensure system availability, to minimize life cycle costs, and to quantify risk and liability.

5. Is probabilistic technology mature enough for practical applications?
Yes, because of the availability of commercial software tools, practical analytical methods, and computer power.

6. At what stage of a project should probabilistic technology be implemented?
Probabilistic technology should be implemented at all stages of the product development and decision making, but the earlier the better.

7. Which industries have potential applications in probabilistic technology?
Insurance, real estate, finance, manufacturing, engineering, software, hardware, aerospace, weaponry, logistics, construction, service, and, in fact, all industries.

8. How do I become a registered user in the Probabilistic Technology Community?
You can register right now.

9. How do I become a member of ASQ?
To become a member of ASQ and to take advantage of all the benefits membership has to offer, go to www.asq.org and click on "Membership."