What are Predictive Analytic and predictive modeling and how can industries benefit from them today? While both of these techniques can be applied in many different types of analysis our discussion will be around the benefits for plant and facility maintenance uses. First, let’s talk about what the differences are.
Predictive analytic uses a variety of statistical techniques from equipment modeling to data mining so that current and historical data can be analyzed to make predictions about future equipment events or failures.
Predictive modeling leverages patterns found in current and historical data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential failure associated with a particular set of conditions. These could be as simple as a loss of efficiencies or as critical as mechanical failure.
Today, many of the main stream software companies today use both of these techniques in their software suites to access a variety of data point’s sources and apply a variety of mathematical and statistical formulas to discover the best decision for a given situation.
While most software developer’s offer advanced predictive-analytic software for identifying impending equipment problems and avoiding shutdowns or equipment failure. The difference in today’s predictive software’s can vary on how they leverages existing data, the modeling and infrastructure needed to provide early and actionable warnings of impending problems.
I recommend defining a long term strategy before choosing the software to deploy at your organization. Here are some examples that you should consider; improve your operations, optimize maintenance resources and procedures, maximize equipment performance, and avoid unexpected shutdowns and catastrophic failures, increase availability, ensuring regulatory compliance, reliability, and efficiency.
Also recognize that every piece of equipment is unique, your organization may be challenged with aging equipment, internal technical resources, limited budgets and most importantly lack of vision. Having a clear vision and organization support is critical to any long term sustainability. All of these questions need to be defined before purchasing your predictive analytic software.
Now that have a plan you can refine short list of software by functionality. Some examples are, do they create empirical models, does the software utilize templates for distribution across like assets, and does the software write its notification back to a data historian, Prophecy, PDH or OSIsoft PI System.
At the end of the day your Predictive analytic software integration should give your company a competitive edge and improve the ROI, substantially and ensure integrity. Predicative analytical software is becoming the science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right actions in the shortest time possible.