What is Predictive Analytics

Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predictive variables from past occurrences, and exploiting it to predict future outcomes.

Predictive modeling draws from statistics and optimization techniques to extract accurate information from large volumes of data. Modeling techniques produce interpretable information allowing maintenance personnel to understand the implications of events, enabling them to take action based on these implications.

Predictive analytics builds on prior investments in enterprise asset management (EAM) systems, combines real-time data from sensors and other acquisition techniques with historical data to predict potential asset failures, and enables the move from reactive (scheduled, break-fix) to proactive (condition-based, preventive) maintenance.

Predictive analytics can be used to analyze the real-time data from the sensors in the context of historical data and asset information held in the EAM system to predict future conditions such as faults or failures and produce alarms or schedule maintenance or replacement. Predictive analytics also complement other existing systems such as data historians and SCADA systems.

Replace intuition with predictions

Many industrial manufacturing companies run a reactive maintenance organization. That means they wait for things to break before replacing parts. Studies have proven that running a preventive maintenance plan reduces expenses by up to 75% in the long run.  The challenge is that many of the Preventive Maintenance schedules were developed on intuition.  Changing the oil filter once a year is enough.  Most organizations are sitting on a wealth of data like prior downtimes (failures), work orders, parts, and the rich information captured in the indicators / meters.

Predikto has the skills and technology to analyze the maintenance data in order to provide a preventive maintenance plan and predictions to help reduce failures and reduce the overall cost of maintenance.

With Predictive Maintenance, organizations can spend less time and fewer resources repairing things too early or avoiding fixes when it is too late. Industry studies have documented cost reductions in the 3-5X range for catching problems early as opposed to catching a problem once there is significant damage to the equipment. Companies can spend more time focusing on what will happen next and be smarter about the preventive maintenance. It is much easier to fix a problem before it happens rather than reacting after the production floor has come to a screeching halt.