We recently saw a study that Oil & Gas drilling contractors spend $100 to $150 million dollars a year in non productive time caused by failure in their equipment.
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.
In the maintenance industry, 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.
Predikto is able to create models to predict failures in similar equipment.