Unplanned downtime is one of the biggest causes for loss of productivity, displeased customers and loss of revenue, in the manufacturing domain. According to recent studies, the industry takes a blow of around $50 billion every year due to this.
To tackle the unbending situation with a solid solution, manufacturers across the globe have employed predictive maintenance in recent years. While failure analysis set the ball rolling for the large-scale predictive maintenance program in 1974, at present, a successful combination of failure analysis, along with an effective predictive maintenance project, does the job.
The plan of action does not only reduce the cost of maintenance by 20 to 30%, but also increases production on similar ratios, though the proportions vary from organization to organization.
The Industrial Internet of Things (IIoT) and business analytics tools like Virtual Infrastructure Analytics facilitate advanced predictive maintenance and comprehensive analytics, and helps in forecasting potential equipment failures. These programs enable manufacturers to make reliable decisions based on well-calculated predictions and problem statements.
For example, oil and gas companies can benefit largely from monitoring their machine assets. The machines on oil rigs often break down and anticipating these failures will help the companies make proactive maintenance decisions based on different thresholds.
Most solutions in the market at present, fail to provide adequate data or undersupply highly accurate predictions. They also fail to accord sufficient time for responding to those predictions. Virtual Infrastructure Analytics and predictive maintenance solutions by Wynyard Group, IBM, Uptake, C3 IoT, bring together machine learning and data science as a modern collaborative solution to empower enterprises with true prognostics and subsequent actionable insights.
Schlumberger, is a global energy services company offering flow assurance and consulting, formation evaluation, seismic acquisition and processing, artificial lift etc., to their customers in oil and gas industry. In 2016 the company announced that in less than a year, they saved more than $8 million by employing a new predictive analytics program for forecasting equipment defaults in fracturing pumps. Also, they estimated that the same program would save more than $30 million over a period of three years.
With predictive maintenance evolving considerably over the years, scientists at Palo Alto Research Centre (PARC) deployed newly advanced fiber-optic sensors in critical components of an energy project to analyze crucial internal parameters. Enhanced sensors can equip manufacturers with a comprehensive and accurate delineation of system health.
Global manufacturing can significantly improve with the adoption of prognostics accuracy, which will result in reduced downtime and augmented production and profitability.