Unplanned outages in the industrial world are always undesirable, and can sometimes even be disastrous. They cause bottlenecks, significant labor overhead, more scrap production, and even loss of business opportunities. And unplanned downtimes are very expensive. According to recent statistics, plants in the oil and gas, chemical, and power industries routinely suffer 5 to 7% unplanned downtime losses due to poor maintenance practices. And in the automotive industry, it has been shown that one minute of unplanned downtime costs an avg. $22,000, or $1.3 million per hour.
With such high price to pay for unplanned downtimes, why are most industrial plants only engaged in emergency maintenance practices? Because maintenance is labor-intensive and also incurs significant costs, and it is hard to justify any downtime for preventive maintenance, when there is no problem. But by the time the problems become visible, it is often too late.
Advanced Failure Predictor (AFP) by algorithmica technologies is our answer to this difficult dilemma. It uses historical data and advanced machine learning algorithms to understand the dynamics of your plant. At the same time it monitors the plant it in real time, and projects current trends into the future. By doing that AFP can forecast and alert you to problems days in advance, thus allowing the maintenance team to order parts, and schedule downtime and repairs. By doing this, AFP will help you reduce your maintenance costs by at least 20%, and increase plant availability by about 2%.
Even better, AFP is not a static model. Thanks to its machine learning, once implemented in your plant it observes and continues to learn in real-time. Even as the conditions in your plant change, AFP continues to provide accurate and reliable monitoring and failure prediction.