Amir Kupervas
Written by Amir Kupervas

Predictive Maintenance: What’s the Economic Value?

Predictive maintenance will help companies save $630 billion by 2025, according to McKinsey.  However, a new paradigm is required for analyzing real-time IoT data.

Predictive maintenance, i.e. the ability to use data-driven analytics to optimize capital equipment upkeep, was identified in a recent McKinsey Global Institute report as one of the most valuable applications of the Internet of Things (IoT) on the factory floor. The report, The Internet of Things: Mapping the Value Beyond the Hype, calculated that predictive maintenance manufacturers’ savings would total $240 to $630 billion in 2025.

Predictive maintenance in factories could reduce maintenance cost by 10 to 40 percent by fostering better maintenance, according to McKinsey. It also reduces downtime by 50 percent,  and lowers equipment and capital investment by 3 to 5 percent by extending machine life.

A report by Deloitte University Press, Industry 4.0 and manufacturing ecosystems, provides examples in which, for companies like Schneider Electric and Caterpillar, predictive maintenance and understanding root cause of failures can offer millions of dollars in potential savings along with far fewer days of equipment downtime.

Realizing these advantages is not without challenge. Most IoT data, according to McKinsey, is not currently leveraged, squandering immense economic benefit. For example, only 1% of data from an oil rig with 30,000 sensors is exploited.

Because traditional Business Intelligence (BI) platforms were not designed to handle the plethora of IoT data streams, use of BI reports and dashboards only periodically leads to late detection of issues.

In addition, currently, alerts are set with static thresholds, leading to false alerts in case of low thresholds, and failed detection in the instance of high thresholds. What’s more, data may change by time of day, week or season with those irregularities being outside the limited scope of static thresholds.

Last, BI platforms were not designed to correlate between the myriad of sensor data—a correlation that exponentially boosts the likelihood of detecting issues. Let’s say an engine that is about to fail may rotate faster than usual, have an unusual temperature reading and low oil level. Connecting the dots between these sensor readings can multiply the likelihood of detection.

Automated Anomaly Detection

By addressing the deficiencies of existing BI platforms, Anodot’s automated anomaly detection paves the way for factories to realize the full value of predictive maintenance. Analyzing big data from production floors and machinery to deliver timely alerts, Anodot’s visualizations and insights facilitate optimization, empower predictive maintenance and deliver bottom line results.

Anodot leverages the IoT-generated stream data such as meter readings, sensor data, error events, voltage readings and more. Monitoring data over time—and in real-time—it uses machine learning to learn the metric stream’s normal behavior. It then automatically detects out-of-the-ordinary data and events, serving up diagnoses and making preemptive recommendations that represent significant cost savings on upkeep and downtime.

Rejecting old-school static thresholds, Anodot identifies anomalies in data that changes over time, for example, with built-in seasonality. This dynamic way of understanding what is happening in real time detects and alerts for real issues, often much earlier than a static threshold would have alerted. Without manual configuration, data selection or threshold settings, this platform automatically calibrates to achieve the best results.

Anodot algorithms can control data of any size or complexity, such as seasonality, trends and changing behavior, because they are sufficiently robust to handle an army of data variables, intelligently correlating anomalies whose connections may escape the limitations of a human observer.

Predictive maintenance offers a hefty opportunity for factories to save money on maintenance, downtime and while extending the life of their capital equipment. Automatic anomaly detection, such as Anodot, offers the best way to expose and preempt issues that require real maintenance in real time.

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