Machine learning and predictive analytics have many differences but are both based on efficient data processing. To have an effective analytics strategy, you first need a standardized connectivity layer to have consistent access and visibility to asset health and performance data. It also strongly benefits from an IIoT platform to analyze the data. A true industrial IoT platform should offer machine learning to perform predictive analytics. The benefit is you don’t need to consult multiple data scientists to gather an opinion on your organization’s data.
Predictive analytics surround a set of mathematical techniques that look for trends in historical and current data to predict how things are likely to pan out in the future. Drawing on both descriptive and diagnostic analytics, predictive analytics create data models to help to inform future interpretations. The idea is to help you identify potential opportunities, or spot issues far enough in advance to make smart, timely decisions on how to prepare.
Predictive analytics are typically structured around answering a specific business question but can be applied in several ways to increase workforce productivity, reduce machine downtime and lower costs at your factory. Predictive analytics can also play a key role in ongoing maintenance. The ability to gather information to predict future events can be used as an early warning system for equipment that’s heading for failure, helping companies to mitigate the risk of unplanned outages. Reduced downtime avoids costly maintenance and lost revenue resulting from outages.
Machine learning is the technology that enables predictive analytics—and often a core feature of many common artificial intelligence (AI) applications. A machine learning algorithm finds patterns in the data and uses classifications it has available. It then uses these to predict the answer to a question. The algorithm relies on self-learning in response to the huge amounts of data it collects.
Machine learning is a powerful tool that, when used with an IoT platform, can gather data from equipment across your entire enterprise to optimize plant efficiency. The algorithm continuously scans network activity data including real-time status of equipment, lines, and processes. Machine learning helps equipment to self-learn over time and look for signs of changing data, which can signal the machine to recalibrate in response. Machine learning, in tandem with the IIoT, is critical to monitoring asset health and behavior. Maintenance can be proactively scheduled in response to signs of deteriorating equipment health to prevent unexpected breakdowns and costly downtime.
What sets machine learning applications apart from other types of predictive analytics is the human element. Machine learning works out models and predications to automatically readjust and in real-time, without human assistance. Predictive analytics, on the other hand, are specific to the question or situation at hand. They draw on relevant data so experts can interpret the data to find causes that help to answer a theoretical question. While predictions are made, it is up to the human expert to interpret the results based on past performance data.
Machine learning can be used for tasks you trust a machine to do better than a human. Whereas big-picture, strategy-driven decisions can rely on predictive analytics to provide useful insight, with a human making the final call. To learn more about how PTC uses IoT analytics to drive reliable, actionable insights, download our ThingWorx Analytics product brief.