Humans have been collecting data and using it to make informed decisions on crop storage since about 18,000 BC. Manufacturing data analytics tools have been a staple of factories and plants since Henry Ford first started making cars on an assembly line. As Industry 4.0 continues to evolve and the power of manufacturing analytics tools become clearer, more and more organizations are asking, “How can these tools help us do better?”
For most of its existence, data analytics were fundamentally backward-looking in the sense that organizations were relying on historical data to make better informed decisions. With the dawn of IIoT, there was a shift so leaders could access up-to-the-minute data from a vast number of physical machines. The quantity and speed of data provided more real-time insights but also ushered in the need for manufacturing analytics software to help work through the millions of data points collected. As those software programs became more sophisticated, the tools they offered began to offer stronger and better predictions. Now, eyes are turning in larger numbers to the future, and demand is rising for predictive analytics tools for manufacturing.
Predictive Analytics Tools for Manufacturers Look Forward, Not Backward
Today’s world is smarter, leaner, and faster than ever before, and data is everywhere. IIoT has created a whole new wealth of information, and that has driven wide-spread adoption of data analysis solutions across nearly every industry to help understand it. Manufacturing data analysis software is becoming a critical tool for plants that want to remain competitive but, now the real gold standard for data is incorporating predictive analytics tools for manufacturing.
Traditional, manual data analysis is on its way out, and the usual arguments for adopting software-based manufacturing analytics tools remain truer than ever. Manual data analysis is time consuming, tedious, expensive, error-prone, and all but impossible to use with the volume of data created by IIoT. This only increases the growing demand for predictive analytics tools. When paired with historical and real-time data, the value is immeasurable. This is because historical and real-time data can help inform the future in a meaningful way. Manufacturing data analytics software takes that even further by leveraging AI and predictive analytics to make predictions about the future, which become more and more accurate over time.
Businesses that have adopted predictive analytics tools are finding that machine learning is much better and much faster than humans at making forecasts. In addition, AI is better at adjusting as additional data is collected. Businesses that adopt predictive analytics tools are seeing real results, which is why these tools for manufacturing will be increasingly important in the coming year.
How to Use Predictive Analytics
It’s easy to understand how gaining information about the future would be helpful, but the term “predictive analytics” may be a bit mystifying. So what is it? A practical way to think about how to use predictive analytics is to think about how it will directly impact your operations through predictive maintenance.
Unplanned downtime costs manufacturers around $50 billion each year. In fact, it’s one of the main causes for negative outcomes related to productivity and revenue. Predictive analytics tools for manufacturers can help businesses nearly eliminate unplanned downtime while providing information on when it would be best to schedule necessary maintenance, so it doesn’t affect output.
Manufacturing analytics software, like Sightline EDM, collects real-time IIoT data from sensors on physical machines, sorts it, analyzes it, and gives manufactures a current view of what is happening in their plants, including alerting team members to anomalies in the data. By combining that current information with historical data, the software can provide a forecast for what is likely going to happen in the future.
For example, your manufacturing data analytics software is collecting data about equipment and analyzing it in real time to let you know when something doesn’t look right. That alone can be extremely valuable. Because the software is able to continuously collect and analyze data to detect patterns, it can also start to alert you when the data conditions are beginning to resemble the conditions for a potentially negative event and alert you before that event occurs. That means you will have the time to examine what might be happening and execute a plan to correct for it without ever having to deal with the negative consequences of unexpected downtime.
How Do You Deal with Outliers in Predictive Analytics
When collecting data, sometimes there’s a piece of information that just doesn’t seem to fit. That piece of data is called an outlier, and there are a few reasons it might be there. Sometimes, it’s a technical error—a number that was misrecorded, a glitch in data transmission that impacted correct delivery, or an input mistake. Sometimes, it’s a natural deviation that needs to be further examined to understand the significance. The uncertainty behind deviations can cause confusion surrounding how to deal with outliers in predictive analytics.
The good news is that data analytics tools for manufacturing can simplify how you should deal with outliers in predictive analytics. First, data visualization can help provide a big-picture view of how all your data relates to each other, so you can see where the outlier is in connection to other points. Second, predictive analytics will help mitigate the effects of outliers naturally by using machine learning and AI to put that information in context and correct for their effects.
The Benefits of Predictive Analytics Tools for Manufacturing
The short reason predictive analytics tools for manufacturing are so valuable is because they increase profits and decrease costs. Manufacturing data analysis software can:
● Forecast upcoming demand fluctuations
● Predict maintenance timelines
● Prevent downtime
● Increase output
● Maximize usage of inputs
● Increase efficiency
More importantly, earlier adopters of manufacturing analytics tools will have a significant advantage over competitors who do not yet understand how to use predictive analytics.
Sightline Leads the Field in Predictive Analytics
Sightline EDM is a leading manufacturing analytics software and a frontrunner in helping manufacturers leverage predictive analytics and other tools to take their businesses to the next level. With Sightline EDM, you can see all your industrial and manufacturing data in a single, unified platform.
By streamlining anomaly detection, root cause analysis, and predictive analytics on hundreds of metrics across production facilities into a nearly simultaneous process, Sightline EDM offers accurate and quick current information alongside future predictions.
With so much data and so little time, being able to understand what story your data is telling you at a glance is vital. Sightline EDM provides visualized data in a single dashboard that lets you see the big picture, while also offering the ability to dig deeper, if needed.
Sightline EDM offers proactive manufacturing analytics tools, which means it is continuously monitoring and analyzing the data currently being collected. It then adds in historical data to provide context and make connections throughout what you are seeing. The result is a forward-looking, data-informed view of what is likely to happen next.
Learn how Sightline EDM can offer future insights for your business with the power of predictive analytics or get to the root of an existing issue by scheduling a free consultation today with our team today.
FAQs:
Predictive analytics will help mitigate the effects of outliers naturally by using machine learning and AI to put that information in context and correct for their effects.
Manufacturing data analytics software is collecting data about equipment and analyzing it in real time to let you know when something doesn’t look right.
Businesses that have adopted predictive analytics tools are finding that machine learning is much better and much faster than humans at making forecasts, and at adjusting as additional data is collected. Those businesses are seeing real results.