IoT predictive analytics is an emerging technology that is changing the way manufacturers approach every aspect of manufacturing, from business operations to the shop floor.
IoT, short for the “Internet of Things”, connects equipment to a central dashboard, making collecting and analyzing a vast amount of data effortless. IoT predictive analytics examine real-time data from connected devices allowing your company to predict potential issues before they become costly problems.
For manufacturing managers, understanding how IoT predictive analytics works and the benefits it can bring to their production lines leads to smarter decisions, reduced downtime, and longer equipment life.
In this article, we’ll break down the basics of IoT predictive analytics and explore how it can improve your manufacturing processes.
What Is IoT Predictive Analytics?
IoT predictive analytics is a manufacturing tool that uses data from connected devices and sensors to predict potential future events or failures.
In manufacturing, these devices are often embedded in machines with Wi-Fi connectivity or attached later to machines without an Internet connection to collect data on metrics like temperature, vibration, pressure, and humidity.
The analytics part comes in when this data is processed by IoT software, helping you spot patterns that signal an issue before it happens.
Instead of waiting for a machine to break down, IoT predictive analytics allows you to plan for maintenance ahead of time. This process, called predictive maintenance, reduces downtime and helps your facility run more efficiently.
How Does IoT Predictive Analytics Work in Manufacturing?
Predictive analytics in manufacturing starts with setting up an Industrial IoT ecosystem within your factory. After IoT implementation, data is collected and analyze per your configurations. Here are some of the aspects of how IoT predictive analytics works.
Start with Sensors and Data Collection
The foundation of IoT predictive analytics is data – and lots of it! IoT devices collect information from your equipment 24/7. Sensors can measure almost anything, from motor speed to temperature and pressure.
Every second, this data flows from your machines to a central platform where it’s stored and processed. Over time, this gives you a detailed picture of how your equipment is performing.
Revel in Data Processing and Algorithms
The collected data is then analyzed using machine learning (ML) algorithms. These algorithms learn the normal patterns of your equipment’s operation. For example, if a motor runs at a certain temperature when it’s in good shape, the system will recognize that as a baseline.
When a piece of data deviates from that norm, the algorithm flags it. For example, your team willl be notified if a motor heats up or is spinning slower or faster than usual. This gives you a heads-up that something might go wrong soon. These notifications can be set up to notify you on systems at your facility or on your mobile phone so you can address issues on the go.
Top 3 Benefits of IoT Predictive Analytics in Manufacturing
You’re probably already imagining a factory with safer workers, a reduced workforce, and lower maintenance costs. You’re on the right track. Here are some other benefits of IoT predictive analytics in manufacturing.
1. Reducing Unplanned Downtime
One of the biggest benefits of IoT predictive analytics is minimizing unexpected equipment failures. Manufacturing managers know that an unscheduled machine breakdown can lead to lost production time and expensive repairs. Predictive analytics prevent you from being blindsided.
Instead of reacting to problems after they happen, you can take a proactive approach and schedule maintenance during periods of low production. This way, you avoid disruptions, lost profits, expensive repairs, and unhappy customers.
2. Extending Equipment Life
All manufacturing managers can agree on one truth: When machines are maintained properly, they tend to last longer.
IoT predictive analytics allow you to perform maintenance exactly when it’s needed, not too early and not too late. This gives you insights so that you’re not overworking your equipment or performing unnecessary repairs, both of which can shorten its lifespan. Instead, you’ll be able to keep your equipment in optimal condition for a much longer time.
3. Improving Operational Efficiency
IoT predictive analytics can also help you identify inefficiencies and bottlenecks in your manufacturing processes.
Sometimes, it’s not about preventing a machine failure but optimizing how machines are used. The data collected by IoT devices can reveal patterns that point to areas where improvements can be made, such as better load distribution across machines or adjusting the timing of production cycles. This will help your team make smarter decisions and improve workflows.
Key Components of an IoT Predictive Analytics System
Industrial IoT systems are composed of a variety of devices working together, a concept you are very familiar with as a manufacturer. Successful IoT implementation is all about setting up the optimal environment so you can track everything your heart desires and improve overall performance.
Here are a few of the key components you should get acquainted with before starting your IoT journey.
1. Sensors
As mentioned earlier, sensors are a key part of an IIoT system. Sensors gather the data that will be analyzed later, so it’s important to choose sensors that are appropriate for the specific types of equipment in your facility.
For example, vibration sensors are commonly used in rotating machinery, while temperature sensors might be more useful for monitoring ovens or other heat-producing equipment.
2. Data Analytics Platform
The analytics IoT platform is where all the data gets processed. It’s essentially the brain of the operation. It houses the machine learning algorithms that make predictions. Many platforms, like ProphecyIoT, offer user-friendly dashboards that make it easy to see real-time data from your equipment and receive alerts when a machine needs attention.
Choosing the right platform is critical because it needs to handle the volume of data your sensors are generating while offering insights that are easy to understand. ProphecyIoT offers many integrations and is the #1 IoT platform recommended by Infor.
3. Machine Learning Models
Machine learning models are what make predictive analytics possible. These models are built to recognize patterns in your data, learning from historical performance to predict future outcomes.
As more data is collected, ML models become more accurate. While you don’t need to be an expert in machine learning to use predictive analytics, it’s helpful to understand that these models are the driving force behind the insights you’re seeing.
How to Implement IoT Predictive Analytics in Your Manufacturing Facility
Implementing a system that is so game-changing should always start with a consultation with an IoT expert. IoT implementation can feel overwhelming and it takes a solid strategy to implement correctly.
Here’s how implementation of IoT predictive analytics solutions will look from a high level.
Step 1: Identify the Most Impactful Machines
Start by determining which machines are most essential to your production line. These are the machines where a failure would cause the most significant disruption to your operations. Focusing on these machines first will give you the most immediate return on investment.
Step 2: Install Sensors
Once you’ve identified the critical machines, the next step is to install the necessary sensors. Work with an experienced provider who can recommend the best sensors for your equipment and help with installation.
Step 3: Choose an IoT Platform
With the sensors in place, you’ll need a platform that can analyze the data they collect. There are several IoT platforms available, so it’s important to choose one that integrates easily with your existing systems and offers the insights you’re looking for.
Step 4: Start Small, Then Scale
Begin with a pilot program focused on a few key machines. This will allow you to get comfortable with the technology and evaluate its effectiveness. As you see results, you can gradually expand the use of predictive analytics across your facility.
Common Challenges in IoT Predictive Analytics
Between implementation snafus and a wealth of data that is almost impossible to imagine, our experts have seen it all. Here are two of the most common pitfalls we’ve helped clients resolve.
Data Overload
Data overload is a challenge that can be avoided with careful planning. You may find your existing infrastructure struggles to handle the sheer volume of data generated by IoT devices. Make sure that your servers can store and manage the amount of data that will be collected by your IoT devices before booting up.
Even with enough data storage, you can end up with more data than you know what to do with. A good analytics platform will help you sift through the noise and focus on the most relevant information.
Integration with Existing Systems
The second challenge is integrating IoT predictive analytics with your existing manufacturing systems. It’s important to choose a platform that can communicate with your current equipment and software, ensuring a smooth transition.
Learn more about how ProphecyIoT integrates with existing systems.
The Future of IoT Predictive Analytics in Manufacturing
As more manufacturers adopt IoT technology, predictive analytics is expected to play an even larger role in operations. Machine learning algorithms will continue to improve, offering more accurate predictions and enabling even more proactive maintenance strategies.
Looking ahead, predictive analytics is primed to become standard practice in manufacturing. It’s simply too helpful in increasing efficiency, reducing costs, and staying competitive in an increasingly data-driven world.
Learn More About IIoT in Manufacturing Today
IoT predictive analytics offers manufacturers a powerful way to stay ahead of equipment issues and improve operational efficiency. As IoT technology continues to evolve, integrating predictive analytics into your manufacturing operations will become even more essential for staying competitive.
If you’re ready to explore how IoT predictive analytics can benefit your company, contact us today for more information and personalized solutions.
