IIoT Integration
Industrial Operational Analytics
Advancements in Operational Analytics will Optimize Your Factories
Operational analytics is the most critical component to an IIoT implementation.
It’s all about the data and using it to gain insight. Without using analytics to drive meaningful decisions in the industrial setting, IIoT practices and benefits are mute. Operational analytics occurs after the pertinent data in an industrial system has been collected at the source, moved into the appropriate storage containers, and prepared for analysis.
This preparation is critical because, without proper steps, bad data could be included in the analysis, skewing results. You can use operational analytics for process control, risk analysis, predictive forecasting, quality management, predictive maintenance, visualization and many more. What’s more, you can share industrial IoT analytics with small groups of people on a need-to-know basis or for company-wide consumption and real-time decision-making.
Predictive and Preventive Maintenance—IIoT’s Most Significant Benefit
Additionally, you’ll drive intelligent decisions and make the manufacturing process predictive and anticipatory instead of reactionary. Analytical insights will come through SQL queries and analyzing large swaths of data—compared against various specs. Overall, your collected data will help you better cater your manufacturing to the parts you have, deliverables you must meet, and the constraints in which they work.
Furthermore, when you analyze data for anomalies and typical operating values, you’ll better understand your assets and ensure you’re not having any issues. A typical example of operational analytics at work in the industrial manufacturing environment is the practice of predictive and preventative maintenance.
Most manufacturing managers cite predictive and preventive maintenance as the most significant benefit of IIoT. The benefits of this practice far outweigh any drawbacks. For instance, when you capture asset statuses such as vibration, temperature, and torque, you can compare these metrics to a machine’s regular operation and lifecycle curves. Thus, operations managers can identify when their machine needs maintenance BEFORE it stops production. Companies can prolong asset life while saving money by avoiding unforeseen downtime in manufacturing—which usually takes longer to recover from than planned downtime
The Need for Lots of Data
To accurately prevent and diagnose a potential issue for an asset, you need lots of data. You can analyze the data numerous ways, but cloud-based systems are the best for computing power scalability and the limitless number of trackable data points.
These versatile and malleable analytics systems are essential because they allow managers and operators to predict the best time for maintenance and drive production. What’s more, by using the collected values from operational assets, managers can compute the best time to run their systems to optimize supply costs or manufacturing constraints. For instance, it would be unwise to manufacture products if raw materials were too low, electricity was at peak cost, or inventories were high.
You can analyze the data numerous ways, but cloud-based systems are the best for computing power scalability and the limitless number of trackable data points.
Retire Your Spreadsheets, Move to Real-Time Data
If you’re still using Excel or similar tools to analyze your manufacturing data, you won’t have the ability to visualize and identify trends quickly. Some leaders populate Excel spreadsheets with thousands of data points that sit unused and unleveraged for years.
On the other hand, by analyzing your data through modern industrial IoT platforms that use real-time information, your managers and operators gain insights that improve and better control industrial processes. In other words, If you add smart sensors and transducers to your machines but don’t analyze the resultant data, you’re wasting money.
Improve Decision-Making, Optimize Your Factories
As an IIot integrator, manufacturing leaders turn to Results Engineering when they realize their company’s operational analytics capabilities are subpar or nonexistent. We help our clients design and install the proper hardware and software to capture all your manufacturing data, which improves decision-making while optimizing your factories.
Common Industrial IoT Operational Analysis Questions
How can I set myself up for the operational analytics that can drive meaningful preventative maintenance?
Because of the demand on networks when it comes to processing much more data, much faster, and with more precision, it is likely you will need to integrate new types of sensors with the capability of processing all this information more consistently and easily. Results Engineering has the experience to make this transition seamless.
Can my collected data be used retroactively?
With the right setup, you can not only use retroactive data, but you can also clean up, organize, and make your old data more valuable.
Learn more about our on-site IIoT integration audit and implementation roadmap >
Manufacturers have no idea how impactful IIoT/Smart Factory is to their plants.
Results Engineering created a free plant assessment to solve that problem. 30 questions and 30 minutes will reveal all you need to know.
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