Technically speaking, Industry 4.0 is the optimization of technology to revolutionize manufacturing and industrial systems. In practice, that means Industry 4.0 is making manufacturing better, and production cheaper, by leveraging an improvement of smart process-improvement software.
These smart software solutions are enabled through the Industrial Internet of Things (IIoT). The IIoT lets connected machines communicate. And in some cases, when predictive automation or other data-driven triggers are in use, those machines can even make their own decisions.
Consider a production line that automatically shuts down if ideal moisture content parameters are not being met. This data-driven, automated shutdown can alert operators to the production issue and potentially save money on productivity and unwanted capital expense.
No matter how smart your software is, you need a strategy for collecting the right data at the right time. The right data will effectively actuate the software and deliver better results from smart feature integrations like artificial intelligence, machine learning and predictive analysis through data mining and modelling.
The better your data, the better your outcomes. But data can also overwhelm your automation if you don’t create a strategy to collect, analyze and share it at the right time.
“Too often, a company invests in technology like AI and then searches their operations for a problem to solve,” says Scott Schneider, VP of Engineering for Finna Sensors. “What we’re saying to our customers is, tell us your challenges and we will work to serve up the right data to help you solve those problems.”
Clearly, it’s no longer enough to have the right tools; today you need a strategy to help achieve the most benefit from your investment in smart solutions. But the right strategy can be hard to discern.
That’s why we partner with our customers to effectively implement our sensor hardware and software, and to identify the robust data points that will improve the process issues encountered. We help you wrangle the right data into the right automated inputs for your improved productivity.
If you do it yourself, your data management strategy needs to consider your production pain points and look for solutions to overcome them. You should create a process or model to achieve repeatable outcomes.
Ask questions about your data like:
The answers to these questions will help to inform your individual process or model for collecting and using data effectively.
But be careful in your assessment of these questions and with your answers. Data strategies can very often be data-heavy; they have too much info in them.
The key factor to achieving the best results from smart software features is to determine which data to collect and which data to send. Too much data is as detrimental to business as too little. This means data overload is a common problem that can create clutter and reduce the effectiveness of many Industry 4.0 systems.
Avoiding this clutter is one reason Finna Sensors works upfront to determine which issues our customers need to solve.
“We work with our customers to identify their problems,” continues Schneider. “Instead of having our customers invest in technology and analytics that look for problems to fix, we first identify the business pain points, and then we determine which data is required to answer them. It ensures the data collected is the data that’s needed—and the data that’s used. No more data overload.”
At Finna Sensors we thrive on finding smart industry solutions. Good data is clearly a key requirement, but we go well beyond the data collected:
Read about how a proper combination of hardware and software can enhance your Industry 4.0 manufacturing strategy.
Learn more about Finna Sensors, our people, and solutions to see how we can help you advance in Industry 4.0 and beyond.