The Benefits and Challenges of Real-Time Manufacturing Data Collection
By Bill Dykas
September 13, 2021
By Bill Dykas
September 13, 2021
The quality of your manufacturing data analytics is proportional to your data collection capacity.
It’s not difficult to understand why. Data collection is the foundational element of the Internet of Things (IoT). Industrial IoT (IIoT) projects hinge on how well your organization can:
Many business initiatives would benefit from unlocking the real-time data in your machines and devices on the shop floor. Access to information is powerful. Better data can help your business:
If the benefits of accessing IIoT data are so great, why aren’t more manufacturing companies doing it?
It helps to look at some data. In 2019, Bain & Company surveyed more than 600 executives and found their enthusiasm for industrial IoT projects waning. The projects, such as those aimed at delivering predictive maintenance capabilities, took a long time. Issues during the implementations concerned them, such as difficulties porting data across various formats and transitioning risks. A NetSuite Brainyard article points to the projects’ high costs and the significant requirements for structural change required. Researchers from Bain were optimistic that the outlook might change with progress in sensor technology, edge computing and analytics, and 5G connectivity.
The conditions created by the current pandemic look like they’ll continue to affect us into the future. While demand is high and its value recognized, we have yet to see explosive growth in implementations. A recent McKinsey article put it well:
“Despite tailwinds from declining compute power costs and improvements in IIoT integration … few manufacturers have successfully scaled up their IIoT-enabled use cases in a way that achieves significant operational or financial benefits.”
Why haven’t IIoT projects delivered at scale the way many had envisioned? I’d argue that one of the issues is real-time data collection. The good news is, it’s the problem and the solution.
First, real-time, low-latency data collection is complex. There are many machines and devices from which we need to collect data. They don’t speak the same language, and they have different business owners.
Second, in planning IIoT projects, many organizations make the mistake of looking past the data collection stage. Remember, it’s about collection capacity. There are many data sources, including data from sensors, cameras and more. It’s challenging to determine what data we need to get the answer we seek. Because there’s a lot of data we can collect, there’s a significant potential for noise if it’s not distributed and transformed. Failing to consider processes and tools for distributing and transforming data prevents organizations from realizing the value of real-time data collection.
We’ve established that it’s vital to remember that real-time data collection doesn’t stop at collection. We must manage, distribute and transform the data to do the job we want it to do.
What is required to collect real-time data? Much of this is addressed in this Telit white paper: “Overcoming the Challenges of OPC for Industrial IoT Applications.”
First, we must be able to operate within the existing environment. Not every machine has an OPC interface. Not every machine is network-connected. Equipment using dated standards will need to be supported. Investments made in current networking hardware — including routers and gateways — from diverse vendors must be protected. Older and new machines must coexist and give up their data for effective applications.
We must continue to develop and improve the device drivers. We need to consider how we’ll read the data. We must ensure those technologies can read entire data blocks from PLCs (e.g., Rockwell) to meet end-user needs and find the unique answer they seek. We need the ability to collect data from multiple mediums, including audio and video.
The data collection technology must be smart enough to know what to do if the system it’s connecting to is offline. Then it must store and forward the data to distribute it in a cost-effective and efficient manner. Of course, we need to transform the data to ensure it’s transparent and accessible and has context within the business process.
Above all, you need the flexibility to change how you collect data depending on the use case and environment. You need to be able to do this without developers building custom code to create the interfaces connecting diverse components.
The right technology can mask the complexity of connecting the factory or plant floor to IT systems. A solution must be flexible and agile to collect data when needs change and make data accessible. It doesn’t take years to prove the value of operational data and what it can do for your business. The system must be able to launch projects in a matter of weeks.
Connecting needs to be as simple as selecting a driver and typing in the machine’s address. That’s what Telit deviceWISE® does. It masks complexity and acts as the edge intelligence. Operating on multiple hardware platforms, it has a growing library of hundreds of device connection drivers. deviceWISE comes with industry-standard connection software like OPC UA and DA and device-specific native drivers. It can read and write directly to enterprise systems. The edge logic engine transforms the data without the need to write custom code. It’s all served up in dashboards or an HMI by Telit deviceWISE VIEW and third-party software.
IIoT’s promise can become a reality by improving real-time data collection capacity, especially when IoT benefits are becoming business necessities.