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Welcome, Troubleshooters! Thanks for tuning in to Troubleshooting Thursdays again. If you missed it, be sure to check out last week’s post on enhancing trainee buy-in using employee training agreement letters. Today we’re beginning a new series on the digitally connected factory, big data in manufacturing and what to do with all of the data that it generates.
For years now, industry experts have been talking about unlocking previously hidden value in manufacturing processes by harnessing new data streams. In Part 1 of this series, we’re going to look at how those streams can actually become overwhelming torrents of data.
Manufacturers are warming up to digitization
Initially, the manufacturing industry was slow to adapt to the new world of digital interconnectivity and data mining. While the transportation, retail, energy, healthcare, city management and other sectors were quick to jump on the data bandwagon, manufacturers lagged behind.
It’s only in the last few years, with the industrial world abuzz with the promise of Industry 4.0 and even Industry 5.0, that manufacturers have awakened to the idea that they can only remain competitive and grow profits by embracing digital connectivity. And they have been embracing it. In fact, a 2014 survey by The Economist’s Intelligence Unit found that 86% of manufacturing executive respondents had initiated “major increases” in their data collection in the previous two years.
This is important, because according to McKinsey Global Institute, systems based on IIoT (the Industrial Internet of Things) will create up to $11 trillion in new value for manufacturers by 2025.
But can you have too much of a good thing? That same Economist survey also found that only 14% of respondents had no problem managing the “glut” of data coming from real-time production sensors, and fewer than half (42%) felt they had a good data management strategy.
Going from smart to brilliant
To understand the quantity of big data in manufacturing that is being churned out by the most state-of-the-art digital plants, consider GE’s new battery production facility in Schenectady, New York. GE has invested heavily in taking the smart factory to the next level—the “brilliant factory.” The Schenectady plant is just one of their new, super-efficient, totally connected digital plants. At this location, they collect 10,000 different variables of data, some of them as often as every 250 milliseconds.
Facilities such as this have many control systems to handle all the tasks that have to be accomplished in the plant—maintenance, scheduling, material handling, machine error codes, quality inspection, etc. Big data in manufacturing is coming in from all of these systems.
Additional data sources manufacturers have access to include customer feedback, process historian systems, existing enterprise resource planning systems, accounting and financial data, pre-existing supply chain management systems, and after-sales failure data.
The sheer quantity of data collected in this new generation of production facility is astounding. But even less “brilliant” plants that are just starting to dig into new data streams may find they are overwhelmed by it. And the amount of data to sift through is going to get bigger, not smaller—a 2019 MPI study found that 76% of manufacturers intend to increase smart devices and embedded intelligence in their production processes, and that 66% are going to increase investments in IoT-enabled products in the next two years.
How much of this data is really valuable? How much is dispensable, redundant, or otherwise superfluous? How much of it is just noise?
Finding the signal in the noise
Data management experts tell us that at any given time, much of it is in fact noise. “The image of a needle in a haystack doesn’t begin to portray the challenge of big data in manufacturing,” writes Sundeep Sanghavi of Razorsight, an analytics solutions provider. Each data stream has the potential to generate a relevant insight at some point. It’s just a question of finding the “right insight at the right time.”
The key to leveraging manufacturing data is to find the signals in the noise—to isolate the relevant data streams from those that are just a distraction at this point. However, 44% of manufacturers say that their company’s limited understanding of what to do with IoT data is their biggest obstacle to extracting the value from it.
And that’s it for today, Troubleshooters! Tune in again next week for Part 2, when we’ll talk about how to cut through all of this noise and find the signals that really matter to your enterprise.
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