It may not be the first thing that comes to mind when thinking about data analytics, but former SNL star John Mulaney does a bit in one of his stand-up specials about investigating a murder in the 1930s before anyone knew about DNA that actually does a good job describing its importance:
“One cop would just walk in and be like, ‘Detective! We found a pool of the killer’s blood in that hallway!’ And he would just be like ‘Hmm…gross! Mop it up. Now then, back to my hunch…’”
As funny as that is, it sums up how data is only useful once you know it’s there and take the time to use it. Otherwise, that important information is washed away, never to be seen again.
Luckily, manufacturers in the industrial sector have been using sensors and connectivity options to mine data for a while now. And it’s a rare company that isn’t doing some kind of data analytics to improve their processes. But in many cases the data that’s being collected isn’t complete, or it’s not being utilized to its full potential.
Basic uses of data, and how to go beyond
If your company is working under Industry 4.0 standards, it’s likely you’re using data to measure and monitor things like order fulfillment time, machine set-up time, and inventory turns. Real time production data collection allows you to calculate overall equipment effectiveness (OEE) and gives you production rates for each machine. This in turn allows you to more efficiently schedule your workforce and can help increase your plant’s overall uptime.
You may even be tracking employee churn, reportable health and safety incidents, and revenue per employee. Manufacturers who increase their employee retention through workforce analytics can significantly lower their yearly payroll costs. On average, it costs 33% of a worker’s salary to find, hire and train a replacement, not to mention the temporary dip in productivity. Injury prevention can have a return on investment of anywhere from 2/1 to 6/1.
A Game Changer
But properly utilized data can do much more. Combined with AI software, data can be used to identify where and how processes can be automated in order to decrease operating costs. It can identify areas of opportunity within your plant that may need a complete revamp to optimize production, or where it can be tweaked to produce the same result.
Now with more advanced wearable biosensors coming on to the market, properly mined data can also identify safety issues for your workforce by keeping track of hazardous zones or blind spots and areas where collisions seem likely. It can also help redesign your factory workflow to solve these problems. This IIoP (Industrial Internet of People) subset of IIoT data will become an important source of information as manufacturers look to transform their operations for 21st century demands. Integrating your workforce into your data collection will become a necessary step for all manufacturers as we take the next step toward AI-optimized manufacturing.
Proper data analysis will be the edge for those manufacturers willing to use it. The integration of all parts of your business–product, machines, and people–as part of the dataset will give you a more complete picture of what’s happening on your production floor. Analytics will be able to identify every inefficiency and offer up a solution that will improve your output to its highest possible level. Properly used data can solve all the small niggling issues you’ve identified but can’t visualize a solution for, as well as a window that opens a view to issues you’re currently unable to see.
Technology writer Marla Keene works for AX Control Inc., an industrial automation supplier located in North Carolina.