Artificial intelligence and mobility— a proactive road to process control
Leverage artificial intelligence and mobile technology to make sense of plant data.
Engineers in modern manufacturing facilities have a large variety of tools at their disposal. In most cases there is no shortage of data available to these professionals, but in virtually all cases there is a shortage of another key resource: Time.
The large amounts of data available in modern, connected factories help today's engineers keep their processes stable and in control, but there is a time cost associated with managing that data. The time it takes to figure out what database from which to pull data, the time to mine that data, the time to pull reports, and the time to analyze them adds up. The amount of time spent only grows if they get into something more involved, like regression analysis. Timing is critical, especially if a process issue or production stoppage is the reason the engineer started the data mining and analysis work to begin with.
Every minute of mining and analysis could be one minute of lost production which equals lost profit. Some companies even track a metric called Nonvalue Added Activity (NVAA) which is the amount of time dedicated to work done that does not produce parts. An argument can be made that all the time data mining and analyzing process data can be put directly in the NVAA cost bucket. However, without data, how can an engineer know where to start to fix a production issue?
The answer is to leverage artificial intelligence and mobile technology to monitor and mine the data, complete the analysis in continuous real-time, and "push" the answer to the engineer. Utilizing tools that enable push analytics creates plant floor intelligence and visibility that will shorten time to root cause, reduce NVAA, and ultimately reduce loss.
Artificial intelligence and push analytics
As the connected factory grows and joins the Industrial Internet of things (IIoT), it has become possible to apply technology to eliminate the time associated with traditional data acquisition and analysis. Artificial intelligence and push analytics can automate the mining, analysis, creation of charts, and can send the information directly to the correct person to fix the issue. But there is a further step, which is breaking new ground: Doing all of this before there is a production problem, transforming the culture of the plant floor from reactive to proactive.
Pushing alerts and automating reports that can be sent to engineers is not new. The technology to accomplish this task was being used in the 1990s and is still used today. It is improvements in other technologies that enable continuous real-time analytics powered by artificial intelligence to be more affordable and available to the factory floor. Lower-priced data storage, increased connectivity of machines, and high-speed computing are also catalysts for this change in manufacturing. Now instead of pushing alerts and charts, the software can push answers through e-mail clients, text messages, and even smartphone mobile apps, enabling engineers to spend their time fixing issues or, better yet, preventing them.
Providing an intelligent IT automation platform that pushes answers based on the continuous automated analysis of every process on the floor can help anticipate potential process problems and point plant personnel to the source before a production loss occurs. Existing process data is collected, analyzed, and monitored at the cycle time of the production line, and information is sent to the right user at the right time; which is before there is a production loss, not after. This creates a cultural change on the plant floor, where effort is spent preventing issues at a controlled and manageable pace, instead of reacting to production losses in a frenetic and unpredictable fashion. For example, Trumble Inc.'s software product, Reveal, shows engineers emerging trends in the process data that could lead to a production loss. The software uses traditional, time-tested tools like statistical process control (SPC), regression analysis, and Shainin pre-control combined with high-speed computing, powerful algorithms, and artificial intelligence to enable a truly proactive approach to process management.
Challenges of proactive process control
Enabling plant floor intelligence and proactive control does have its challenges. One of the first things that needs to be addressed is getting the plant personnel to recognize that they will not be responding to traditional triggers like flashing red lights, stopped conveyors, and scrambling technicians and engineers.
Another paradigm shift is getting personnel used to fixing something before it breaks. Exception-based alerts based on trends in "good data" will show engineers where to go before a major process event occurs, saving time and money. Again, traditional triggers are absent in this scenario because the plant floor continues to work as designed. Once plant floor personnel embrace this new way to manage their process, they can begin to expand even further to enable concepts like true data-driven preventive maintenance. For example, in machining, preventive maintenance based on process data and part quality can save millions of dollars a year. Expensive cutters are often changed on a regular time interval or after a major quality spill or broken tool. Once proactive controls are enabled, trends in the data are monitored by the system and will only alert when a change needs to be made, before any quality spills or loss occurs. Imagine getting another 10 hours of life out of a tool because the tool tells you it has 10 more hours of life in it. Another area for major improvement will be to take the continuous real-time analytics from the floor and use them for comparison purposes to "digitally validate" plant production virtual simulations. The potential is limitless. The ability to harness artificial intelligence for proactive process control is already here. Once the right technology that truly enables plant floor intelligence is identified, all it takes is a change in a plant's cultures to fully leverage the efficiencies created by push analytics. The technology is important, of course, but people make the difference.
Don Manfredi is president of Trumble Inc., Farmington Hills, Mich.
- Events & Awards
- Magazine Archives
- Oil & Gas Engineering
- Salary Survey
- Digital Reports
- Survey Prize Winners
- CFE Edu
Annual Salary Survey
Before the calendar turned, 2016 already had the makings of a pivotal year for manufacturing, and for the world.
There were the big events for the year, including the United States as Partner Country at Hannover Messe in April and the 2016 International Manufacturing Technology Show in Chicago in September. There's also the matter of the U.S. presidential elections in November, which promise to shape policy in manufacturing for years to come.
But the year started with global economic turmoil, as a slowdown in Chinese manufacturing triggered a worldwide stock hiccup that sent values plummeting. The continued plunge in world oil prices has resulted in a slowdown in exploration and, by extension, the manufacture of exploration equipment.
Read more: 2015 Salary Survey