Artificial Intelligence And Mobility, Take advantage of mobile technology to make traffic data even more meaningful.
Engineers and technicians in today’s state-of-the-art manufacturing plants have a wide range of commonly available tools. In most cases, these experts cannot complain about the lack of available data, but in virtually all cases, we can talk about the absence of another key source: time.
Large volumes of data are available in modern, interconnected factories, and thanks to this data, today’s technicians are able to keep their processes in a stable state and under control, but we have the time costs associated with managing this data. It adds up to the time it takes to figure out which database to extract the right data from, the time it takes to get the most important information from that data, the time spent downloading various records and logs, and the time we analyze it all.
The amount of time spent continues to grow if the situation becomes even more complicated and technicians have to deal with something like regression analysis. The issue of time is very important, especially if, due to process problems or production interruptions, the technician has to start reviewing the data and analyzing the situation.
Every minute spent checking and analyzing data could mean one minute of lost production, which equals lost profit. Some companies even follow a metric of non-value-added activities, which is the amount of time spent working when production is stopped and not produced.
One could argue that all the time devoted to reviewing data and analyzing process data can fall directly into the costs of the non-value-added activities sector. However, how can a technician without relevant data know where to start fixing a production interruption and shutdown problem?
The answer to these questions is the wider use of artificial intelligence and mobile technologies to monitor and review data, complete real-time analysis, and “push” the right solution to the appropriate technician. The use of tools that enable such groundbreaking analysis contributes to smarter manufacturing operations, which shortens the time to find the root cause of the problem, reduces non-value-added activities, and ultimately reduces losses.
Artificial intelligence and penetrating form of analysis
As interconnected factories connect to the Industrial Internet of Things (IIoT), the use of technology to eliminate the time required for traditional data acquisition and analysis is becoming possible and affordable. Through artificial intelligence and a “breakthrough” form of analysis, it is possible to automate data review, analysis, graphing, and send information directly to the right person who is able to solve the problem.
But there is another step that is preparing a whole new ground: It is about doing everything before production problems occur, i.e., transforming the company’s culture from reactive to proactive.
Encouraging alerts and automation of reporting sent to technicians is nothing new in technical practice. The technology for this task began to be used in 1990 and is still used today. Improvements in other technologies enable continuous real-time analysis supported by artificial intelligence to make it more affordable and accessible to businesses.
Lower storage fees, expanded machine connectivity, and high-speed computing are also catalysts for this change in production. Now the software is not only able to send alerts and graphs but can immediately provide solutions via e-mails, text messages, and even mobile applications on smartphones, allowing technicians to spend more time correcting problems or dealing with them even better with prevention.
Providing an intelligent automated IT platform that delivers solutions based on ongoing automated analysis of each process in the company will help us anticipate potential problems within the process and guide company employees to the source of the problem before production losses occur.
Existing process data is collected, analyzed, and monitored during the production line duty cycle, and the information is sent to the right user at the right time, i.e., before production losses occur, not just “with a cross after funds.”
Emerging software technology combines data management with artificial intelligence and aims to implement a more proactive approach to process management. Software algorithms have been developed to introduce technicians to emerging trends in-process data, which could indicate potential production losses. This creates a change in the cultural environment within the company, where there is a clear effort to prevent failures at a controllable and manageable pace and to eliminate frenetic and unpredictable reactions to production losses.
Challenges to be faced in proactive process management
Wider use of enterprise intelligence tools, including proactive management, presents certain challenges. One of the first issues to be addressed is to teach the company’s employees that they will not respond to traditional starters, such as flashing red lights, stopped conveyors, and a growing number of technicians struggling to fail.
Another example of a new approach in the management of operating personnel who must learn to repair even a small failure before a major failure occurs. Alerts based on monitoring deviations from “healthy” data trends force technicians to resolve an impending problem before a more serious failure occurs, saving time and money. Again, traditional starters are missing in this scenario because the business continues to run as originally designed.
Once the company’s employees adopt this new way of managing the process, they can move on and participate in the creation of a preventive maintenance concept driven by real process data. For example, preventive maintenance concepts based on process data and part quality monitoring can save millions of dollars a year in the machining sector.
Expensive cutters are often changed at regular intervals after a significant loss of quality or when the tool is damaged. Once the proactive controls are made available, the system monitors trends in the data, and a warning signal only appears when changes need to be made, i.e., before any deterioration occurs. Imagine that you get another 10 hours of tool life because the tool itself will tell you that it has another 10 hours of life ahead of it.
Another room for major improvement is the possibility of taking an ongoing real-time analysis from the workshop and using it for comparison to “digitally verify” a virtual simulation of enterprise production. The potential for such a thing is unlimited.
The opportunity to use artificial intelligence for proactive process control is already here. Once the right technology is identified to increase intelligence in manufacturing operations truly, all that needs to be done is to change the corporate culture so that the efficiencies created by the “penetrating” form of analysis that comes with the stimuli can be fully exploited.
Of course, technology is essential, but it depends mainly on people and their approach to the issue.