How to Apply AI to Manufacturing Systems
Jun 06, 2019 Comments (0)
AI adoption in the manufacturing world is inevitable. It’s not a matter of if the technology is coming, but when.
It allows for incredible levels of productivity and efficiency and much more nuanced automation processes. It makes economic sense too, creating more effective yet lower-cost operations in a variety of fields.
It shows so much promise that 92% of senior manufacturing executives believe “Smart Factory” digital technologies will enable increased productivity and an empowered workforce. Artificial Intelligence plays a big part of that.
What some may not realize is that it’s already here.
AI In Manufacturing Today
AI already sees widespread adoption in countries such as Japan, China and India. Major brands like GE, Bosch, Panasonic and Mitsubishi Electric already apply the technology to their operations to improve production and supply processes.
Panasonic’s “Technopark” in Jhajjar, located in the state of Haryana, is deeply ingrained with AI which is used to automate manufacturing operations. The facility handles the development of air conditioners, washing machines and other appliances.
Mitsubishi Electric India has developed a behavioral-analysis AI that can be used to augment the modern production line. More specifically, it can analyze human behavior such as an assembly-line worker’s motions and actions.
The question, however, is what exactly are these applications doing? How do they improve productivity and what kind of competitive advantage do they enable?
Production Quality and Performance
Defects, problematic equipment and human error can all contribute to massive problems in the manufacturing sector. For industrial manufacturers, unplanned downtime costs approximately $50 billion annually. Every time the production line incurs a mistake, not only are the goods in question effectively ruined, but there’s also downtime needed to assess and fix any errors. Both of these scenarios eat into the revenue of a manufacturing organization, eroding profits.
AI can help eliminate these things from happening, in several ways.
Preventative maintenance for industrial equipment and machinery can be handled by AI controllers. Identifying performance issues early matters, so they can be taken care of before a breakdown or major malfunction. It results in several benefits such as safer work environments, more reliable equipment, reduced operating costs and improved product quality.
Real-time analysis of performance data can help quantify and recognize problems in the production line. In addition, the output can be measured and assessed to ensure goods develop at the right quality levels.
The beauty of these AI applications is that they can work autonomously, pouring over data flowing in and delivering alerts and notifications to the necessary personnel. The preventative maintenance system, for example, could send an alert to service crews that a tool or machine needs cleaning and repairs.
Increased performance and efficiency provide a competitive advantage already, but AI can also be used to get more of an edge. Flexible automation and streamlined quality control, as well as accurate forecasting and demand-driven production, all contribute to a better position for manufacturers in regards to market trends.
A major part of producing goods — old or new — is assessing the current market and adjusting for consumer and client demands. Producing an excess that goes nowhere is a waste, and will eat away at profits as much as any other problem. AI can help create a more accurate and timely system that reacts to market trends appropriately. When demand goes up so does production, and when it falls, production slows. The reduction of supply chain forecasting errors by as much as 50%, and lost sales by as much as 65%, all with better product availability possible thanks to machine learning.
AI applications can also help with materials acquisition, production volumes, delivery dates, facility and storage utilization, and more. By managing all these disparate systems collectively and comparing the results, AI platforms can help build a remarkably efficient operation, powered by intelligence and real-time insights.
Data at the Core
For advanced analytics and AI technologies to work properly, they need a steady flow of data — comprised of performance, behavioral and environmental insights. It’s essentially the same information that a human analyst or manager would use, except AI can pour over the data faster and better. Machine learning is a subset of this technology that can handle massive loads of data at a time.
The road to AI is about implementing more data-oriented processes and systems that can feed the kind of information needed into a remote analysis platform. So, before AI takes center stage in manufacturing, these data solutions need to be put in place. If you haven’t already, that’s where you should start within your facilities and operations.
Megan Nichols is a technical writer and blogger. She writes about engineering, science and technology topics. Megan is also editor of Schooled By Science, an easy to understand science blog. With Schooled By Science she hopes to encourage others to learn more about STEM subjects.