Ways AI can build up the manufacturing sector in 2021
- Manufacturers that fail to recognize the importance of AI are likely to lose their competitive edge
- While the current adoption rate in manufacturing is low, the prevalence of AI is expected to increase significantly by 2030
A glimpse into the future of manufacturing can be found at FANUC’s plant in Oshino, Japan. There, at one of the largest manufacturers of industrial robots in the world, the robots build, inspect and test themselves. It is in fact the world’s first factory complex with robots that create digitized “offspring” capable of machine learning and operates on a 24-hour basis. It demonstrates just how far the use of artificial intelligence (AI) in manufacturing processes has come.
While FANUC exemplifies an AI-led future, many manufacturers today still struggle to deploy the technology at scale. In fact, a 2018 PwC Global Digital Operations study of 1,155 manufacturing executives across 26 countries showed that only 9% have implemented AI in their processes to improve operational decision-making. Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery.
That said, leveraging on AI and other technologies like machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their AI transformation. A recent report by the World Economic Forum quoted Instrumental CEO and founder Anna-Katrina Shedletsky who reckons by 2025, ubiquitous streams of data and intelligent algorithms will enable manufacturing to continuously optimize towards higher levels of output and product quality – reducing overall waste in manufacturing by as much as 50%. As a result, we will enjoy higher quality products, produced faster, at a lower cost to our pocketbooks and the environment.
Industrial internet of things (IIoT) connects all IoT-enabled devices to the factory floor, integrating manufacturing processes with big data and making them programmable via a logic controller. Increased use of precision sensory equipment means that information can be generated, recorded, and analyzed for all aspects of the production process, covering anything from temperature to item picking and packaging.
Programmable logic controllers with AI capacity for deep learning can then respond automatically to the seamlessly generated information, and make alterations to the minutest function without recourse to human intervention. The big data analytics processed by AI can substantially improve performance across the entire production process and can be operated remotely.
24/7 production and safer environment
For any production facility to continue working round the clock, humans are required to work in shifts but with the help of AI, the production line can function 24/7. This allows the expansion of production capabilities, which is increasingly necessary to meet the demands of worldwide customers. Furthermore, robots are more efficient in many areas such as the assembly line and picking and packing departments. They can greatly reduce turn-round times in many areas of the business operation.
Errors and accidents do occur on the factory floor and in any construction or processing environment – a tendency that can be all but eradicated by AI and robotic assistance. Remote access control means a reduction in human resources, especially when the work is dangerous or requires superhuman effort. Even regular working environments will cut down on the incidence of industrial accidents and lead to an overall improvement in safety. In addition, more advanced sensory equipment integrated with IIoT devices makes the installation of safety guards and barriers a simpler and more effective measure to protect human lives.
Lower operational costs
Many companies view the introduction of AI into the manufacturing industry with trepidation, as it requires a huge capital investment but what they fail to realize is the ROI is significant and increases with time. Once intelligent machines begin to take over the daily activities of a factory floor, businesses will benefit through considerably reduced operating costs, with predictive maintenance helping additionally to reduce machine downtime.
Quality control and greater efficiency
IIoT enables the collection of vast amounts of data and advanced analytics which can be used to gain insights into consumer behavior. AI has the capacity with machine learning to anticipate information, refine processes, and track incongruities, all the way down the supply chain from source to finished product. AI is also extremely useful for carrying out predictive maintenance on machinery and equipment.
Using sensors to track performance and operating conditions, machines can learn to predict malfunctions and failures, and take action to remedy them before they occur. This can result in faster feedback, helping companies to eradicate unplanned downtimes. Sensors can even also detect the mot microscopic defects, scanning them at resolutions far beyond the capacity of human vision, thus improving productivity and increasing the percentage of items that will pass quality control.