Artificial Intelligence and the Fourth Industrial Revolution

 There are many factors that spike up the production costs of a company. In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation especially in this fourth industrial revolution phase. Manufacturing companies are finding it increasingly harder to maintain high levels of quality during the industrial revolution. Bringing out the best product takes time as well as large human resources. But all that is set to change.

Introducing – The Fourth Industrial Revolution

The First Industrial Revolution used water and steam power to mechanize production. The Second Industrial Revolution used electric power to create mass production. The Third Industrial Revolution used electronics and information technology to automate production. Now the Fourth Industrial Revolution is building on the Third and has had a massive impact on the manufacturing sector. The fourth industrial revolution, powered by technology is remolding the industrial sector, helping businesses achieve more profits and more efficiency. The sector is entering its next phase – Industry 4.0 – which is driven by automation, AI, and Internet of things, and cloud computing. The big players are already investing millions in computer intelligence so that they can save time, money, and resources while maximizing their production. The Manufacturer’s Annual Manufacturing Report 2018 found that 92% of senior manufacturing executives believe that ‘Smart Factory’ digital technologies – including Artificial Intelligence – will enable them to increase their productivity levels and empower staff to work smarter.

How is the Manufacturing Sector using Artificial Intelligence?

Through computer vision, machines can be powered to pay attention to the tiniest of details, far beyond a man’s potential. Landing.ai, a startup formed by Silicon Valley veteran Andrew Ng, has developed machine-vision tools to find microscopic defects in products such as circuit boards, using a machine-learning algorithm trained on remarkably small volumes of sample images. If it spots a problem or defect, it sends an immediate alert, an artificial intelligence process known as “automated issue identification.”

Artificial intelligence can also be used to monitor the whole process of manufacturing. Siemens, one of the leading manufacturing companies on the planet, did just that. They embarked on a digitalization strategy of which one of the major goals was Overall Equipment Efficiency. In late 2017, the company announced the latest version of its IoT operating system, MindSphere. Physical machines can be connected to Mindsphere cloud environment, enabling it to build the application that visualizes the various metrics that plant managers need to monitor in 2018. It also gives the resources needed to build an industrial Internet of Things system in a fraction of the time it would take to set up a physical environment.

General Electrics is yet another leader in the manufacturing sector that has adopted the artificial intelligence strategy. In 2015 GE launched its Brilliant Manufacturing Suite for customers, which it had been field testing in its own factories. The system takes a holistic approach of tracking and processing everything in the manufacturing process to find possible issues before they emerge and to detect inefficiencies. GE claims it improved equipment effectiveness at this facility by 18 percent.

It is powered by Predix, their industrial internet of things platform. In the manufacturing space, Predix can use sensors to automatically capture every step of the process and monitor each piece of complex equipment. You can view a short video of how its done here.

Another application of artificial intelligence is the use of generative design. Designers or engineers input design goals into generative design software, along with parameters such as materials, manufacturing methods, and cost constraints. Software explores all the possible permutations of a solution, quickly generating design alternatives. It tests and learns from each iteration what works and what doesn’t. From the many solutions that are put forward, the designers or engineers filter and select the outcomes that best meet their needs. This can lead to major reductions in cost, development time, material consumption, and product weight. Airplane manufacturer Airbus used generative design to reimagine an interior partition for its A320 aircraft and came up with an intricate design that ultimately shaved off 45 percent (30kg) of the weight off the part.

Such applications will affect the future of work. These will bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed. It all comes down to optimal manufacturing performance. If manufacturers do not invest in the long term, ignore the fusion of technologies then their profits would be affected, as prices of products as well as the raw materials would only go up. However, it is not late yet as the field of artificial intelligence is constantly evolving and these applications will be the future of artificial intelligence.