Why Is Artificial Intelligence the Need to the Hour in the Manufacturing Industry?
“Artificial intelligence isn’t the scary future. It’s the amazing present.”-David Gelernter Thanks to the ample number of subsites available in the market, modern customers are more ‘choosy’ than ever before. There is a rise in demand for products that are customized, personalized and unique over standardized products. Moreover, customers also expect the best products at […]
“Artificial intelligence isn’t the scary future. It’s the amazing present.”-David Gelernter
Thanks to the ample number of subsites available in the market, modern customers are more ‘choosy’ than ever before. There is a rise in demand for products that are customized, personalized and unique over standardized products. Moreover, customers also expect the best products at the lowest prices. However, poor demand forecasting and capacity planning, unexpected equipment failures and downtimes, supply chain bottlenecks, and inefficient or unsafe workplace processes are some of the critical hindrances in the manufacturing sector. To meet the changing demands and overcome these challenges, manufacturing companies must rely on automation, machine learning, computer vision, and other fields of artificial intelligence. Here is how artificial intelligence is going to transform manufacturing companies in the years to come :
Artificial intelligence in emerging markets
One of the main problems faced by manufacturing companies is high capital investments and thin profit margins, resulting in outsourcing of manufacturing activities to low-wage countries. However, rising standards of living in these countries and the need for modernization and automation makes the implementation of artificial intelligence a more feasible option. Although several workers in manufacturing companies will lose their jobs due to automation, companies can retrain those workers to perform higher-level design, programming, or maintenance tasks. The real driver, however, will be to develop applications for artificial intelligence that will not just automate tasks but will make entirely new business processes feasible, like the custom configuration of products to individual customer requirement.
In a manufacturing setup, several minute details are not visible to the human eye or often go unnoticed. Advanced technologies like machine learning and artificial intelligence help to find microscopic defects in products such as circuit boards at resolutions well beyond human vision. Also, the use of collaborative robots by manufacturing companies is becoming increasingly popular. These robots can work productively with human colleagues and can take instructions from humans including new instructions that are not anticipated in the robot’s original programming. Hence, better machine senses will result in a safer workplace in the long run.
Supply chain efficiency
Artificial intelligence is also expected to have a great deal of impact on areas of manufacturing that do not have any connection with robotics. The use of AI technology in the supply chain of manufacturing companies can forecast patterns of demand for products across time, geographic markets, and socioeconomic segments while accounting for macroeconomic cycles, political developments, and even weather patterns using different algorithms. Artificial intelligence is also highly beneficial in carrying out predictive maintenance for equipment, with sensors tracking operating conditions and performance of factory tooling, learning to predict breakdowns and malfunctions, and taking or recommending corrective actions.
Automated quality control
Artificial intelligence can aid in faster feedback loops, helping manufacturing companies to tackle unplanned downtimes, low yield (percent of units that pass quality control), and low productivity (time it takes to make a product). AI can help to speed up processes and ensure accuracy rather than relying on humans for in-process inspection and quality control which is time-consuming and also there are chances of failure to detect errors.