How is AI Revolutionizing Manufacturing?

Artificial Intelligence has changed the scenario for almost every industry. As technology has matured with big data and cloud computing, the cost has dropped, making AI more accessible to companies. It has also left its footprints in manufacturing.


The manufacturing industry is always eager to embrace new technologies that can improve its efficiency. AI can transform manufacturing in real-time by optimizing manufacturing processes, improving customer service and supply chain, thus, helping in making fast data-driven decisions and better performances. AI in production is at a point that computation is done at the machine level. Artificial intelligence space can grow to 52% by 2025, and 80% of manufacturing companies will see its positive effects, leading to an increase in revenue and reduced costs by 20 percent.


Manufacturing has majorly transformed in style, technology, scale, and efficiency, but there are still bottlenecks such as reluctance of sensitive data sharing and its integration. An intelligent system is needed to streamline operations, reduce downtime, grow consumer satisfaction, and enhance production efficiency. Can an AI developer provide it? Let’s analyze.


Learning of the Blog


  • Predictive Maintenance
  • Accuracy
  • Designing
  • Supply Chain Management
  • Product Optimization
  • Sensory Perception
  • Cost Reduction
  • Early Accessors
  • Conclusion


If you are fascinated by the advantages of AI, we recommend going for an artificial intelligence course for beginners before looking at the manufacturing industry. In this blog, we will deliberate in detail how artificial insights can streamline the manufacturing process with some examples.

1.  Predictive Maintenance

Predictive maintenance can allow companies to detect when machines need support instead of guessing or performing preventive maintenance. For this, data from various sources like sensor data from devices, maintenance records, and weather data is needed. Research by McKinsey & Co. suggests that AI-equipped predictive maintenance of industrial tools generates a 10 percent reduced annual maintenance cost. The use of computer vision and sensor data has caused a 20 percent downtime reduction and one fourth in the inspection.

2.  Accuracy 


To maintain high levels of quality, real-time analysis, and detection of faults are required, which is not possible by human efforts. The prediction of equipment failure in real-time has been possible by AI. With the availability of data and subtle imagery, quality insurance is possible at each process step. This has also helped financially as there is a chance of a 10 percent decrease in warranty liabilities.

3.  Designing


AI can prove successful in coming up with out-of-the-box designs when provided machine constraints, cost, and functional requirements. In the future, the technology can also figure how to build the model using both additive and subtractive methods keeping imperfections and manufacturing choices in mind. This utilizes machine learning techniques. The process is similar to a natural selection of designs and has niche applications in automation, architecture, and aerospace.

4.  Supply Chain Management


AI can provide valuable business insights if operational data is applied to advanced analytics. Big data helps in smarter supply chains as the algorithms formulate estimations of market demand based on linking location and weather patterns, environmental changes, political status, consumer behavior, and more. The AI’s ability to analyze trends and make predictions can help industrialists forecast the demand even before it’s time to create the products. With the help of sentiment analysis, the data over social media can be analyzed to predict product demand, and these platforms can also sway political sentiments.

5.  Product Optimization


For maximum efficiency, parameter optimization is necessary. The task is impossible in case of a vast number of process parameters. The algorithms of Machine learning, a branch of AI, analyses vast quantities of historical data to form complex relations such as regression between parameters and production. The optimum production rates can be achieved by determining and adjusting the essential control variables. This is a real-time data streaming algorithm.

6.  Sensory Perception


AI can identify patterns in the images with the help of Machine Vision. The equipped cameras make sense of perception a reality. This technology is applied in factories in the form of self-driving forklifts and conveyors. With this, machines can avoid obstacles and prevent accidents. Manufacturers can also pass instructions in ordinary language to the sensors without the help of an artificial intelligence expert.

7. Cost Reduction


Cost reduction is the goal of every company but not at the expense of the quality of products. This makes purchasing the right amount of inventory a constant struggle for manufacturers. If the demand is more and resources are less, it’s a loss on revenue. Also, if demand isn’t generated, then purchasing too much leads to overspending. Overspending is a more common phenomenon. There is always a fluctuation of public sentiment, which makes it nearly impossible to predict market demand accurately. As mentioned above, artificial intelligence has proven its worth by anticipated market demand using a range of tools and market analysis. Thus, manufacturers don’t have to rely on instinct. This leads to cutting down overspending and an increase in revenue.


8. Early Accessors


A recent study by PricewaterhouseCoopers says that 78% of manufacturing organizations have deployed or are planning to use predictive maintenance, manufacturing execution systems, digital twins, and automated robotic processes. Here is a list of the first users of AI in the industry:


  • General Electric is one of the earliest accessors of industrial transformation. They explored the AI domain to prevent the decline of productivity in its sector.


  • Florida-based Jabil is a Fortune 500 company that is performing contract manufacturing for major global brands. It began to use artificial intelligence to spot manufacturing defects and for predictive maintenance.


  • Lennox International- a Dallas based HVAC systems maker, is a manufacturer of image recognition to spot manufacturing problems.


  • Facebook and Google’s server farms use machine learning to predict malfunctioning in ‘blades.’ This tool also generates highly personalized email alerts for consumers.



The companies in the manufacturing sector have a lot on their plate, and using the examples mentioned above, they can think about supercharging their businesses using AI. Artificial intelligence can immensely benefit businesses of all sizes. Productivity enhancement and cost reductions are two crucial aspects that can be achieved with the right and timely approach. There is a high demand, but the shortage of a Certified Artificial Intelligence Developer in the industry and companies are now resorting to providing artificial intelligence training to their expert staff.