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AI can be implemented in many applications. As AI technology continues to evolve, we can expect to see more innovative use cases of AI in smart manufacturing. However, below are some of the important use cases in the ecosystem of AI in Smart Manufacturing:
· Predictive Maintenance: AI can analyze real-time data from machines and equipment to predict when maintenance is required, which helps in reducing downtime, increasing machine lifespan, and improving overall efficiency.
· Quality Control: AI can analyze images and videos of products to detect defects in real-time and helps the operator catch quality issues before they become costly problems.
· Supply Chain Optimization: AI can analyze data on demand & supply, and pricing to optimize supply chain management which helps in increasing overall production efficiency.
· Autonomous Robots: AI-powered robots can perform repetitive or dangerous tasks with precision and speed. This frees up human workers for more complex and value-added tasks.
· Predictive Analytics: AI can analyze large amounts of data from various sources, such as sensors, to predict trends and patterns in production and helps operator make informed decisions.
In the future, we may see artificial intelligence being more integrated into different new technology/equipment. As the development of AI technology is still in process, we may see more integration of technology with AI. Brief details of these technologies have been mentioned below:
· Augmented Reality (AR) And Virtual Reality (VR): AR and VR technologies can be used to create virtual models of manufacturing facilities and production lines, which can help to optimize the layout and improve efficiency. AR and VR can also be used for training, allowing workers to practice tasks in a simulated environment before working with equipment.
· Collaborative Robots: Cobots are equipped with sensors and AI algorithms that allow them to work safely and efficiently alongside humans, increasing productivity and reducing the risk of workplace injuries.
· 3D Printing: AI can be used to optimize the printing process and improve the quality of printed parts. AI algorithms can be used to analyse data from the printing process and make real-time adjustments to improve the quality of the final product.
· Digital Twins: Digital twins are virtual models of physical objects which can be used to simulate different scenarios and test changes to the manufacturing process in the real world. AI algorithms can be used to analyse data from the digital twin and provide insights that can be used to optimize the manufacturing process.
· Edge Computing: Edge computing involves processing data on devices located near the source of the data rather than in a central location. AI algorithms can be used to analyse
data in real-time at the edge, allowing manufacturers to respond to changes in the manufacturing process more quickly.
While AI has the potential to revolutionize smart manufacturing, this also comes with several risks and challenges associated with its implementation. Below are some of the few potential risks and challenges:
· Data Privacy and Security: As AI relies on vast amounts of data, there is a risk that this data could be compromised, either through cyberattacks or data breaches. This could lead to sensitive data being leaked or stolen, potentially causing damage to a company’s reputation and financial losses.
· Dependence on Technology: As AI becomes more prevalent in smart manufacturing, there is a risk that companies become overly dependent on technology. This could lead to a loss of critical skills among workers and an overreliance on automated processes, potentially reducing quality and safety standards.
· Cost: Implementing AI technology in smart manufacturing can be expensive, particularly for smaller companies with limited resources. There is a risk that companies may invest heavily in AI without seeing a sufficient return on investment or that the cost of implementation may be prohibitive for some companies.
· Workforce Changes: The implementation of AI in smart manufacturing is likely to lead to changes in the workforce, with some jobs being eliminated and others being created. This could lead to job displacement and a need for retraining and upskilling.
The end user is using AI to optimize their manufacturing processes and improve efficiency, quality, and productivity. By implementing AI technologies, these end users can reduce downtime, improve the accuracy of their processes, and ultimately improve the quality of their products, which can lead to increased customer satisfaction and improved business outcomes.
· Siemens: Siemens is using AI to improve the efficiency of its manufacturing processes, including predictive maintenance to minimize downtime and machine learning to optimize the use of resources.
· GE Aviation: GE Aviation is a subsidiary of General Electric that produces jet engines for commercial and military aircraft and is using AI to improve the quality of its products by analysing data from its manufacturing processes to identify and correct issues before they become critical.
· BMW: BMW is using AI to optimize its manufacturing processes, including predictive maintenance to reduce downtime and machine learning to improve the efficiency of its production lines.
· Bosch: Bosch is using AI to optimize its manufacturing processes, including predictive maintenance to reduce downtime and machine learning to improve the efficiency of its production lines.
· Jabil: Jabil is a global manufacturing services company that has implemented AI technology in its factories. By using machine learning algorithms to analyse production data, Jabil was able to identify inefficiencies and helps in reducing cycle time.
· PepsiCo: PepsiCo is using machine learning algorithms to analyse data from sensors on production lines; PepsiCo was able to identify equipment that was likely to fail and proactively perform maintenance.
· Rolls-Royce: Rolls-Royce is using machine learning algorithms to analyse data from sensors on its engines; Rolls-Royce was able to predict maintenance needs and schedule maintenance in advance.
Some important major players working towards implementing AI in the manufacturing ecosystem include Siemens, IBM, GE, Intel, Microsoft, Schneider Electric, Honeywell, Rockwell Automation, and NVIDIA. These companies are just a few examples of the many organizations that are working on AI for smart manufacturing. As technology continues to evolve, we expect to see even more companies entering the space that can develop innovative AI solutions for the manufacturing industry. Not only this, but there are also many startup players who have developed innovative products related to AI which can be implemented in the manufacturing ecosystem. These startups are just a few examples of the many organizations that are working on AI for smart manufacturing. This may include Augury, Falkonry, Fero Labs, KONUX, MachineMetrics, Neurala, Precognize, , and Sight Machine.
Further, if we look at the recent AI development, many tools have been developed similar to ChatGPT, which is developed by OpenAI and Microsoft; and Bard, developed by Google. If this tool is implemented in the smart manufacturing ecosystems, it will help in several ways, such as quality control, predictive maintenance, production planning, and training & education. Development of such tools in the future is expected, especially for manufacturing ecosystems, which helps increase manufacturing companies’ production.
We at MarketsandMarkets have developed a 360 understanding of artificial intelligence and its related technology. Below are a few of the associated studies: