There was a time when Artificial Intelligence was considered futuristic. We are using so much AI in our daily lives like navigation systems in cars, fitness apps, Alexa and Siri, Amazon, Netflix, weather forecasting, and high-speed stock trading are among current must-have AI applications.
AI known is the significant propellant of the Fourth Industrial Revolution and expected to wipe out nearly half of the human jobs (mostly the white-collar jobs) in the next 20 years. Most of the industries will opt to replace humans for work that can be performed with the help of AI. Algorithms and automation are a vital threat as they offer improved efficiency at a lower price.
AI in the manufacturing market is expected to be valued at USD 1.1 Billion in 2020 and is likely to reach USD 16.7 Billion by 2026 at a CAGR of 57.2% during the forecast period. The emergence of Industry 4.0 across the globe and growing big data technology in manufacturing are the primary factors creating an affluent platform for the adoption of AI in manufacturing.
Why is AI important in the manufacturing industry?
Implementing AI is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.
This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. The manufacturing is full of analytical data, which is easy for machines to analyze. There are hundreds of variables that impact the production process while these are very hard to analyze for humans, Machine-learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.
There’s no uncertainty that the manufacturing sector is leading the way in the application of artificial intelligence technology. From significant cuts in unplanned downtime to better-designed products, manufacturers are applying AI-powered analytics to data to improve productivity, product value and the safety of employees. Here is how:
Industry 4.0 and smart maintenance
In manufacturing, ongoing maintenance of production line machinery and equipment represents a greater expense, having a crucial impact on the bottom line of any asset-reliant production operation. Moreover, studies show that unplanned downtime costs manufacturers an estimated $50 billion yearly and that asset failure is the cause of 42 per cent of this unplanned downtime. For this reason, predictive maintenance has become a must-have solution for manufacturers who have much to gain from being able to predict the next failure of a part, machine or system.
Predictive maintenance uses advanced AI algorithms in the form of machine learning and artificial neural networks to formulate predictions regarding asset malfunction. It allows for drastic reductions in costly unplanned downtime, as well as for extending the Remaining Useful Life (RUL) of production machines and equipment.
The rise of quality 4.0
Due to very short time-to-market deadlines and a rise in the complexity of products, manufacturing companies are finding it increasingly harder to maintain high levels of quality and to comply with quality regulations and standards. On the other hand, customers have come to expect faultless products, pushing manufacturers to up their quality game while understanding the damage that high defect rates and product recalls can do to a company and its brand.
Quality 4.0 involves the use of AI algorithms to notify manufacturing teams of emerging production faults that are likely to cause product quality issues. These faults can include deviations from recipes, subtle abnormalities in machine behaviour, change in raw materials, and more. By tending to these issues early on, a high level of quality can be maintained.
Additionally, Quality 4.0 enables manufacturers to collect data about the use and performance of their products in the field. This information can be robust to product development teams in making both strategic and tactical engineering decisions.
Human-robot collaboration
Automation and robotics, in particular, are an integral part of digitalization efforts. More specifically, collaborative robots, or cobots, enable Human Robot Collaboration, which is one of the 26 levers of Industry 4.0 identified by McKinsey India & Company. It is a core tenet of enterprises in their march towards the Internet of Things.
In India, most major manufacturers across industries are already using cobots. In the automotive industry, Bajaj Auto has over 150 cobots deployed across their assembly lines for applications such as bolt-tightening and machine tending. Mahindra & Mahindra is another avid cobot user in the automotive sector. In the FMCG sector, multinational players such as L’Oréal have completely eradicated ergonomic risks by using cobots for automated palletizing. Indian SMEs and even MSMEs across industries have also been able to adopt cobots for various applications, including packaging, spray-painting, and quality inspection.
Making better products with generative design
Artificial intelligence is also changing the way we design products. One method is to enter a detailed brief defined by designers and engineers as input into an AI algorithm.
The brief can include data describing restrictions and various parameters such as material types, available production methods, budget limitations and time constraints. The algorithm explores every possible configuration, before homing in on a set of the best solutions.
Adapting to an ever-changing market
Artificial intelligence is a core element of the Industry 4.0 revolution and is not limited to use cases from the production floor. The AI algorithms can be used to optimize manufacturing supply chains, helping companies anticipate market changes. This gives management an enormous advantage, moving from a reactionary/response mind set to a strategic one.
Industrial AI will continue to transform the manufacturing sector
The manufacturing sector is a perfect fit for the application of artificial intelligence. Even though the Industry 4.0 revolution is still in its early stages, we are already witnessing significant benefits from AI. From the design process and production floor to the supply chain and administration, AI is destined to change the way we manufacture products and process materials forever.
How will AI have an impact on the Manufacturing Industry?
The manufacturing industry has always been open to adopting new technologies. Drones and industrial robots have been a part of the manufacturing industry since the 1960s. The next automation revolution is just around the corner. With the adoption of AI, if companies can keep inventories lean and reduce the cost, there is a high likelihood that the Manufacturing Industry across India will experience encouraging growth. Meanwhile, the manufacturing sector has to gear up for networked factories where supply chain, design team, production line, and quality control are highly integrated into an intelligent engine that provides actionable insights. Below are the results of AI power:
Virtual Reality: Virtual Reality will enable new tools that help to perform testing in the virtual world. It allows people, remotely located, to connect and jointly work on situations that require troubleshooting. Simulation and product-creation can help reduce manufacturing time drastically.
Automation: Automation will help the manufacturing industry reach a high level of accuracy and productivity, a level that is even beyond human ability. It can even work in environments that are otherwise dangerous, tedious or complicated for humans. Robotics as a service is expected in the future to have capabilities like voice and image recognition that can be used to re-create complex human tasks.
Internet of Things (IoT): We all have started to use smart sensors. It is a little known fact that IoT functionality will have a pivotal role in the manufacturing industry. It can track, analyse production quotas, and aggregate control rooms, the technology can help to create models for predictive maintenance. When combined with augmented and virtual reality and analysis of customer feedback, there can be several meaningful insights to help towards innovation.
Robotics: With the promise of increased output, robots are already being used in the manufacturing companies. However, with their growing intelligence, robots will soon replace the workforce in the factories. Every stage can be closely monitored with the help of sensors; data can be shared with AI and analytics software. With an increase in output, defect detection and corrective action are much faster, and the entire production cycle is much more efficient.
Top 10 uses cases of AI in the manufacturing industry
Quality Checks: Some internal defects in manufacturing equipment’s cannot be found that much easily with eyes. Even experience professionals were also some time unable to detect the flaws in products. Thanks to artificial intelligence and machine learning technologies. They can detect smallest flaws in machinery. AI-powered inspection tools offers fully automated flaw detection processes. The intelligent device flaw detection tools in manufacturing monitors the equipment performance and its quality. Microscopic faults will also be identified using AI tools in manufacturing.
Predicts Equipment Failure: Manufacturers face challenges with machinery/products failures in many ways. A product might look perfect from outside, but it may damage once we use it. With the availability of vast data on how the products are tested and how they function, artificial intelligence-based tools and machines identifies the specific areas that need to be tested efficiently.
Equipment Predictive Maintenance: Predictive maintenance of devices allows manufacturer to avoid device damage overheads. Using ML-powered predictive analytical solutions, you can predict when machinery require maintenance services. Machine Learning is one of the out most technology that can prevent unplanned downtime.
Not only analytics solutions, cloud and the Internet-of-Things (IoT) sensors are also playing a vital role in modernizing manufacturing industry. They embedded in machinery to better predict the maintenance and thus overcomes equipment issues that has to be occur in the future.
Digital Twins: AI helps to completely visualize the infrastructure, products or services. The process of virtual representation of a manufacturing unit is called a digital twin. Using data gathering tools like sensors and cameras, the physical representation of manufacturing environment will be completely visualized.
To make sure that digital twins are working properly, you should integrate all smart components like sensors that are collecting data from equipment’s. Using a cloud connection, the data generated by smart components will be collected, stored and processed. As AI-based systems needs vast amount of data, further, AI systems retrieves data from cloud and makes it workable for the company.
Supply-Chain Management:
The technology is gaining momentum across supply chain management operations. Machine learning, natural language processing, computer vision, robotics and speech recognition are making supply chain management tasks smarter. AI has multiple applications in supply chain management. They include: Establishes a strong communication channel among departments, Warehouse management & logistics, Development of autonomous vehicles for logistics.
Forecast Product Demand: Artificial intelligence systems using predictive analytics can also forecast the product demand efficiently. AI tools for manufacturing collects data from various sources and based on it, they can accurately forecast the product demand.
Inventory Management: Artificial intelligence app in manufacturing allows you to manage order records and delete/add new inventories. Here, we should talk about machine learning technology. It was one of the most significant technology that is used for managing supply, demand, and inventories.
Price Forecasts: Using historical data of product prices, and analysing pricing structure of various competitor’s product prices, machine-learning algorithms can forecast the price of a product. Competitive prices are always offers more profits to the companies.
Robotics in Manufacturing: We are all well aware of use of robots in manufacturing processes. It is a fact that machines can perform more efficiently than humans. However, any way machine are very much faster than humans in doing tasks. AI-powered robots for manufacturing performs repetitive tasks without being programmed. This is one of the best application of AI and ML for manufacturers.
Customer Management: AI applications for manufacturing, customers help in increasing sales, productivity, and business performance through managing their customers smartly. Yes, with the use of smart AI apps for manufacturing, service providers can quickly understand the customer issues and resolve them, and also personalize their experience.
Is AI the future of manufacturing?: Artificial intelligence will be the future of manufacturing industry. Not only manufacturing, it is a game-changer for all industries. AI technology is now more accessible for all businesses. Driven by increased product demand, manufacturing industry will always open up to adopt new technologies like AI, ML etc. Process optimization, low cost overheads, high productivity, quick decision-making, and improved customer services, everything will be obtained using AI in manufacturing.
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