Role of AI and ML in Industry 4.0
The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using modern smart technology. The four design principles identified as integral to industry 4.0 are Interconnection, Information transparency, Technical assistance, and Decentralized decisions. Industry 4.0 is paving the path for digitization. Artificial intelligence (AI) and machine-learning-based systems are changing the ways we interact with information and computers. Artificial intelligence is a powerful technology to drive efficiency, improve quality, and better manage supply chains.
Many organizations are adapting to the Industry 4.0 implementation and preparing for a future where smart machines improve their business. Industry 4.0 describes the growing trend towards automation and data exchange in technology and processes. Few of the technologies are the internet of things (IoT), The industrial internet of things (IIoT), Cloud computing, Cognitive computing, and Artificial intelligence.
In recent years, there is a buzz around artificial intelligence (AI) and Machine Learning (ML). AI and ML technologies are mainly driven by big data, predictive analytics, and cloud computing. The reasons for most companies to shift towards Industry 4.0 and automate manufacturing include Increase productivity, minimize errors, optimize production costs, and improve efficiency.
Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans. AI is often used to describe machines or computers that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving. Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. AI enhances computing systems that can see, hear, learn, and reason, creating new opportunities to improve and achieve a more sustainable future.
The smart factory comprises of various machines communicating with one another, relying on AI automation platforms. They use AI and ML technologies to collect and analyse all types of data including images, standardized code text, and categorized fixed field text.
AI and ML impact on Industry 4.0 can be categorized into 5 main areas. They are,
Predictive Maintenance
Smart Manufacturing
Optimize Production
Improve Quality
Market Adaptation
Predictive Maintenance:
For faultless functionality, all major businesses rely on top-notch modern equipment and devices. Predictive maintenance is the need of the hour. Industry leaders choose Artificial Intelligence integration for Predictive Maintenance. Error history, Maintenance history, Machine condition, and data of the equipment should be considered before developing a predictive maintenance solution. Machine Learning technology has the capability to dynamically adjust to new data, analyse and understand the actions in real-time. Notification, Alarms, and alerts can also be thrown in real-time to alert staff of serious issues. The data is collected over a period. This data is used to analyse and monitor equipment status. This activity is undertaken to find patterns, predict failures, and prevent them well in advance.
Smart Manufacturing:
Smart manufacturing is a broad category of manufacturing that employs computer-integrated manufacturing, high levels of adaptability and rapid design changes, digital information technology, and more flexible technical workforce training. Machine learning technology implementation in production processes improves system performance and decision making. Deep learning provides advanced analytics tools for processing and analyzing big manufacturing data. AI and Big Data are making machines smarter than before. As a subfield of AI, Machine Learning is the primary driver of such innovations in the manufacturing sector. With the implementation of AI and ML techniques, new processes are obtained that can adapt production changes continuously. This helps industries to optimize different individual processes. The real-time implementation of these optimizations is known as Smart Manufacturing.
Optimize Production:
Artificial intelligence is used as one of the important and valuable tools in industries to increase efficiency and optimize production. Artificial Intelligence is used as an identification tool to find all the inefficiencies due to different mechanical failures and different asset malfunctions. The use of advanced analytics applied to data from the production floor enhances manufacturing performance. AI implementation in business/production/industries can function at an exceptional speed while reducing costs and enhancing customer experience. The combination of AI and ML technologies can predict failures and prevent machine downtime. Not only that, but the technology can also manage inventory by keeping track of stocks, forecast estimated delivery time, and work on delivering the highest quality products.
Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.
Improve Quality:
Historically, the quality of the product was evaluated only at the end of the production process. This practice was always challenging. With the help of AI and ML technologies and algorithms, an alert can be sent to manufacturers/unit heads even if there is a minor issue is detected. This helps the quality and performance to improve and avoid unwanted downtime. These algorithms also help manufacturers to improve the quality of machinery continuously. With the help of data received from sensors, continuous evaluation of the quality in each of the production phases can be done. Artificial Intelligence is used to reveal the hidden causes of many of the perennial production losses manufacturers face daily. The AI and ML technique is used, and the algorithm is trained to identify trends and patterns in the data.
Market Adaptation:
Adapting production to demand is a common concern in the industry. Artificial intelligence algorithms can be used to optimize the supply chain of manufacturing operations and to help them better respond to, and anticipate, changes in the market. To understand the market and its demand, an algorithm can be developed to identify demand patterns categorized by date, location, socioeconomic attributes, macroeconomic behaviour, political status, weather patterns, and more. Industries are using this valuable information to optimize inventory control, staffing, energy consumption, raw materials, and make better financial decisions regarding the company’s strategy.
