Automation & Beyond: The AI Revolution Reshaping Manufacturing

Over a decade before the United States of America declared its independence, a curious machine called the steam engine was beginning to change the world as we know it. This device was first used in England and slowly moved to other European nations, the newly formed USA, and later other parts of the world. The steam engine allowed machine power to replace human and animal power and was the start of the Industrial Revolution. Artificial Intelligence (AI), Machine Learning (ML), and the Industrial Internet of Things (IIoT) are bringing us into the fourth industrial revolution or “Industry 4.0”.

The Role of AI in Manufacturing

AI and ML are changing manufacturing using data generated from sensors, devices, and other sources. This technology will transform manufacturing on a much larger scale than the assembly line. As familiar technologies and processes are made obsolete, AI will be the brains of the operating system.

The Rise of AI in Manufacturing

According to a survey by Deloitte on AI adoption in manufacturing, 93 percent of companies believe AI will be a pivotal technology to drive growth in the sector. The market size of AI in the manufacturing sector is expected to exceed $2 billion by 2025, indicating a 40 percent average annual growth from 2019. A survey by McKinsey indicated that 64 percent of respondents in the manufacturing sector saw a decrease in costs as a result of AI adoption. Global robotics sales for industrial robots that leverage AI have risen by nearly 26%.

The Benefits of AI in Manufacturing

AI is transforming the manufacturing industry in all aspects of the production cycle. The benefits are not limited to large-scale manufacturing; even smaller companies can benefit from AI by increasing output, reducing defects, and improving cycle times. Benefits include:

• In R&D (Research and Development), AI improves product development and life cycle by comparing and analyzing data to identify patterns that can enhance product development, design, and assembly line techniques.

• Production techniques use Data-Driven AI for predictive maintenance, reducing downtime and lowering repair costs, improving equipment uptime, and having a safer production environment. Cross-functional processes powered by AI are used to reduce scrap and improve quality.

• Supply chain management and logistics use AI with outbound and inbound demand forecasting, which helps to predict the future demand for manufactured products and inventory. This optimizes inventory management and reduces costs.

• The control tower, a centralized hub that gives real-time visibility across all sectors, becomes far more efficient when managers can make data-driven decisions, made possible with the use of AI.

• Process optimization increases with AI with optimized task sequencing, improved resource allocation, and precise scheduling. Quality control and predictive maintenance powered by AI also work to reduce defects.

The Future of Smart Manufacturing

Industry 4.0, or “smart manufacturing,” is the transformation of the manufacturing industry using technologies including Artificial Intelligence, Machine Learning, and connectivity using the Industrial Internet of Things to enhance and improve current production techniques.

This future will lead to higher automation rates, customization, sustainability, cybersecurity, and increased human-machine collaboration as creative control becomes decentralized.

AI and ML will help manufacturers discover patterns in data that would be challenging for humans to discover, leading to more accurate predictions and informed data-driven decisions.

According to a survey conducted by McKinsey, 70 percent of companies are considering or using smart manufacturing.

The Role of AI and ML in Predictive Maintenance in Manufacturing

Artificial Intelligence and Machine Learning are increasingly used in data analysis to help predict when equipment failure is due to occur so that maintenance can be scheduled before any breakdown or failure. This process analyzes a large amount of data from sensors and other devices located on or near production equipment, supplies, and other devices. Predictive maintenance boosted by AI can increase production equipment availability by up to 20% while reducing inspection and maintenance costs.

The Importance of AI and ML-Driven Quality Control in Manufacturing

Quality control in manufacturing is also improving because of AI and ML. A new process of predictive quality analytics is quickly transforming the industry and is improving productivity and profitability. Using the IIoT, sensors, and machine records all contribute to quality control in real-time, gathering data that the AI needs to improve quality control.

The use of AI and ML in quality control helps manufacturers understand patterns, predict potential problems, and address them before they arise and slow production. This has helped increase productivity and profitability.

The Impact of AI on Industrial Automation

Factories are becoming increasingly automated and require less and less human interaction. During the past decade, especially the past five years, AI and ML have powered the switch from traditional fieldbus technologies and other legacy networks, replacing them with ProfitNET, EtherCat, and Ethernet/IP.

While connected robotics have been around for a few decades, with AI, ML, and the IIoT, automated systems now include preventative maintenance, process monitoring, inventory management, and process flow. Industry 4.0 means that not only are your industrial robots connected, as was the case earlier, they are also learning from each other and their environments, via their sensors and other IIoT devices.

Previously, industrial robots could only operate in an exact way following precise programming; now, industrial robots can interact with changing environments based on sensory or visual feedback. This leads to further automation, which benefits efficiency and profitability and helps reduce humans' exposure to hazardous job conditions.

Outside of direct factory applications, these advances in robotics can be used in exploration, including in dangerous fields such as the oil & gas industry, and inside the factory, mobile deliveries to assembly lines become possible as machines “learn” their surroundings and have collision avoidance.

Examples of AI in Manufacturing

Automobile manufacturers, including Ford and BMW, use AI, ML, and the IIoT for quality control and precision tasks. Ford uses this technology to sand cars (to get them ready for painting) in just 35 seconds. Meanwhile, BMW has saved over S1 million yearly through use of AI tools in the manufacturing process and was able to use its workers in other positions. Samsung uses AI-powered robots, mechanical arms, and automated vehicles for quality checks, assembly, and even to transport materials in its factories.

Airbus reduced aerodynamics prediction time to 30 milliseconds from one hour using ML, which means design teams can explore multiple design changes in far less time to make safer and more efficient aircraft.

Frito-Lay used AI and ML to gain over 4,000 hours of manufacturing capacity, decreasing downtime and production costs.

Powering Industrial Innovation With AI Solutions

The fourth industrial revolution, powered by AI, ML, and the IIoT, has dramatically increased productivity and efficiency for manufacturing operations and will continue to do so. This is for both large and small manufacturers. Adoption means higher quality, reduced downtime, safer working conditions, and more automation and reliability.

Processes, including predictive maintenance, quality control, inventory management, and worker safety, have all been positively affected. To take your operations to new heights with increased efficiency and cost savings, work with our AI experts to craft transformative solutions custom-tailored for your business. Contact us today for Artificial Intelligence solutions.