With the rise of the internet, the leading top-producing factories worldwide have digitized their operations. As a result, organizations are flooded with large amounts of data from various factory tools, making it difficult to manage.
Unfortunately, many companies lack the resources to utilize this information to decrease expenses and improve productivity. In order to accomplish their goal, they need the assistance of AI technology.
Across a wide variety of applications, manufacturers are adopting AI and machine learning tools at a rapid pace. According to a TechJury article, 35% of companies currently use AI, and 42% are considering implementing it in the future. [1]
Artificial intelligence (AI) is revolutionizing the manufacturing industry by offering a wide range of benefits, including the following:
AI for manufacturing companies can scale operations more efficiently and effectively by automating tasks, optimizing processes, and predicting demand.
AI can quickly generate new ideas, test prototypes, optimize designs, and improve the efficiency of existing ones.
Leveraging artificial intelligence enhances energy efficiency in manufacturing by optimizing production processes, reducing waste, and improving predictive maintenance.
With real time monitoring, artificial intelligence identifies potential hazards and monitors compliance standards by reviewing video and data.
AI provides insights from complex data sets, identifying trends and predicting future outcomes.
With artificial intelligence, the possibilities are seemingly endless.
The manufacturing industry produces data daily through industrial IoT and smart factories. This has led to the increasing implementation of artificial intelligence (AI) solutions such as machine learning (ML) and deep learning neural networks. Manufacturers can analyze data more effectively and make informed decisions using these AI technologies.
Manufacturing often utilizes predictive maintenance with the help of artificial intelligence. By analyzing production data, AI can aid in predicting failures and planning maintenance, resulting in decreased maintenance costs for production lines.
Did You Know: Projections put the use of AI in manufacturing increasing from $1.1 billion in 2020 to $16.7 billion by 2026. [2]
Below are some AI manufacturing trends to keep an eye out for.
The dream of fully automated manufacturing isn’t new, but reality started to take shape around the same time as the German government’s 2011 introduction of “Industry 4.0,” or I4. AI-assisted tasks, such as interoperability, information transparency, technical assistance, and decentralized decision-making, underpin the fundamental principles of Industry 4.0, the fourth industrial revolution, by monitoring, recording, and analyzing the entire manufacturing process.
Did You Know: According to a study, 75% of manufacturers say they benefit from AI/ML optimization. [3]
Major conglomerates and manufacturers like GE and Siemens are linking design, engineering, manufacturing, supply chain, distribution, and services together into single global systems that are intelligent and stable.
New business models like MaaS (manufacturing-as-a-service) and PaaS (production-as-a-service) allow for large-scale customization of manufactured goods, using AI to process manufacturing cloud data to determine relevant information, like the materials used in product design.
AI-enabled sensors make the manufacturing process safer and more accurate. Machine vision tools like IBM Cognitive Visual Inspection, an AI-assisted camera that’s more sensitive than the naked eye, are used for finding manufacturing defects and improving productivity.
These technologies enable the rise of “cobots,” collaborative robots that can work alongside humans and understand instructions in plain English. Combined with the same motion-sensing technology as self-driving cars, cobots and machine vision-enabled manufacturing tools can sense and understand the world around them, preventing accidents and operating more efficiently.
AI-powered robotics refers to integrating AI technologies and capabilities into robots, enabling them to perceive, learn, adapt and make decisions autonomously. Using robotics in manufacturing can potentially revolutionize industries by increasing efficiency, precision, and safety in various applications.
Manufacturing businesses require more than just assembly lines; they also need efficient supply chains, logistics, and inventory management solutions, which can be achieved through AI and machine learning. By creating a real-time and predictive model, businesses can monitor suppliers and receive instant alerts in case of any supply chain disruptions.
This enables them to quickly evaluate the severity of the situation and take appropriate measures to minimize the impact. AI-powered forecasting can reduce supply chain errors by 20-50%, and improvements to forecasting will reduce lost sales by 65%. [4]
Artificial intelligence can consider geography, socioeconomic conditions, economic cycles, political activity, and even the weather to accurately predict product demand. Additionally, monitoring social media algorithms can identify product trends that brands otherwise may not as easily find.
AI-assisted sensors contribute to predictive maintenance, forecasting possible equipment breakdowns and recommending preemptive actions to keep machinery in working condition. From distributors to suppliers, sensors can be embedded throughout every step of the manufacturing process, ensuring equipment is optimized to its maximum efficiency.
Manufacturing’s AI revolution will continue to streamline the manufacturing process by making it more accurate, reliable, and safe. Continued adoption of AI-enabled IoT devices will continue to drive market growth.
By 2025, the World Economic Forum predicts that 85 million jobs will be displaced by automation and technology, but it will also create 97 million new roles. That's a net increase of 12 million jobs. [5]
The proliferation of AI means many low-wage factory jobs will be replaced with technology, especially in developing countries like India and China. However, concerns about AI completely replacing workers appear to be overblown.
Employees in factories handle dangerous machinery on a daily basis. Despite following safety protocols, their work involves risks. Health and Safety is a top priority in the manufacturing industry. Machine learning technology helps prevent accidents by analyzing data from camera feeds IoT devices and tracking workers' location and health indicators.
It even monitors the correct usage of personal protective equipment. AI can react faster than a human worker if a dangerous situation arises. It can quickly shut down machinery and provide a prompt response, minimizing the accident's impact.
Predictive analytics and scenario modeling use machine learning to identify past accident causes and prevent future ones.
Manufacturers can use AI to proactively address safety concerns by identifying the underlying cause of issues, resulting in lower injury rates, increased productivity, and faster incident resolution.
Artificial intelligence based cameras and sensors play a crucial role in identifying any defects or errors during manufacturing and can detect irregularities quickly and accurately, leading to a significant increase in first-pass yield.
Also, the quality control modeling outputs can be improved even further by utilizing large language models to extract textual information from assorted reports and refining the data through quantitative measures. This approach can enhance the manufacturing process's efficiency and effectiveness, producing higher-quality products.
The journey toward a more innovative, connected manufacturing ecosystem is underway, and you can seize the opportunities too. Whether you are looking to upgrade a single workflow or tackle a large existing production system, Gigster can adapt and scale to all your AI needs.
Gigster uses AI to find, vet, and assemble specialized software development teams. We have a flexible model to either provide a team on-demand or fully manage the entire project on our side. We’ve completed over 5,000 projects for global brands like Harley Davidson, Vodafone, Colgate, Total Energies, YMCA, Microsoft, Nike, and Delta.
Integrating AI in the manufacturing industry means propelling forward into a new era of efficiency, innovation, and growth. As we've seen, AI's ability to optimize production processes, predict maintenance needs, and facilitate data-driven decision-making is reshaping the way manufacturers operate.
There is vast potential for AI-driven transformations, from automated quality control to predictive supply chain management. Embracing these advancements will not only lead to cost savings but also empower manufacturers to deliver higher-quality products at a faster pace.
[1] Cannon, L. (2019). 101 Artificial Intelligence Statistics [Updated For 2020]. [online] TechJury. Available at: https://techjury.net/blog/ai-statistics/.
[2] www.marketsandmarkets.com. (2022). Artificial Intelligence in Manufacturing Market Size, Growth, Trend and Forecast to 2025 | MarketsandMarkets. [online] Available at: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-manufacturing-market-72679105.html.
[3] Deloitte China. (n.d.). Deloitte Survey on AI Adoption in Manufacturing | Deloitte China | Consumer & Industrial Products. [online] Available at: https://www2.deloitte.com/cn/en/pages/consumer-industrial-products/articles/ai-manufacturing-application-survey.html.
[4] www.mckinsey.com. (2017). Smartening up with artificial intelligence. [online] Available at: https://www.mckinsey.com/industries/semiconductors/our-insights/smartening-up-with-artificial-intelligence.
[5] Schwab, K. and Zahidi, S. (2020). The Future of Jobs Report 2020. [online] World Economic Forum. World Economic Forum. Available at: https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf.
