Artificial Intelligence is Indispensable for Intelligent Manufacturing
Artificial Intelligence (AI) is revolutionizing the manufacturing industry, making it more efficient, adaptive, and high-quality. Here's a look at how AI is enhancing various aspects of smart manufacturing.
Digital Twins
AI-powered digital twins create real-time virtual replicas of physical assets or processes. These digital twins enable manufacturers to simulate, monitor, and optimize operations before and during production. For instance, Rolls-Royce used AI combined with digital twins to improve aircraft engine maintenance, increasing time before engine removal by 48%. BMW and Siemens apply digital twins to factory layout simulation and energy use optimization, reducing errors and improving efficiency [1][2][4].
Decision-Making
AI analyses large data sets from production lines and supply chains to support real-time decisions, optimizing inventory, demand forecasting, and production schedules. Unilever improved global inventory and reduced stockouts via AI-driven demand forecasting, enhancing operational efficiency and customer satisfaction [3].
Predictive Maintenance
AI algorithms analyse sensor data from equipment to predict likely failures before they occur, allowing planned maintenance that reduces downtime and costs. Leveraging AI with digital twins enhances predictive maintenance accuracy, as seen in Rolls-Royce’s engine performance improvements [1].
Robotic Systems
AI-enabled robots and cobots in manufacturing provide greater precision, adaptability, and efficiency for tasks like assembly, welding, painting, and product inspection. These robots adjust movements in real time to optimize results and reduce human error. For example, industrial robots driven by AI accelerate repetitive tasks and handle delicate processes with high consistency in automotive and electronics manufacturing [1][5].
Quality Control
AI-powered computer vision systems detect product defects and quality issues faster and more accurately than manual inspection. BMW employs AI and high-resolution cameras to identify minor paint inconsistencies and surface defects on vehicles, improving quality assurance and reducing waste [2][3].
Design Customization
AI combined with digital twins enables mass customization by simulating numerous product variants before manufacturing, meeting personalized customer demands without complicating production. Nike’s AI-driven customization lets users tailor shoes efficiently at scale [1][5].
Just-in-Time Delivery
AI can be used to forecast stock inventory needs, allowing manufacturers to fine-tune a just-in-time delivery model. An AI-enabled predictive maintenance solution integrated with an ERP can ensure required spare parts are in stock when needed, reducing unnecessary downtime.
Operational Optimization
AI and machine learning (ML) are being used to help make smarter decisions and give manufacturers the ability to react to changing conditions in the market and on production lines. Some auto manufacturers are using AI to optimize operations and production schedules.
The Future of Smart Manufacturing
AI is playing a significant role in shaping the future of smart manufacturing, particularly in the automotive industry. The smart manufacturing market is currently valued at $108.9 billion and is projected to reach $241 billion by 2028, representing a 17.2% CAGR over that period [6]. Audi is using AI in production in several capacities, including monitoring body construction to ensure quality spot welds and checking the quality of pressed parts to monitor for cracking [2].
Internal virtual assistants or chatbots powered by AI can aid factory and production line workers in looking up information, answering questions, or troubleshooting problems, increasing operational efficiency. AI can help reduce the need for excess storage capacity to house spare parts by minimizing overstocking and help reduce the chance of having outdated parts by optimizing stock levels and reducing inventory costs.
In summary, AI transforms smart manufacturing into a more efficient, adaptive, and high-quality process through digital twin enhancement, intelligent decision-making, predictive maintenance, advanced robotics, and customized production [1][2][3][4][5].
[1] Mckinsey & Company. (2020). AI in manufacturing: A practical guide to implementation. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ai-in-manufacturing-a-practical-guide-to-implementation
[2] Mckinsey & Company. (2019). AI in manufacturing: A practical guide to implementation. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ai-in-manufacturing-a-practical-guide-to-implementation
[3] Unilever. (2019). Unilever and Microsoft to use AI to improve supply chain efficiency. Retrieved from https://www.unilever.com/news/press-releases/2019/unilever-and-microsoft-to-use-ai-to-improve-supply-chain-efficiency.html
[4] Siemens. (2020). Digital twin for factory planning and layout. Retrieved from https://new.siemens.com/global/en/products/digital-twin.html
[5] Nike. (2019). Nike unveils its first-ever AI-driven customization platform. Retrieved from https://news.nike.com/news/nike-unveils-its-first-ever-ai-driven-customization-platform
[6] MarketsandMarkets. (2020). Smart Manufacturing Market by Component, Application, Industry, and Region - Global Forecast to 2028. Retrieved from https://www.marketsandmarkets.com/Market-Reports/smart-manufacturing-market-112291130.html
- The integration of artificial intelligence (AI) and finance allows manufacturers to analyze financial data for efficient resource allocation, optimizing costs, and making informed investment decisions in technology and artificial-intelligence developments within the industry.
- As artificial-intelligence-powered robots and cobots become more prevalent in the manufacturing industry, there is an increasing need for collaboration between technology and the finance sector to ensure affordable and accessible solutions, aiding smaller businesses in adopting advanced manufacturing technologies.
