Everyone is talking about artificial intelligence (AI) in manufacturing today because it promises to change everything, from managing our day-to-day lives to managing a massive manufacturing facility.
Manufacturers specifically are starting to realize how the different types of AI can be used in manufacturing. AI is already being used to automate processes and routine tasks, create more accurate forecasts, improve predictive maintenance, improve logistics, sniff out potential errors, and so much more. This demonstrated success across many AI use cases in manufacturing is encouraging manufacturers to invest even more, prompting a snowball effect that stands to transform how smarter manufacturers work and win.
Understanding How AI is Used in Manufacturing
To understand how AI is used in manufacturing, we first need to understand AI itself. McKinsey defines Artificial Intelligence as “machine’s ability to perform the cognitive functions we associate with human minds.” In other words, AI uses data to solve problems that would typically require humans.
Machine learning (ML) is a subset of AI where algorithms (i.e., a set of rules or processes) are trained using specific data. More data and feedback to the machine learning creates better results, and more relevant data and feedback creates even better results. For example, Spotify and news platforms use machine learning; as you engage with and like the content, algorithms detect the patterns to deliver similar content. In manufacturing, machine learning can detect patterns to optimize operational performance and spot exceptions quicker and more effectively.
AI and ML in manufacturing can be used to optimize decision-making by using millions of historical data points and considering thousands of scenarios and options, all in the blink of an eye. If your business typically gets most customer orders in the last week of the month, for a simple example, AI will take that into account when forecasting demand, scheduling deliveries, and creating a workforce plan.
Use Cases: AI in Manufacturing Examples
How is AI used in manufacturing today? Just think about the areas of your manufacturing operations where more time, more data, and more correlations would have helped you increase efficiency, reduce costs, or improve operations. AI in manufacturing examples include things as simple as scrap reduction and as complex as demand forecasting. Following are a few specific examples of AI applications in manufacturing.
Predictive maintenance is a hot topic for how AI is used in manufacturing. Manufacturers typically spend much effort and money to optimize predictive maintenance schedules to minimize downtimes and maximize production efficiency and equipment utilization. AI can analyze real-time equipment data, weight it against past performance, repair and manufacturing costs, and delivery timelines, and generate optimal predictive maintenance schedules to maximize profits, efficiency, and more.
Quality control and defect detection
Quality control is based a lot on statistics, meaning there is an acceptable, albeit minimal, number of defects that make it through to customers. AI applications in manufacturing can analyze scrap, quality, customer, operations, equipment, and even visual data from cameras to monitor 100% of products and catch defects that a human might miss.
Supply chain optimization and demand forecasting
Optimizing the supply chain is an unrelenting and continuous effort for manufacturers, which makes it one of the most prominent AI use cases in manufacturing. Because AI works thousands of times faster and can analyze exponentially more data than any human ever could, it can capture data from suppliers, logistics providers, quality control, operations, sales, customer service, and more, all with just one goal in mind: getting products to customers efficiently. As noted by IBM, “AI is becoming essential to innovative supply chain transformation. Forty-six percent of supply chain executives anticipate that AI/cognitive computing and cloud applications will be their greatest areas of investment in digital operations over the next three years.”
Autonomous robots and smart manufacturing
Robots in manufacturing are typically used to conduct routine operations, flawlessly and continuously. As AI becomes smarter and more reliable, AI in manufacturing examples will start to see autonomous forklifts moving inventory, AI adjusting equipment settings without human intervention, AI placing orders with suppliers automatically, and more.
Intelligent inventory management
Inventory is a critical yet expensive cost of doing business. Minimizing those costs while maximizing efficiency is a perfect example of the artificial intelligence use case in manufacturing. Imagine AI in ERP systems eliminating the need for manual inventory counts while ensuring always accurate information on quantities and locations.
The benefits of AI in manufacturing are many and the above examples are but a few. More broadly, AI in the manufacturing industry will change how every role interacts with technology and gains the time to focus on higher-value activities. Some specific benefits of AI and ML in manufacturing include the following:
- Increased operational efficiency and productivity using more data in less time with higher accuracy to benefit the critical areas of manufacturing operations.
- Improved product quality and reduced defects by working faster and more thoroughly than any human ever could.
- Enhanced safety and risk management using data and intelligence to alert operations to potential issues, pull humans out of dangerous or tedious jobs, and serve as another pair of eyes constantly scanning for risks.
- Cost savings through optimized resource utilization by taking business, customer, supplier and other information into account when developing operations and workforce plans.
- Faster and more accurate decision-making because managers and executives get near-instant access to more accurate information for more informed decisions made with more confidence.
Challenges and Considerations in Implementing AI in Manufacturing
While there are many benefits of artificial intelligence, some challenges lie ahead for manufacturers who don’t consider all the implications before moving forward. These include the following:
- Data collection and data quality are critical to powering successful AI use cases in manufacturing. Be sure AI uses only clean data and has access to a broad range of data.
- Integration with existing systems and infrastructure similarly ensures AI in the manufacturing industry has access to the right data without requiring human intervention. Make sure the selected AI applications in manufacturing can integrate with existing technology.
- Workforce readiness and upskilling can make or break AI use cases in manufacturing. Workers must be comfortable trusting the AI and have the skills necessary to understand AI’s implications and get full value out of AI tools.
- Ethical considerations and privacy concerns are another hot topic for AI in general and AI in manufacturing. Understand how an AI solution uses, stores, and processes internal, customer, payments, and other sensitive data.
Future Trends & Opportunities in AI-driven Manufacturing
AI in manufacturing is just getting started. It has a promising future and will transform how manufacturers operate over the coming years and decades. Here are a few areas to keep an eye on for future advancements in artificial intelligence.
- Advances in machine learning and deep learning algorithms will continuously make AI more powerful, engaging, and valuable to manufacturers.
- Internet of Things (IoT) integration with AI in manufacturing enables seamless data exchange and more autonomous devices, equipment, and operations.
- Collaborative robotics and human-machine interactions will drive massive leaps in efficiency and productivity across AI use cases in manufacturing.
- Adaptive and self-learning systems will accelerate the move from one-task robots and technologies to ones that use AI to adjust to changing needs without human interaction.
- Leveraging AI for sustainability and green manufacturing, or any other financial-adjacent objective, will help manufacturers balance profitability and sustainability while supporting environmental, social, and governance (ESG) or other “triple bottom line” initiatives.