The adoption of artificial intelligence (AI) in manufacturing isn’t new, but it has grown exponentially in recent years. In 2023, AI investment in the manufacturing industry was valued at an estimated $3.2 billion and is expected to grow to $20.8 billion by 2028.
It’s no surprise — AI offers unprecedented opportunities to help improve operational efficiency and drive better decision making. If you’re still wondering if you should bring the technology into your manufacturing facility, here’s what you need to know to get started.
AI areas
Today’s AI technologies can contribute to virtually all aspects of the manufacturing process, including:
Product development. One of AI’s many strengths is the ability to process vast amounts of data. It can gobble up information from a range of sources, including financial filings, media and technical reports, social media posts, and consumer research to help you come up with new product ideas. Further down the development road, you can use AI-based simulations to inexpensively test new products without producing a physical prototype.
Production. Automating your production line, whether partially or completely, can streamline your production process. Collaborative robots (or cobots) can work alongside your employees to perform a wide variety of routine tasks, from welding parts to picking and packaging end products. This reduces the risk of human error and lets your employees focus on more high-value activities. AI also can improve scheduling and allow you to make real-time adjustments (for example, redeploying employees or adjusting equipment settings) based on production floor data, customer purchase patterns and other information.
Machinery/equipment maintenance. Predictive maintenance monitors machinery and equipment data so you can get ahead of breakdowns and other disruptions. IoT (Internet of Things) devices can sense potential red flags like unusual vibrations, energy consumption patterns and temperature fluctuations. Predictive maintenance also makes it easier to have the necessary parts on hand when you need them and schedule maintenance for slower periods, cutting downtime and boosting productivity. And generative AI can process manuals and maintenance logs for troubleshooting and determining whether equipment can handle increased workloads.
Quality control. AI-powered image recognition systems can automatically identify defects and anomalies so substandard products can be removed before they reach customers. The technology compares items with images of acceptable looking products and flawed products. In addition, AI can track information, such as customer complaints, for patterns suggesting an uptick in problems that warrants investigation.
Supply chain and inventory management. Manufacturers can automate their supply chain planning using AI data analytics. This technology enables more accurate predictions so manufacturers can prepare for and survive volatile conditions. Real-time information minimizes bottlenecks and leads to cost savings.
First steps
Bear in mind that for all its benefits, adopting AI is no small matter for manufacturers. The bottom-line effects can be dramatic, but a significant investment is required. That’s just one reason why it’s wise to start small.
Rather than implementing AI tools across your manufacturing company, consider launching a pilot program. For example, introduce AI to only one or two production lines. You can test and adjust as necessary before you roll it out more extensively — but still on a gradual basis.
Before kicking off a pilot program, consider:
Staffing. Do you have the necessary expertise on board to make the most of AI? In a tight job market, it may take some time to hire the skilled workforce that’s required.
Data quality. As the saying goes, garbage in, garbage out. Invest in advance in the necessary measures to achieve clean and accurate data.
Scalability/integration. Your AI tools must be able to grow with your company as it expands. They should also integrate with your existing systems, including your manufacturing execution system, as well as any enterprise resource planning and custom software.
Compliance. AI implicates a host of standards and regulations related to, for example, data security and privacy. Legal liability is another concern. For instance, AI has caused problems when it produced unintentional but also unlawfully biased results.
Get on board
AI in manufacturing is likely to prove game-changing, and your company can’t afford to fall behind. Careful planning and budgeting are essential, though. We can help you assess your needs, determine the necessary capital outlay required and help you take advantage of certain tax breaks for your investment.
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