The Role of AI in Lean Manufacturing

Summary

Manufacturers have relied on lean principles for decades, recognizing that these approaches help them improve output, remain competitive and support the bottom line. Additionally, many have recently explored how artificial intelligence could complement lean manufacturing.

What is lean manufacturing?

Lean manufacturing is a system that aims to reduce waste, enable continuous improvement and enhance customer value. Waste appears in many forms within production facilities. In addition to the wasted materials caused by excess or product defects, employees may not make the most of their time or skills, resulting in additional waste.
 
Continuous improvement identifies issues and empowers people to collaborate while solving them. By regularly seeking process and practice improvements, they can make ongoing progress rather than becoming complacent.
 
Manufacturers that prioritize customer value understand how to listen and respond to ever-changing needs while accounting for feedback. Product developers must also put themselves in customers’ positions to increase user-friendliness, functionality and enjoyment.
 
Artificial intelligence (AI) is an excellent addition to the lean manufacturing framework—if thoughtfully deployed, it can tackle all these areas and others.
 

Offering accessible personalization for high-end items

When people consider purchasing expensive goods, the prospect is usually even more appealing if they can customize specific aspects. For example, decision-makers at Ferrari emphasized the customer value aspect of lean manufacturing with AI.
 
The automaker achieved a 20% reduction in vehicle configuration time with an interactive customization tool. A machine learning feature lets users visualize potential cars in 3D while zooming in on or rotating aspects for fully immersive experiences. This offering has also increased sales leads by allowing buyers to be more involved in these early stages.
 
Moreover, generative AI helps design teams test more iterations with fewer physical prototypes for F1 vehicles and sports cars. That benefit lets designers apply drivers’ feedback faster and more meaningfully than they previously could.
 
Companies could also use generative AI to improve advertising campaigns by making numerous versions to cater to different audiences. Statistics indicate that 54% of consumers prefer personalized advertising. Sales should rise if manufacturers tailor advertising to show potential buyers they understand and cater to them.
 

Analyzing factory processes to find improvement opportunities

Another reason AI supports lean manufacturing efforts is because it accelerates problem-solving efforts by pinpointing inefficiencies. Once factory leaders know which process steps require too much time, excessive materials or inefficient employee use, they can identify practical fixes.
 
In one example, steel plant workers used an AI tool to perform a complex analysis and find the root causes of quality problems. Although this exercise formerly took weeks, artificial intelligence completed it in hours. Team members then had information to act on the provided insights. The chosen solutions improved product quality by 15% and reduced energy usage across production lines by 7%.
 
Some manufacturing executives extract additional feedback from Internet of Things (IoT) sensors placed strategically throughout production environments. This hardware can provide real-time information about cycle times, defect rates, machinery utilization and much more. Leaders track those details to quantify their success and focus on changing metrics.
 
Many IoT sensors connect to interfaces that authorized users can access from anywhere. Then, whether leaders want to monitor the activities at one factory or 100 sites, they can conveniently retrieve the information. Decision-makers may also find that AI aligns with technologies they already use, making adoption more accessible. For example, research indicates that 40% of manufacturing and distribution companies invest in IoT and big data technologies.
 

Boosting productivity with artificial intelligence

As manufacturers face increasing customer demands, more competitors and an ongoing labor shortage, many realize artificial intelligence could make their facilities more productive. Some use robots, recognizing that these advanced machines excel at repetitive tasks. Others have installed computer vision tools that use AI algorithms to spot defects invisible to the naked eye. These work quickly but thoroughly, increasing the number of products checked per hour.
 
Such applications tackle the waste-reduction aspect of lean manufacturing and identify process enhancements to support continuous improvement. Additionally, a study of equipment and machinery manufacturers revealed that these entities could become up to 50% more productive by implementing AI, lean manufacturing, sustainability and digital technologies. The analysts confirmed that these components would become the backbone of tomorrow’s factories.
 
Another finding was that 75% of parties within advanced manufacturing identified adopting emerging technologies—including AI—as a top priority for research, development and engineering. Those interested in artificial intelligence will get the best results by matching the technology to a clearly defined need or goal.
 

AI and lean principles: A smart pairing

Manufacturers have many appealing reasons for bringing AI into their lean practices. Doing so could help them reach goals faster and keep companies on track for long-term success. Those needing inspiration should consider seeing what peers have done and planning small-scale trials to test specific applications that fit their budgets and other parameters.