Henry Ford famously said, “Any customer can have a car painted any color that he wants so long as it is black.” Ford could get away with this lack of personalization because his manufacturing process gave him such a competitive advantage and made the price of the car so cheap.
A hundred years later, companies designing, creating and building products need to do both. They need a more customizable, personalized product and they need streamlined and efficient processes to make it. They are increasingly turning to AI as the answer.
As AI becomes more sophisticated and new technologies such as generative AI (GenAI) grow in utility, more companies are using AI to enable high-quality, custom manufacturing without sacrificing quality or speed.
This demand for personalization has already been seen with digital products, but is difficult to translate into the physical manufacturing process. The use of AI enables the bridging of digitally originated and physical products. With technology like GenAI, new concepts can be produced very quickly in physical form and distributed to consumers rapidly and efficiently.
Consumer Demand for Customization
Products such as software-defined vehicles suggest a future where the manufacturing process is never done – even after the product has reached the consumer. Consumers want products that can be tailored and customized to specific preferences. In fact, 71% of consumers expect personalization. This demand has largely been driven by digital products, which can be easily personalized to deliver a unique experience to each user.
Companies want a product of one – one product designed especially for each consumer. They understand that companies that excel at personalization generate 40% more revenue. However, it is difficult to be efficient in producing individual versions of physical products for each person. This is where AI can influence the various stages of the manufacturing process.
Ideation and Testing Using AI
Artificial intelligence now allows consumers to be more involved at the conception of products in new and impactful ways. AI can be used to analyze trends and automate the product ideation process. Predictive analytics and digital twins can then be used to test the outcomes of potential product variants before they go into production. These simulations allow innovators to assess the impact, feasibility and limitations of their ideas, enabling faster iteration and refinement by minimizing the time and cost associated with full physical prototyping.
The fashion industry is a strong example as companies are tasked with tapping into microtrends and rapidly responding to changing demands and new social media trends. Fast-fashion clothing brand, Shein, uses GenAI to discover fashion trends and quickly create unique clothing designs. Through this process, they can post 10,000 new products on their site per day and manufacture and ship the garment in as little as 10 days.
In January 2024, Shein faced a lawsuit alleging their AI-based algorithm infringes on intellectual property rights by analyzing clothing and fashion trends from independent designers. The use of GenAI and copyright laws is still a very murky subject. Several ongoing lawsuits against companies like ChatGPT will better define the use of generative AI in the future. However, it is clear that whatever the guidelines for its use, AI has significant potential to speed up the ideation and testing process.
Production
When it comes to the production process, AI is helping to enhance overall productivity and speed up production times, which presents new opportunities for rapidly delivering more personalized products. Through predictive maintenance, AI can predict when equipment is likely to fail, enabling proactive maintenance that minimizes downtime and maximizes operational efficiency. Through techniques like computer vision, AI systems scrutinize products on the assembly line, detecting even subtle defects that might elude human inspection. This not only ensures a higher standard of quality but also accelerates the manufacturing process by swiftly identifying and rectifying issues.
The integration of “cobots,” collaborative robots working alongside humans in assembly lines, exemplifies the evolving synergy between AI and human labor. These sophisticated robots contribute to improved efficiency and precision, augmenting human capabilities. Audi is one example of a company integrating cobots to handle monotonous and strenuous tasks, increase safety and improve productivity.
AI-powered automation has led to a remarkable 50% reduction in production time. Additionally, technologies like spatial computing, driven by AI, are gaining prominence on factory floors, further enhancing manufacturing capabilities.
General Motors has embraced generative design, additive manufacturing, and 3D printing, leveraging AI to experiment with new parts configurations and swiftly iterate on improvements. This idea of additive manufacturing is closely related to the auto industry’s hopes for the software-defined vehicle where the product is never finished – even after delivery. Additive manufacturing allows GM to introduce incremental upgrades and improvements.
Delivery
Traditionally, fulfilling product demand involved planning production far in advance and maintaining large product stocks. AI-based techniques for demand planning and forecasting now provide manufacturers and distributors with a more detailed and highly confident view of where and at what scale products will be demanded. In the Shein example mentioned above, the company only produces 100-200 pieces for each new SKU and relies on its rapid production process to meet further demand.
Predictive analytics, fueled by AI, can identify trends and predict spikes in demand. For instance, in the shoe industry, leveraging data points related to overall style popularity, artist or athlete associations, and consumer preferences, predictive models enable manufacturers to tailor products more precisely, ensuring availability closer to customer demand.
The application of AI in supply chain optimization further enhances efficiency. AI helps reduce forecasting errors by up to 50%, leading to a remarkable 65% reduction in lost sales due to better product availability. This not only streamlines operations but also allows for more customized products targeted at specific markets.
Decades ago, pioneers like Zara embraced just-in-time manufacturing, shortening lead times for new product variations. Today, Tesla leverages generative AI to enhance demand forecasting and optimize inventory handling in international supply chains. These examples underscore how AI is steering companies away from outdated Excel-based models, fostering more efficient and responsive delivery processes in manufacturing.
Empowering Humans Through AI
As the manufacturing industry embraces more AI technology in 2024, it is important to note that the goal of AI-powered automation is not to replace humans with an automated process. The goal is to increase speed and scale to empower existing teams to do much more. For example, the cobots aren’t meant to replace human workers. However, they do increase worker safety and free human employees up to focus on tasks that require more critical thinking and have the potential to improve the quality and efficiency of the production process.
We’re increasingly transitioning into a world where people’s tastes and demands are defined by their experiences in digital spaces. AI is a big helper in bridging the gap between the physical and digital and allowing companies to keep up with these changing demands.
