Generative AI in Manufacturing: Usecase, Trend & Future
The manufacturing industry has seen a significant shift in recent years, driven by advancements in automation, robotics, and artificial intelligence (AI). Among these technologies, generative AI (genAI) stands out as a major driver of innovation.
One exciting use case of genAI in manufacturing is in the development of humanoid robots. GenAI empowers these robots to learn from various inputs, enabling them to take intelligent actions and integrate seamlessly into workflows. Ambitious projects like GR00T showcase this potential, with the humanoid robot market projected to reach $38 billion by 2035 (Goldman Sachs). But genAI’s impact goes beyond robots.
While AI already plays a role in manufacturing through ERP systems, breakdown identification, machine process optimization, and defect detection (with the help of IoT and cloud computing), this article focuses specifically on the versatile applications of genAI in the manufacturing sector.
Use case of GenAI in Manufacturing
Tools like chatGPT, Bard, and Bing are now a part of every industry, even manufacturing. Product research, decision-making, product ideation, documentation, and inventory management are basic applications of genAI in the manufacturing industry.
Decision making
Optimizing the manufacturing process is always a priority among decision-makers–but how to do so? GenAI has its applications here, by analyzing the data from sensors, detectors, and machinery AI can assimilate and provide better ways to optimize the manufacturing process.
Several manufacturing units have reported significant improvements in MAS action with informed decision-making using AI. For instance, a leather manufacturing company reported a 30% reduction in lead time and a significant drop in scrap waste. The healthcare sector reported a drop in instrument malfunction and breakdown, as the AI preemptively suggested replacements and maintenance. Thanks to GenAI, along with computer vision data, defective products in the assembly line can be detected much more precisely, resulting in a 65% reduction in quality control staff while maintaining productivity.
GenAI has also been successful in market data analysis, providing intelligent suggestions for better decision-making based on the data analyzed.
New product ideas and customization
Gathering consumer feedback is an essential aspect of improving the quality of a product, and consequently, its sales. However, it can be challenging to sift through piles of feedback to identify specific cases or suggestions. It’s like finding a needle in a haystack.
But what if you have a magnet, there is no better way to explain it. AI can quickly analyze large amounts of data in seconds and provide a detailed outlook that goes beyond the top-level impressions. This has helped companies in various industries to create new product designs and patterns based on customer feedback. The result is a reduced time to market, lower costs, and better responses from customers.
Overall, AI-powered technology has proven to be a valuable tool for manufacturing units, resulting in tremendous growth for companies.
Inventory Management
Inventory management is a significant challenge faced by the manufacturing industry, which affects around 62% of business finances due to poor tracking. However, improving inventory management can help save up to 10% of inventory costs.
To tackle this challenge, AI-powered neural networks can analyze inventory storage data, upcoming cargo deliveries, production rates, and other available information to create an effective inventory management system. This system can help address overcommitting, overstocking, understocking, and improve warehouse space management and logistics, ultimately resulting in significant cost savings for businesses.
Robotic intelligence
Manufacturing giants such as Dell and Tesla have already implemented autonomous environments in their workshops, where AI-driven machines handle tedious tasks. In a recent CNBC interview, Cathie Wood suggested that Tesla could reduce its EV production costs by up to 50%, which is a challenging but achievable goal with intelligent robotic manufacturing units.
The predictive intelligence of GenAI is capable of analyzing the manufacturing process and finding optimal ways to improve it. This technology enhances production time, reduces labor costs, and requires only an investment in AI development and data handling.
Innovation: Holograms and Digital Simulation
The manufacturing industry has been familiar with concepts like 3D holograms and digital twins for a long time, which are now becoming a reality. Instead of creating a new test sample for every product, it is more convenient to run a digital simulation, which is made possible by AI.
For instance, companies like Nvidia and IBM, who design advanced chipsets, no longer need to create a new chip for every new feature they want to test. This used to be a necessary evil due to the high costs involved. However, with the help of advanced AI, the process of product design and simulation has become faster and more cost-efficient for manufacturing companies.
GenAI Benefits in Manufacturing
Faster Time to Market: GenAI generates multiple design options based on your concept and automatically simulates and tests them. This speeds up development, getting your product to customers quicker.
Optimized Operations: GenAI can analyze vast amounts of data to identify bottlenecks and inefficiencies in your production line. It can then suggest adjustments, like scheduling maintenance or optimizing material flow, to keep things running smoothly.
Better Inventory Handling: GenAI can predict demand fluctuations and suggest optimal inventory levels for raw materials and finished products. This reduces the risk of stockouts or having excess inventory that ties up capital.
Enhanced Product Quality: GenAI is capable of detecting defects in products during the manufacturing process. Detecting these issues at an early stage can help reduce waste and guarantee that your customers receive products of the highest quality.
Cost Saving: By streamlining operations, reducing waste, and optimizing inventory, GenAI can lead to significant cost savings. Additionally, faster time to market allows you to capitalize on market opportunities more quickly.
Keynotes: Implementing AI to Your Business
As the manufacturing landscape evolves, GenAI (Generative Artificial Intelligence) presents a transformative opportunity. However, for successful implementation, a strategic approach is crucial. Here are keynotes to guide your journey:
1. Good data quality and adequate quantity are crucial for effective AI models. Context is essential when feeding data to AI. Annotate and label your data to explain process variations, equipment issues, or external factors affecting production. With enriched data, AI can learn and make insightful predictions.
2. It is recommended to conduct a thorough analysis of the production line to identify bottlenecks. Focusing GenAI on these areas can lead to targeted improvements and maximize its impact.
3. It is crucial to avoid attempting to transform your entire operation in one go. Identify specific areas where you can implement AI efficiently, like predictive maintenance or improving a particular production stage. Once you have achieved success in that smaller setting, you can then expand to include your entire facility.
4. Seeking guidance from experienced AI specialists can significantly enhance your success. HCode Technologies, for instance, boasts a team of seasoned AI specialists with extensive experience in the manufacturing domain.
Hurdles
While advanced technology has its advantages, it also presents some challenges that need to be overcome. In the case of GenAI, there are challenges related to data security and privacy, implementation, and the consequences that follow, such as job reductions, regulatory compliance, and resistance to change in manufacturing processes.
These hurdles must be carefully addressed and managed to ensure the successful implementation and adoption of new technologies.
Integration Challenges
Marrying cutting-edge GenAI with existing legacy systems can be a complex task. These established systems may require significant modifications or even a complete overhaul to ensure compatibility with AI, leading to disruptions and potential downtime during the integration process. This can significantly impact productivity and profitability.
Ethical and Regulatory Concerns
As GenAI becomes more sophisticated, ethical considerations and regulatory compliance become paramount. Manufacturers must navigate concerns around data privacy and security as vast amounts of data are collected and analyzed. Additionally, the potential for job displacement due to automation raises ethical questions and requires careful workforce planning to mitigate negative social impacts. Keeping pace with evolving regulations around AI usage in manufacturing is also crucial to avoid legal or financial repercussions.
Operational Risks
Overreliance on untested or poorly calibrated GenAI systems can introduce operational risks. Malfunctioning AI can lead to production delays, quality issues, and increased supply chain management costs. Manufacturers need to have robust testing and calibration procedures in place to ensure the reliability of their GenAI systems and mitigate these risks.
Real-Examples of GenAI in Manufacturing
GA Telesis
GA Telesis is collaborating with Google Cloud to integrate generative AI technology in its sales and service operations for airlines. This integration aims to improve the accuracy and efficiency of handling customer inquiries and orders for parts supplied to major global airlines.
GA Telesis is using Google Cloud’s Vertex AI platform to automate the synthesis of purchase orders and quote generation, enhancing customer experiences and expanding business opportunities. The CEO of GA Telesis highlights the importance of scaling responsiveness using Google Cloud’s AI technology to provide exceptional service and to achieve a zero-minute response time for customer inquiries.
The Outlook
GenAI is an advanced technology that is expected to revolutionize the manufacturing sector in the coming years. With the integration of robotics, automation, and AI, manufacturers can expect to see reduced costs and faster time-to-market.
However, there are challenges that come with this technology, such as job displacement, increased emphasis on digital processes, and data privacy concerns. To overcome these challenges, manufacturers must carefully analyze, label, and collect data, conduct small-scale tests, and collaborate with partners to ensure successful implementation.
By leveraging GenAI, manufacturers can optimize operations, improve decision-making, and drive innovation. Early adopters of this technology are poised to gain a significant advantage in the market, and their impact is likely to be felt for years to come.