Real-World Use Cases of Generative AI in Manufacturing

The world of artificial intelligence has changed dramatically since the emergence of general-purpose large language models (LLMs) such as the Chat Generative Pre-trained Transformer (ChatGPT). Their popularity caused AI to have a sub-field dedicated to such models called generative AI (gen AI).

Today, gen AI leads the way in AI initiatives aimed at boosting operational efficiency across industries. According to the latest McKinsey survey involving over 1,000 organizations globally in several industries, 65% of respondents report using gen AI. 

While gen AI’s industrial use cases are expansive, its applications in manufacturing specifically are increasing, as it helps increase revenue, enhance efficiency, and manage risks.

In this post, we will discuss the most prominent gen AI use cases in manufacturing, implementation challenges, and how you can use Encord to streamline manufacturing workflows.

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Generative AI Use Cases in Manufacturing

Although applications of generative AI in the manufacturing industry are still evolving, some common use cases are worth exploring to understand how gen AI delivers value to manufacturers. The sections below discuss 8 such uses.

Product Design

Gen AI streamlines product development by generating thousands of potential designs optimized for specific constraints like weight, strength, and cost. This accelerates the process from initial research to working prototypes.

Domain experts further augment development by leveraging 3D printing and virtual reality advancements to create lifelike images and prototypes. This allows engineers and designers to explore various possibilities that would otherwise be time-consuming through manual labor.

The technology helps in the initial market research stage, where Gen AI gathers and analyzes extensive customer datasets to provide insights into market trends. Experts then use gen AI algorithms to generate images of new products by writing an initial prompt with design specifications.

Next, they refine and test new ideas through an iterative process by trying different prompts to gain a better understanding of product features.

For example, in the automotive industry, manufacturers use Gen AI to create different dashboard designs with several features, such as a touchscreen, an instrument panel, and modern textures.

AI-generated Car Dashboard Design

AI-driven designs can reduce material usage while maintaining structural integrity, leading to more fuel-efficient vehicles.

Additionally, consumer product companies use gen AI for ergonomic and aesthetically appealing designs. By optimizing functionality and customer experience, manufacturers can bring more innovative solutions to the market faster.

Supply Chain Management

Supply chain optimization techniques using generative AI solutions allow manufacturers to enhance demand forecasting, improve logistic operations, streamline inventory management, and boost sustainability.

They use AI tools to analyze vast amounts of data to accurately predict consumer demand, track inventory levels, and align production schedules according to market needs.

These tools also analyze historical data and run what-if analyses to assess the impact of different events on prices and production processes. Gen AI then produces detailed reports to help manufacturers make informed decisions regarding procurement, distribution strategies, and pricing.

The method reduces the risk of overproduction or underproduction and ensures efficient resource allocation. This leads to significant cost savings, high profitability, and smooth manufacturing operations.

For example, in logistics, gen AI systems optimize transportation routes and schedules by factoring in variables like traffic, weather, and delivery deadlines.

Manufacturers use Internet-of-Things (IoT) sensors to get real-time information on these factors and perform in-depth data analysis through machine learning algorithms. Based on the analysis, generative AI models provide optimal route plans to minimize fuel consumption and improve delivery efficiency.

Generic Illustration of IoT-Powered supply-chain

The technique further allows manufacturing companies to identify opportunities to reduce their carbon footprint and minimize wastage for a more sustainable supply chain.

Gen AI technologies also help manage inventory by predicting when stock levels need replenishment and identifying potential supply chain disruptions. AI models help suggest alternative suppliers or routes when needed, ensuring that production lines run smoothly.

Digital Twin

A significant part of the digital transformation process includes using the digital twin technology to create simulations of processes, products, or systems. These digital models simulate real-world conditions, allowing manufacturers to monitor, analyze, and optimize performance in a risk-free virtual environment.

By incorporating data from sensors and IoT devices, gen AI frameworks can help create digital twins that virtually replicate physical entities. The system can then simulate multiple conditions and track real-time changes, providing valuable insights for making proactive adjustments.

Digital twins differ from traditional simulation techniques, which are static and only allow users to assess the impact of change in specific parameters. However, the scope of digital twins is more extensive, replicating a component, product, process, or entire system within a dynamic model environment.

One significant use case of a digital twin is in the aerospace industry, where companies build ML-based geometry optimization systems. By using gen AI to create virtual replicas of the system, they test the effect of different configurations on multiple performance metrics, such as an airplane’s speed, weight, and other aerodynamic features.

Digital Twin Visualization of the Human Heart

Another application is in the healthcare industry, where scientists create models of human organs to test the effects of multiple drugs on particular diseases. The technique helps them identify optimal treatment plans and opportunities to manufacture medicines with minimal side effects.

Warehouse Automation

Gen AI is driving significant advancements in warehouse automation by allowing manufacturers to use robotics, create smart warehouses with energy-efficient layouts, and improve predictive maintenance.

In modern warehouses, AI-powered robotic systems can autonomously pick, sort, and transport goods with high precision and speed. These robots can learn and optimize their movements to reduce time and energy consumption. 

Gen AI algorithms further enhance their efficiency by suggesting optimal paths and workflows based on real-time data, which minimizes bottlenecks and maximizes productivity.

Smart warehouses leverage gen AI to automate energy consumption and streamline operations. AI systems continuously track temperature levels and identify hotspots to suggest optimal layouts. The warehouses use data-driven simulations to optimize storage layouts and airflow, ensuring reduced cooling needs and faster retrieval of items with better space utilization.

Predictive maintenance is another crucial application in warehouse automation. AI models analyze data from machinery and anticipate equipment failures before they occur. This proactive approach minimizes downtime and extends the lifespan of critical assets.

Automated-Guided Vehicle (AGV)

For instance, companies deploying autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) use sensors to capture performance data. Gen AI models analyze the data to suggest maintenance schedules and potential failures.

Quality Inspection

Quality inspection is a crucial component in the manufacturing sector that ensures high-quality products without defects. While traditional inspection methods rely heavily on human vision, gen AI enhances inspection processes using AI-driven visual systems.

These systems use advanced computer vision (CV) algorithms to detect defects, inconsistencies, or irregularities in products with precision and speed that surpass human inspectors. Gen AI can identify even the slightest imperfections, such as surface scratches, misalignments, or incorrect assembly, by analyzing images or video feeds from production lines.

This makes the system highly effective in dynamic environments that require rigorous quality control, such as automotive, electronics, and pharmaceuticals.

Defect detection of automotive parts

For example, in the automotive sector, manufacturers use AI-powered visual inspection to check for defects in car components like body panels, welds, and paint jobs. Robust AI systems can detect flaws in real time, reducing the risk of recalls and enhancing vehicle safety.

Worker Safety and Training

Gen AI can help improve worker safety and create more effective training sprints in manufacturing environments by using advanced personal protective equipment (PPE) detection frameworks and collaborative robots (cobots).

PPE Detection

AI-powered PPE detection systems can monitor workers in real-time to ensure they wear the appropriate safety gear, such as helmets, gloves, and protective eyewear. Manufacturers can integrate the detection system with gen AI models to create compliance reports.

The system can also recommend improvements to increase PPE usage, reducing the risk of accidents and injuries on the factory floor.

Also, manufacturers can use cobots to help human workers become more efficient in their daily tasks. Unlike traditional industrial robots that operate in isolated areas, cobots can use gen AI to understand and predict human movements, adjusting their actions to ensure a secure work environment.

Safety Design Features of Cobot

For example, in automotive assembly lines, cobots assist workers by handling heavy parts or performing repetitive tasks, reducing physical strain and allowing human workers to focus on more complex activities. 

With gen AI, cobots can generate effective training plans tailored to each worker’s ability, skills, and experience to ensure faster and safer learning.

Patent Management

Filing and managing patents can be a hassle, as manufacturers have to navigate legal complexities, ambiguous portfolios, and extensive documentation.

Manufacturers can train gen AI models to analyze massive amounts of technical and legal documents. The model can then identify novel aspects of an invention and suggest the best way to articulate claims.

For example, companies can use AI systems to generate detailed patent drafts, highlighting potential areas for strengthening claims. This approach speeds up approvals and increases application success rates.

Customer and Supplier Interactions

Manufacturers must remain competitive by evaluating suppliers, acquiring new customers, and maintaining good relations with existing ones. However, the process includes extensive interactions, documentation, and market analysis to select reliable suppliers and increase the customer base.

Gen AI can improve supplier evaluation by analyzing vast datasets from different sources, such as user reviews, website content, and clientele information. It can automatically suggest the most appropriate suppliers and initiate conversations according to the manufacturer’s business needs.

Similarly, manufacturers can integrate customer relationship management (CRM) platforms with gen AI tools for detailed customer data analysis. The AI tools can craft suitable content to attract new customers and retain existing ones.

For instance, Gen AI-powered chatbots and virtual assistants can automatically contact relevant suppliers, asking for price quotes or demos. Similarly, the bots can create tailored marketing campaigns targeting specific customer segments for a higher conversion rate.

📌 Learn about the top 8 use cases of computer vision in manufacturing

Gen AI Implementation Challenges

Although gen AI offers significant benefits, implementing the technology is challenging. The list below mentions the most common hurdles manufacturers face and relevant mitigation strategies.

• Data Volume and Variety: Manufacturers must train gen AI models on a large volume of labeled data. However, acquiring a clean and curated dataset with diverse samples takes time and effort. One way to mitigate the issue is to use foundational models and fine-tune them to specific tasks.
• Data Security: Gen AI frameworks for monitoring workers and analyzing customer data involve significant security risks and privacy concerns. Manufacturers can overcome the issue by implementing encryption, anonymization, and secure access protocols.
• Integration: Integrating gen AI with legacy systems can be complex and costly. Using middleware solutions or APIs can bridge the gap between old and new systems.
• Computational Cost: Running gen AI models is computationally expensive as most use cases include processing extensive data volume in real time. However, manufacturers can leverage edge computing resources that process data then and there without requiring a centralized server.
• Model Accuracy and Maintenance: Real-world data is messy and can change abruptly, causing gen AI models to perform poorly in production. Manufacturers can develop alerts that notify them when models or data deviate from acceptable performance thresholds.
• Initial Investment: Implementing gen AI requires manufacturers to upgrade existing infrastructure and hire relevant staff skilled at developing gen AI frameworks. All these tasks can significantly strain a manufacturer’s budget. Techniques to overcome these budgetary constraints can include implementing gen AI in phases and training existing staff through available AI programs.

📌 Learn about the top 6 tools for managing computer vision data

Streamlining Manufacturing with Encord

Encord is a data development platform for annotating, curating, and monitoring large-scale datasets to train machine learning models, including generative AI. 

• Encord Annotate: Leverage SOTA model-assisted customizable labeling workflows and flexibly setup complex ontologies to efficiently and accurately label computer vision data for generative AI and manufacturing applications.
• Encord Active: Supports active learning pipelines for evaluating data quality and model performance.
• Index: Manage and curate data at scale to prepare high quality datasets for training and fine-tuning generative AI models.

Encord

Key Features

• Scalability: Encord can help you scale AI data pipelines by ingesting petabytes of  data. You can create multiple datasets to manage larger projects and upload up to 200,000 frames per video at a time.
• Functionality: The platform consists of multiple features to filter and slice datasets in a consolidated visual explorer and export for labeling in one click. It also supports deep search, filtering, and metadata analysis. You can also build nested relationship structures in your data schema to improve the quality of your model output.
• Ease-of-Use: Encord offers an easy-to-use, no-code UI with self-explanatory menu options and powerful search functionality for quick data discovery. Users can query large scale datasets in everyday language to search for images and use relevant filters for efficient data retrieval.
• Data Security: The platform is compliant with major regulatory frameworks, such as the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2 Type 1), AICPA SOC, and Health Insurance Portability and Accountability Act (HIPAA) standards. It also uses advanced encryption protocols to protect data privacy.
• Integrations: You can connect Encord with your native cloud storage platforms and programmatically control workflows using its Python SDK.

📌 Need to annotate vast datasets to train your generative AI model? Encord handles up to 500,000 images, ensuring your project can grow without limitations. Get Started with Encord

G2 Review

Encord has a rating of 4.8/5 based on 60 reviews. The most significant benefits that users highlight is the tool’s simplicity, intuitive interface, and several annotation options. 

However, they suggest a few improvement areas including more customization options for tool settings and faster model-assisted labeling for medical imagery.

Overall, Encord’s ease of setup and quick return on investments make it popular among data experts.

Gen AI Manufacturing Use Cases: Key Takeaways

As the manufacturing industry increasingly progresses toward digitization, gen AI will be crucial in determining manufacturers’ success in increasing profitability.

The list below highlights a few critical points regarding gen AI applications in manufacturing.

• Gen AI’s Significance in Manufacturing: Gen AI helps in product design, supply chain management, system simulations, warehouse automation, quality inspection, worker safety and training, patent applications, and customer/supplier interactions.
• Gen AI Implementation Challenges: The most significant challenges include getting sufficient labeled data, ensuring data security, integrating with existing infrastructure, high computational cost, maintaining model accuracy, and budgetary constraints.
• Using Encord for Automating Manufacturing Tasks: Encord’s end-to-end data curation, annotation, and validation pipelines can help you streamline computer vision (CV)-based manufacturing use cases.