What is AI in Manufacturing? All You Need to Know

While AI is conquering the minds of business leaders, manufacturing companies need to catch up quickly. Implementing AI in manufacturing on a meaningful scale is a challenge—the manufacturing sector is extremely diverse, with unique processes at every factory, which can become an obstacle when trying to automate with holistic artificial intelligence tools.

And yet, the global AI in manufacturing market is valued at $3.2 billion and is forecasted to grow 7 times more by 2028. AI has the potential to transform the manufacturing industry completely and optimize manufacturing processes by accelerating production, improving the quality of goods, and decreasing downtime and expenses.

The demand is undoubtedly there, but AI solutions should be chosen and implemented smartly to support highly efficient, connected, and intelligent manufacturing operations. This article explains how AI is used in manufacturing market and gives tips on implementing AI in manufacturing for maximum efficiency.

What is AI in Manufacturing?

Artificial intelligence (AI) in manufacturing is the application of to automate tasks that were once performed by humans and optimize manufacturing processes with AI-driven decision-making.

AI's power largely stems from machine learning, deep learning neural networks, and other self-organizing systems that can independently learn from available data and experience. An AI system like these can quickly identify essential patterns in large datasets that human analysts would struggle to process.

For instance, an often-cited example of AI in manufacturing is AI quality control. Thanks to machine vision technology, an AI agent can evaluate the quality of the product and pass it on for shipment or discard. In this case, manufacturing artificial intelligence helps evaluate more goods in a shorter timeframe and ensures all the QA checks are trustworthy, streamlining this manufacturing process.

AI in manufacturing helps carry out manufacturing processes with more precision, fewer errors, and at a higher speed.

Use Cases of AI in Manufacturing

To better understand the value AI in manufacturing brings, let’s explore some of the most common use cases of smart manufacturing that companies are currently implementing.

Continuous operations

AI enables real-time monitoring of equipment data to identify any equipment operating in an incorrect manner quickly. This allows plant personnel to make immediate adjustments, preventing downtime and ensuring consistent product quality.

Maintenance assistance

AI acts as a maintenance companion by digitizing paper manuals and providing real-time, step-by-step instructions tailored to the specific issue at hand. This streamlines maintenance tasks and reduces human error. Moreover, AI enabled predictive maintenance, ensuring your equipment health before any problems arise.

Defect detection and inspection

AI-powered visual inspection systems augment or replace human inspectors, increasing accuracy and reducing the time needed for inspections. These systems can analyze sensor data from sources like vibration sensors, thermal imaging, and oil analysis to assess machinery health, predict downtime, and optimize spare parts provisioning, leading to significant cost savings.

AI insights also allow manufacturing engineers to accurately provision spare parts and consumables, as well as predict downtime, improving production planning and related activities. This greatly diminishes maintenance costs, helping your company save resources.

Supply chain management

Artificial intelligence, powered by machine learning and Big Data analytics, enables autonomous planning in supply chains. This continuous, closed-loop, fully automated planning maintains supply chain performance even under volatile conditions. AI applications can optimize complex manufacturing schedules, maximizing throughput while minimizing costs.

For example, Lenovo utilized artificial intelligence to establish an overall supply chain control tower to synergize all functions (plan, source, make, deliver, and quality control) in one integrated platform. This Supply Chain Intelligence assists managers in making decisions through what-if scenario simulation.

Automated quality checks

AI assists testing and quality control by using image recognition, computer vision, and sensor data to detect equipment damage and product defects automatically. It can also analyze production data to identify patterns and suggest areas for improvement, reducing the need for rework and lowering scrap rates.

Cobots

AI powers cobots (collaborative robots) that, unlike traditional robots that need to be isolated from human workers, work alongside them, performing tasks such as picking parts, operating machinery, and conducting quality inspections. AI-driven machine vision is crucial for enabling cobots to execute these tasks efficiently, enhancing overall shop floor productivity.

Product design

Generative AI can enhance product development by analyzing market trends, regulatory changes, and customer feedback. This allows designers to innovate and customize products more effectively, ensuring compliance and meeting customer needs. Moreover, generative AI can help build prototypes and create drafts that could be developed into full-fledged product ideas.

For instance, PIX Moving uses generative design software in Fusion 360, robotics, and 3D printing to create a decentralized way of automotive manufacturing. AI-powered generative design helps optimize the chassis design to suit each customer’s application and different shape or physical requirements.

Operational optimization

AI can optimize complex assembly lines by addressing challenges like worker scheduling, varying production rates, and buffer management. It also boosts worker productivity and safety by recommending next-best actions and alerting workers to potential hazards.

Automated administration

AI can eliminate repetitive administrative tasks by automating document management and approval processes, serving as a virtual assistant to your company’s operational and financial departments.

Optimizing resource consumption

AI is used to optimize energy usage in manufacturing and finding sustainable solutions. For example, Doosan Enerbility Steel Mill used AI to predict the volume of molten steel needed in steel mills to ensure efficient energy consumption, even when using varied raw materials.

Digital Twin technology

AI-powered digital twins simulate production cycles to identify potential quality issues or performance gaps, enhancing factory floors performance.

AI for IT Operations (AIOps)

AIOps leverages big data and machine learning to automate IT operations, including data management, event correlation, performance analysis, and anomaly detection. This helps maintain optimal IT infrastructure, crucial for modern manufacturing environments.

6 Benefits of AI in Manufacturing

More transparency into your operations

AI-driven real-time monitoring and data processing give workers greater visibility into production processes, enabling them to swiftly address delays or errors.

Moreover, high-level decision-makers can constantly monitor the business, enabling them to make informed, data-driven decisions that optimize the company's performance and efficiency.

Safe and efficient production

AI fosters a safer and more efficient manufacturing environment by consistently monitoring manufacturing processes.

AI can detect potential hazards, such as equipment malfunctions or unsafe working conditions, and alert workers in real-time to prevent accidents. AI-powered systems can also optimize floor operations by analyzing workflows and identifying areas where safety protocols can be enhanced or where bottlenecks may occur, reducing the risk of injury and ensuring a smoother production process.

Furthermore, AI can integrate with wearable technology to provide personalized safety alerts and guidance to workers, further enhancing their safety on the job. This holistic approach not only safeguards employees but also boosts operational efficiency, minimizing downtime and maximizing productivity.

Enhanced product quality

AI enhances product quality in several ways:

• Automated quality assurance systems powered by AI can detect defects and inconsistencies with greater precision and speed than traditional methods, ensuring that each product meets rigorous standards before it reaches the market.
• Generative design, an AI-driven approach, enables manufacturers to explore countless design variations and optimize for performance, cost, and materials.
• AI's ability to process and analyze vast amounts of customer feedback provides valuable insights into consumer preferences and pain points. This continuous feedback loop, powered by AI, ensures that products are of the highest quality and aligned with evolving customer needs and expectations.

Streamlined innovation

AI enables mass customization by analyzing customer preferences and tailoring products to meet specific needs. It also fosters innovation by providing insights into market trends based on data analysis.

Increased efficiency

AI optimizes production processes by automating repetitive tasks, reducing cycle times, and minimizing waste. This leads to faster production rates and more efficient resource use.

Cost reduction

Through the above-mentioned efficiency gains, reduced waste, and optimized manufacturing processes, AI helps manufacturers significantly lower operational costs.

Challenges of Implementing AI in Manufacturing

While the benefits of AI in manufacturing are clear, the industry has been slow to adopt it on a large scale due to several unique challenges.

The diversity of data in the manufacturing industry

One of the biggest obstacles to AI adoption is the absence of standardized, universal data across the manufacturing industry. Unlike sectors like finance or retail, where data such as transaction records or stock market trends is abundant and transferable, manufacturing data is often localized and specific to particular operations or manufacturing industry domains. This fragmentation makes it difficult to build reliable vertical AI models, as there isn't enough relevant, high-quality data available across the board.

Requirements for customization

The diversity of manufacturing industry also affects AI application. Unlike more uniform industries, where AI solutions can be more easily standardized, manufacturing AI often needs to be adapted to very specific use cases. This customization is resource-intensive and time-consuming, making it difficult for companies to scale AI solutions across different operations or facilities.

The quality and availability of training data

AI algorithms require large datasets of clean, accurate, and structured data to function effectively. However, many manufacturers struggle to provide such data, often relying on small, fragmented, inconsistent, or poor-quality data sets.

These limitations result in suboptimal AI performance, as the models cannot learn effectively or make accurate predictions. Even when large datasets are available, they may not be easily consumable by AI models due to inconsistencies or lack of proper formatting, further complicating the implementation process.

Security vulnerabilities

Manufacturing companies often deal with highly sensitive information, such as product designs, trade secrets, and proprietary processes. If this data is compromised, it could lead to the theft of intellectual property, giving competitors an unfair advantage and potentially causing significant financial losses. Thus, any AI solution that gets access to sensitive data needs to guarantee the highest security standards.

AI models themselves can be targets of attacks, such as adversarial attacks, where attackers manipulate input data to cause AI systems to make incorrect decisions. Protecting AI models from such vulnerabilities is essential for maintaining the reliability of AI-driven manufacturing processes.

Besides, manufacturers must comply with various data protection regulations, such as GDPR in Europe or CCPA in the United States. Failing to secure data properly could result in legal penalties and damage to a company's reputation.

5 Key Steps for Integrating AI into Manufacturing

To overcome the challenges mentioned, it's essential to implement AI in manufacturing strategically. Here are some tips to help guide the process:

1. Ensure data quality

AI relies heavily on accurate, clean, and consistent data. Since manufacturing use cases tend to be more individualized and yield lower returns compared to other industries, providing high-quality data is crucial. Establish proper data management processes and invest time in gathering, cleaning, and organizing your data prior AI implementation to ensure that your AI models can perform optimally.

2. Choose the right tools

Instead of building custom AI solutions from scratch, consider leveraging data-centric vertical AI platforms designed for specific manufacturing needs. For instance, automated anomaly detection tools can enhance or even replace human workers in quality control, leading to more efficient processes. Additionally, AI-powered workflow automation tools, like Creatio, can streamline operational tasks and assist with administrative processes, further boosting productivity.

Given that manufacturing often demands extensive customization, it’s worth investing in AI-powered no-code platforms. These platforms enable you to create new LLMs and automate manufacturing processes without the need for coding expertise.

3. Ensure security and compliance

Data security is paramount in AI implementation. Make sure that your AI systems are secure and comply with industry regulations. Protect sensitive data and ensure that your AI practices adhere to legal and ethical standards to avoid potential risks and penalties.

4. Train your staff

AI will create new roles and require new skills within your organization. Invest in training your staff to work alongside AI systems, ensuring they understand how to manage and interpret AI-generated insights. This may include forming new teams to oversee data quality, monitor automation results, and adapt to new operating models. Organizational redesign may be necessary to fully integrate AI into your operations.

5. Start with targeted experimentation

Begin by applying AI to specific, targeted initiatives where you can quickly learn, develop skills, and demonstrate early wins. Focus on projects with clear ROI potential to build organizational momentum and gain buy-in from stakeholders. Experimentation is key to understanding how AI can best be used in your manufacturing processes.

The Future of AI in Manufacturing

AI in manufacturing is bound to revolutionize the industry.

In the near term, AI will play a crucial role in predictive maintenance by monitoring machining processes in real time. Sensors will continuously collect data on tool wear, acoustic signals, and other machine status inputs, which AI algorithms will analyze to predict potential issues. This proactive approach will allow maintenance teams to address problems before they lead to costly downtime.

AI will also significantly impact additive manufacturing, a growing area on the shop floor. With the integration of new sensors, AI can monitor conditions that affect materials and fabrication processes, ensuring higher quality and consistency in production. This system could enable manufacturers to compare parts produced on different days, analyze quality assurance data, and identify defects early in the process, leading to improved product quality.

Looking further ahead, the concept of a "factory in a box" could become a reality, where AI-driven systems handle most of the production autonomously, requiring human intervention primarily for maintenance. These self-correcting machines will adapt to wear and tear, maintaining performance while advising on component replacements.

Beyond the production process, AI will also influence factory planning and layout optimization. By using sensors to track and measure potential space and material conflicts, AI can help optimize factory layouts to improve safety and efficiency, especially in environments that frequently change or handle short-run projects.

AI in manufacturing will bring about more intelligent, adaptable, and efficient production processes, leading to a new era of innovation and productivity in the industry.

Harnessing the Power of AI with Creatio

If implementing AI on a large scale in your manufacturing process seems overwhelming, consider starting with workflow automation tools that come with pre-packaged, ready-to-use AI capabilities.

Creatio's no-code AI-powered platform allows you to automate any administrative manufacturing process, including partner relationships management, quote management, order processing, product lifecycle management, supply chain management, inventory management, and procurement. Additionally, Creatio's AI supports compliance by monitoring and ensuring adherence to regulatory requirements.

Creatio's dashboard for supply chain management

As a versatile no-code platform for CRM and workflow automation, Creatio allows you to create custom business automation solutions using pre-built components like trigger actions, data fields, forms, and buttons.

AI-powered app creation in Creatio

This high level of customization extends to Creatio's AI tools, enabling you to build and train machine learning models on your database for tailored AI automation. This ensures that your AI solutions and machine learning algorithms are specifically aligned with your manufacturing processes and remain secure from external interference.

Creatio offers an AI assistant, Creatio Copilot, which can create new business applications based on natural language requests. For example, you can simply ask Copilot to build an application to manage internal audit processes for your financial and legal teams and Copilot will generate a prototype in seconds.

Building an automated workflow with Creatio Copilot

Creatio’s Copilot also enhances customer engagement and strengthens long-term partner relationships by delivering highly personalized value propositions. It leverages predictive scoring, AI/ML-driven next-best-offer intelligence, and interaction history to provide tailored recommendations.

In addition, Creatio's Marketplace offers various connectors with AI tools to optimize administrative processes.

By streamlining and accelerating routine tasks, Creatio frees up resources, allowing your company to focus on growth.

Transform your manufacturing operations with Creatio's cutting-edge AI technologies

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In Conclusion

In conclusion, the integration of AI in manufacturing is not about replacing human workers but about enhancing their capabilities. By enabling seamless collaboration between robots and personnel, AI empowers teams to achieve greater efficiency, precision, and innovation in their tasks.

As manufacturers adopt AI-driven solutions, they can expect to see significant improvements in productivity, quality control, and operational safety. By strategically implementing AI, companies can not only streamline processes and reduce costs but also create new opportunities for growth and development.