AI Tools for Manufacturing: Use Cases, Tools to Know, & Benefits

Many see a future where robotics and machine learning are part of the process. We will cover the basics of AI for manufacturing, including:

• 4 AI manufacturing tools
• 6 use cases of manufacturing AI tools
• Benefits to using AI in manufacturing

4 AI manufacturing tools available right now

A number of manufacturing companies have already implemented AI technology in their daily operations. A few of the best AI manufacturing examples currently in use are as follows.

1. ClickUp AI

Best for: Project management and team collaboration

Originally, ClickUp AI was just a project management tool. However, the developers have added AI to enhance it.

Today, it is used by manufacturing companies as a manufacturing project planner. AI has improved productivity and quality control through automation of operations. Processing mapping tools can also link strategic discussion with AI-based execution.

2. DataRobot

Best for: End-to-end AI integration

DataRobot offers a full-lifecycle AI platform. It can do predictive maintenance, quality control and even coordinate the supply chain management. The result is improved efficiency and productivity, two main objectives for any management team.

3. Siemens Insights Hub

Best for: Industrial IoT and real-time factory optimization

MindSphere is Siemens’ cloud-based, open IoT operating system that connects machines and physical infrastructure to the digital world. It collects and analyzes large volumes of production data to enable tools like predictive maintenance, quality analytics, energy monitoring, and more.

4. IBM Watson IoT

Best for: Cognitive analytics and decision-making

IBM uses AI extensively in its own companies. However, they have also made products that use AI for their clients, including this nifty analytics and cognitive insight platform.

You can use their IoT for intelligent decision-making, supply chain optimization, and predictive maintenance. Because IBM Watson IoT uses algorithms to break down sensor data, it will also give insights into workflows.

6 use cases of AI tools in manufacturing

Besides robotics, AI has changed the course of manufacturing history in other ways. Here are the top six AI manufacturing examples.

1. Inventory management

AI offers a number of features that are hugely beneficial to inventory management, like:

• Real-time analytics and measurements
• Data enhanced software
• Accurate stock control
• Improved stock prediction
• Automated reordering processes

AI can understand and learn about how your stock changes and then make predictions about what needs to be updated. It can even notify suppliers for you, so you can maintain leaner inventories and minimize over- or under-stock situations.

2. Predictive maintenance

It’s no surprise that AI, which learns, processes, and predicts, is an impactful tool for predictive maintenance. Enhancing maintenance technology with AI tools for manufacturing can show when a machine or equipment is likely to fail and recommend services or preventive measures.

The benefit of the prediction is that you can schedule a planned service rather than an unexpected breakdown. There will be fewer disruptions, and the machines themselves will not undergo breakdown stress, which can increase their life span. Predictive maintenance supported by augmented reality ensures a more efficient manufacturing assembly line, with fewer chances for a total breakdown.

3. Generative designs

Generative design in the manufacturing industry occurs when engineers, designers, and AI come together to create solutions for product designs.

The designer can use the technology to input the design goals for the product, such as the material type, budget constraints, and space or weight limitations. The AI tool will take all that data and then output all the possible designs that meet those criteria.

Each solution will have pros and cons that the client or designer can review and adjust to accordingly. The process can then start from the beginning again with new parameters, or an end result will be finalized.

4. Cobots for product automation

Collaborative robots, or cobots, are the beginning of robotic automation being accessible for smaller and medium-sized companies. Cobots are usually given jobs that are potentially dangerous for humans or dirty.

5. Digital twins technology for virtual simulations

Another area in which AI is enhancing manufacturing is digital twins. A digital twin replicates a physical asset in a virtual environment, including its behavior, functionality and features. It uses a number of technologies, including AI.

Digital twin technology uses machine learning algorithms to process large quantities of sensor data and identify data patterns. Artificial intelligence and machine learning (AI/ML) provide data insights about performance optimization, maintenance, emissions outputs, and efficiencies.

AI comes in as it can analyze large amounts of sensor data from the digital twins. From the data, you can identify patterns and give insight into performance optimization.

6. Project management with enhanced AI

Project management technology has also evolved with AI. In manufacturing, where you have to stick to deadlines and have a number of different teams and departments collaborating, it makes sense for everyone to be on the same page.

AI has been introduced into project management by giving predictive timelines and notifications and automating certain tasks.

Benefits to using AI in manufacturing

Now that we have looked at how AI tools for manufacturing, let’s look at the benefits AI technology has brought. Here are a few of the top ones you can expect:

• Energy efficiency: Newer technology, including AI, must uphold the latest regulations for energy efficiency. However, AI can also optimize energy by analyzing data from various parts of the production process. It can suggest or even automate adjustments to reduce every consumption without compromising output quality.
• Enhanced safety: Machine learning can absorb large amounts of data to make predictions and modifications. This procedure can be applied to the manufacturing system and the worker’s environment. AI software can monitor workplace conditions and predict potential safety issues. It can propose safety protocols for all hazardous situations.
• Agility and scalability: Companies can respond quicker to changes in the market or customer demands due to AI’s ability to predict and sense changes in real time.

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