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  • Writer's pictureMegan Willing

AI in Manufacturing: What You Should Know

Updated: Apr 24, 2023



Artificial Intelligence (AI) has revolutionized manufacturing in recent years, and its impact is only set to grow in the coming years. However, there are still some misconceptions about AI in manufacturing, and many little-known facts that are worth exploring. In this blog post, we will delve into both. But before we get into that, let's first clarify the difference between data analytics and AI.


While data analytics is a valuable tool in manufacturing, it differs from AI in its approach. Data analytics involves the use of statistical and mathematical methods to analyze large amounts of data and extract insights. On the other hand, AI involves the use of algorithms, like neural networks and transformers, to learn from data and make predictions or decisions. Machine vision systems are another example of AI in manufacturing, as they use algorithms to interpret visual data and make decisions based on that information.


Little Known Facts about AI in Manufacturing


AI has been used in manufacturing for over a decade


AI has been used in manufacturing for over a decade, and its impact has been significant. Machine learning algorithms have been used to optimize cutting paths in CNC machines, reducing waste and improving efficiency. The use of AI in manufacturing has increased over time and continues with new technology being developed often.


Inspection is one area where AI is making a big impact. Machine vision systems are being used to inspect products on production lines and detect defects more accurately and efficiently than humans could. These systems use AI algorithms to analyze visual data and identify patterns that indicate defects, making it possible to catch problems that might otherwise go unnoticed. As a result, the use of machine vision systems can help improve product quality and reduce waste.


In addition to machine vision systems, AI is also being used to optimize manufacturing processes. Traditional linear programming-based optimizers are being upgraded with newer AI algorithms like neural networks, which can learn from data and make more accurate predictions. This can help manufacturers optimize production schedules, reduce downtime, and improve efficiency.


Here are some examples of how AI has been used in a wide variety of applications in manufacturing:


  1. Predictive Maintenance: AI can be used to predict when equipment is likely to fail, allowing for maintenance to be scheduled in advance. This can reduce downtime and improve productivity. For example, a company may use AI to predict when gas turbines are likely to fail, allowing for maintenance to be scheduled in advance.

  2. Quality Control: AI can be used to identify defects in products, reducing the likelihood of defective products reaching the market. For example, a company that sells computers and other IoT devices may use AI to identify defects in its microprocessors, reducing the likelihood of faulty products being shipped to customers.

  3. Supply Chain Optimization: AI can be used to optimize the supply chain, improving efficiency and reducing costs. For example, a retailer may use AI to optimize its supply chain, reducing the amount of time it takes to get products from suppliers to stores.

  4. Assembly Line Automation: AI-powered robots can be used to automate tasks on assembly lines, reducing the need for human workers and improving efficiency. For example, A car manufacturer may use AI-powered robots to assemble car doors on its production lines.

  5. Inventory Management: AI can be used to optimize inventory levels, reducing waste and improving cash flow. For example, an online retailer may use AI to optimize inventory levels in its warehouses, ensuring that products are always in stock when customers need them.


As AI continues to evolve and become more sophisticated, we can expect to see even more applications of this technology in the manufacturing industry.


AI can help reduce waste


Reducing waste is one of the biggest challenges in manufacturing, and AI is capable of playing a vital role in addressing this challenge. With most machines in service today coming with programmable logic controllers (PLCs), it is easier to collect data and set up data pipelines for analysis. By leveraging AI and data visualization reporting technologies, it becomes easier to gain actionable insights from this data.


AI can analyze data from sensors and other sources to identify inefficiencies and areas for improvement. For example, AI can analyze data from a production line to identify bottlenecks and suggest changes to the process that can reduce waste and improve efficiency. By identifying where waste is being generated, manufacturers can take steps to eliminate it and optimize their production processes.


AI can also be used to optimize inventory levels, reducing waste and improving cash flow. By analyzing data on customer demand, suppliers, and production schedules, AI algorithms can help manufacturers optimize their inventory levels to avoid excess inventory or stockouts. This can help reduce waste by minimizing the amount of excess inventory that must be discarded and improve cash flow by reducing the amount of capital tied up in inventory.


Here are some examples of how AI can help reduce waste in manufacturing:


  1. Process Optimization: AI can analyze data from sensors and other sources to identify bottlenecks and inefficiencies in the manufacturing process. By identifying these issues, companies can make changes to the process that reduce waste and improve efficiency. For example, some manufacturers use AI to optimize its production lines, reducing waste and improving efficiency.

  2. Energy Management: AI can be used to optimize energy usage in manufacturing plants, reducing waste and improving efficiency. For example, an electronics company may use AI to optimize energy usage in its manufacturing plants, reducing energy costs and greenhouse gas emissions.

  3. Predictive Maintenance: As mentioned earlier, AI can be used to predict when equipment is likely to fail, allowing for maintenance to be scheduled in advance. By reducing the likelihood of unexpected breakdowns, companies can reduce waste and improve efficiency.

  4. Supply Chain Optimization: AI can be used to optimize the supply chain, reducing waste and improving efficiency. For example, AI can be used to predict demand for products, ensuring that inventory levels are optimized and waste is minimized. AI can also be used to optimize shipping routes and reduce the amount of time that products are in transit.

  5. Quality Control: AI can be used to identify defects in products, reducing the likelihood of defective products reaching the market. By reducing the number of defective products, companies can reduce waste and improve efficiency.


By leveraging the power of AI to identify inefficiencies and areas for improvement, companies can reduce waste and improve efficiency, leading to increased profitability and sustainability.


AI can be used to design products


AI can be used to design products in various ways, from identifying product features that are likely to be popular to automatically generating designs. One active area of research and development is leveraging AI to send production data back into the design phase to create better simulation models. By using AI to drive design decisions and make it easier to Design for Manufacturing (DFM), manufacturers can reduce the time and cost required to design new products and improve their performance.


AI can analyze customer data and market trends to identify product features that are likely to be popular, reducing the risk of failure and improving profitability. However, AI can also be used to optimize the design of products for manufacturing, which is particularly important in industries where the cost and complexity of manufacturing can be a significant factor.


With the help of AI, manufacturers can identify potential issues in the production process and use this information to create more accurate simulation models. By incorporating data from sensors and other sources, these models can provide more detailed information about how a product will perform in real-world conditions, allowing manufacturers to make more informed design decisions.

Here are some examples of how AI can be used to design products:

  1. Personalization: AI can be used to personalize products based on individual customer preferences. For example, an athletic wear and shoe company uses AI to design customized running shoes for its customers, using data from a customer's gait and foot shape to create a shoe that fits perfectly.

  2. Forecasting: AI can be used to forecast demand for products, allowing companies to design products that are likely to sell well. For example, clothing companies may use AI to predict which clothing items will be popular each season, allowing the company to design and manufacture products that are likely to sell well.

  3. Aesthetic Design: AI can be used to generate designs automatically, reducing the time and cost required to design new products. For example, a business may use AI to generate design options for its customers, allowing them to choose the best option for their needs.

  4. Material Selection: AI can be used to select the best materials for a product based on its intended use and other factors. For example, a company that manufactures planes uses AI to select the best materials for its aircraft, taking into account factors such as weight, strength, and cost.

  5. Cost Optimization: AI can be used to optimize the cost of a product by identifying areas where costs can be reduced without sacrificing quality. For example, a company known for electronics and appliances may use AI to optimize the design of its gas turbines, reducing the cost of manufacturing without sacrificing performance.

By leveraging the power of AI to analyze data and identify product features that are likely to be successful, companies can reduce the risk of failure and improve profitability.


Common Misconceptions about AI in Manufacturing


AI will replace human workers


One of the biggest misconceptions about AI in manufacturing is that it will completely replace human workers. While it is true that AI can automate many tasks that were previously done by humans, it is more likely that AI will work alongside human workers as a "copilot". By augmenting human workers, AI can help to improve their productivity and efficiency, leading to greater value creation for both the worker and the company.


AI is often seen as a threat to human workers, with concerns that machines will replace human jobs. However, the reality is that AI can work alongside humans, augmenting their abilities and improving productivity. Here are some ways in which humans and AI can work together:

  1. Improved Productivity: AI can automate repetitive and time-consuming tasks, allowing human workers to focus on more complex tasks that require creativity and critical thinking. For example, an AI-powered robot can be used to assemble products on a production line, freeing up human workers to perform more complex tasks such as quality control.

  2. Decision Support: AI can be used to provide human workers with decision support, providing them with data and insights that help them make better decisions. For example, an AI system can analyze data from a production line to identify areas where improvements can be made, providing human workers with recommendations on how to optimize the process.

  3. Safety: AI can be used to improve safety in the workplace by monitoring human workers and identifying potential hazards. For example, an AI system can monitor a construction site for safety hazards and alert human workers if there is a potential danger.

  4. Training: AI can be used to train human workers, providing them with simulations and other tools that help them develop new skills. For example, an AI-powered virtual reality system can be used to train workers on how to operate complex machinery safely and efficiently.

  5. Customer Service: AI can be used to improve customer service by providing human workers with data and insights that help them provide better service to customers. For example, an AI system can analyze customer data to identify patterns and trends, providing human workers with recommendations on how to improve customer satisfaction.

These are just a few examples of how humans and AI can work together to improve productivity, safety, and customer satisfaction. By leveraging the strengths of both humans and machines, companies can create a more efficient and productive workplace that benefits everyone involved.


Furthermore, AI can provide workers with real-time data and insights that can help them to make better decisions and improve their performance. For example, AI can analyze data from sensors and other sources to provide workers with information about machine performance, which can help them to identify potential issues and take preventative action.


AI is expensive and difficult to implement


Another common misconception about AI in manufacturing is that it is expensive and difficult to implement. While implementing AI can be a significant investment, Model-as-a-Services like GPT and Microsoft Azure Cognitive Services significantly reduce the cost of integrating complicated AI models for manufacturing scenarios. These services offer pre-built AI models and APIs that can be easily integrated into existing manufacturing processes, without requiring significant customization or expertise in AI development.


Moreover, new tools and technologies have made it easier and more efficient for AI software developers to create and deploy these models. This has increased the productivity of developers and lowered the cost of implementing AI solutions in manufacturing operations.


While there may be some upfront costs associated with implementing AI in manufacturing, the availability of Model-as-a-Services and the increasing ease of development and deployment have significantly reduced the barriers to entry. This has made it easier and more affordable for manufacturers to implement AI and reap the benefits of increased efficiency, reduced waste, and improved profitability.


While it is true that implementing AI can be a significant investment, the benefits of AI can outweigh the costs. In addition, there are many off-the-shelf AI solutions that are easy to implement and can provide immediate benefits. Here are some examples of how this misconception can be debunked:

  1. AIaaS (AI as a Service): There are many cloud-based AI services available that allow companies to implement AI without having to invest in expensive hardware and software. For example, a company may offer an AI platform that provides pre-built models and tools for building custom models.

  2. Plug-and-Play AI: There are many off-the-shelf AI solutions available that can be easily integrated into existing manufacturing processes. For example, Cognex offers an AI-powered vision system called In-Sight that can be used for quality control and inspection.

  3. Low-Cost Hardware: The cost of hardware required for AI has decreased significantly in recent years, making it more affordable for companies to implement. For example, NVIDIA offers a low-cost AI development kit called the Jetson Nano, which provides the hardware and software needed to develop AI applications.

  4. DIY AI: There are many open-source AI frameworks and tools available that allow companies to develop their own AI applications without having to invest in expensive software or services. For example, an open-source machine learning framework can be used to develop custom AI applications.

  5. ROI: The return on investment (ROI) for AI can be significant, making it a worthwhile investment for companies. For example, AI can be used to optimize the design of gas turbines, reducing the annual cost of manufacturing.

By leveraging cloud-based services, off-the-shelf solutions, low-cost hardware, open-source tools, and focusing on ROI, companies can implement AI solutions that provide immediate benefits and contribute to long-term success.


AI is only for large manufacturers


It is a common misconception that AI is only for large manufacturers. While some AI applications may be more beneficial for larger manufacturers, there are many AI solutions that can benefit small and medium-sized manufacturers as well. However, it is important to note that the adoption of AI-based manufacturing technologies requires a significant cultural change. Larger organizations may face more hurdles in promoting a culture of AI "copilots" in their operations.


Despite these challenges, AI can be used to optimize scheduling, improve quality control, and reduce waste, all of which can benefit manufacturers of all sizes. By automating routine and repetitive tasks, AI can help small and medium-sized manufacturers become more competitive and efficient, improving their profitability and bottom line.


Here are some examples of how AI can be utilized by smaller manufacturers:

  1. Predictive Maintenance: Small manufacturers can use AI to predict when equipment is likely to fail, allowing for maintenance to be scheduled in advance. This can reduce downtime and improve productivity, regardless of the size of the manufacturing operation.

  2. Quality Control: AI can be used to identify defects in products, reducing the likelihood of defective products reaching the market. This is important for small manufacturers who may have limited resources for quality control.

  3. Inventory Management: AI can be used to optimize inventory levels, reducing waste and improving cash flow. This is important for small manufacturers who may have limited resources for inventory management.

  4. Scheduling: AI can be used to optimize scheduling, ensuring that production runs smoothly and efficiently. This is important for small manufacturers who may have limited resources for scheduling.

  5. Sales Forecasting: AI can be used to predict demand for products, ensuring that small manufacturers have the right inventory levels and production capacity to meet customer demand.

  6. Customer Service: AI can be used to improve customer service by providing insights into customer behavior and preferences. This is important for small manufacturers who may have limited resources for customer service.

AI is not just for large manufacturers, and companies of all sizes can benefit from this transformative technology.


Overall, it is important for manufacturers of all sizes to consider the benefits of AI and work towards adopting it in their operations. While there may be cultural and technological hurdles to overcome, the potential benefits in terms of increased efficiency, reduced waste, and improved profitability make it a worthwhile investment. Conclusion


In conclusion, AI is transforming manufacturing, and its impact is set to grow in the coming years. While there are still many little-known facts and misconceptions about AI in the industry, understanding the potential benefits and limitations of AI can help companies make informed decisions about how to leverage this technology to improve their operations and profitability.


From reducing waste to improving efficiency, AI has many applications in manufacturing that can benefit companies of all sizes. However, it is important to consider the cultural and technological changes required to adopt AI-based manufacturing technologies, especially for larger organizations.


With the decreasing cost of computing and storage, and the availability of Model-as-a-Services and off-the-shelf AI solutions, it has become easier and more affordable for manufacturers to implement AI and reap the benefits of increased efficiency, reduced waste, and improved profitability.


By embracing AI, manufacturers can stay competitive and become more efficient, reducing their environmental impact and improving their bottom line. As AI technology continues to evolve and become more accessible, it is important for companies to stay informed about the latest advancements and opportunities in this field.






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