Digital Strategy

The Essential Guide to AI in Manufacturing

March 23, 2021

Implementing AI in manufacturing processes now is a competitive advantage, but in five years, it may be a necessity for a manufacturer's survival. Here's what you should know.

Artificial intelligence is popping up in industries from marketing to healthcare. The manufacturing sector is no exception; the application of AI is growing rapidly and businesses who have adopted it are seeing demonstrable improvements in their processes.

While artificial intelligence is a not-so-new concept, its use in manufacturing is in its fledgling stages. Small- and medium-sized manufacturing companies need to examine the potential of implementing AI in their own processes because the opportunity cost may be large in the coming years. Through 2026, the value of AI use in manufacturing is expected to increase over 57%, according to research by Markets and Markets. 

Implementing AI in manufacturing processes now is simply a competitive advantage; in five years, it may be a necessity for a manufacturer’s survival.

How is AI used in the Manufacturing Sector?

To understand how artificial intelligence can be used in the manufacturing industry, it’s important to first understand what AI really entails. Simply put, AI is a type of technology that takes big data and uses an algorithm to generate a desired outcome for the user. 

To reach that end goal, though, takes training. Either an AI expert or another algorithm will evaluate the output of the AI to see if the result matches the desired specifications. The AI is alerted to which outputs fit the desired results and uses that “knowledge” to repeat the process, which is known as machine learning. As time goes on, less human intervention is needed because the output becomes more accurate to the required specifications.

Artificial intelligence has a high potential for use in manufacturing and is already applied in ways that improve efficiency and quality. A good way to understand how AI is and can be used in manufacturing is to examine what “smart manufacturing” would look like.

In a smart factory, physical production processes are combined with digital and robotic technology. The result is a tightly run operation that maximizes worker input and streamlines supply chain management.

A smart factory requires more than just automation, which is a popularly applied manufacturing technique in modern plants. However, automation does not necessarily mean that AI is being used. Even automated processes are disconnected and humans must intervene to bridge the gap. Combining automated processes with artificial intelligence and machine learning eliminates the gaps in processes, allowing workers to use their time more effectively.

Artificial intelligence, unlike automation, gives the opportunity to use real-time data to affect the manufacturing process. Sensors and monitoring devices, for example, can turn images or observations into data that can be compared to existing data to ensure operations are on track.

5 Ways AI is Transforming the Manufacturing Industry

For a deeper understanding of the use of AI in manufacturing, consider the following activities that are being transformed by its application.

  • Streamlining quality control measures: Artificial intelligence can be used to report inconsistencies in the production process in real time, allowing issues to be addressed immediately, saving time, materials and labor.
    AI use in manufacturing also results in error reduction. Manual tasks performed by humans are inherently more susceptible to errors. Leveraging AI in the evaluation or even performance of manual tasks cuts down on costly errors.
  • Reducing unplanned downtime: Artificial intelligence is capable of detecting when parts or machinery are wearing out and are nearing the end of their use in the production process. Alerting operators to the status of the machinery helps predict when preventative maintenance will need to be performed and order replacement parts ahead of time, thus reducing downtime. This technique is known as predictive maintenance and is a major component of AI use in the manufacturing sector. Further, monitoring machinery ensures consistency in the product quality that is coming off the production line.
  • Improving demand forecasting: AI-based systems synthesize data from as many business activities as are made available to them. With AI, manufacturers can connect data from sales, industry data, factory output and more to better forecast demand for the manufacturer. The manufacturer can then better manage their supply chain and inventory to meet demand accurately and reduce waste.
  • Revamping the design process: AI is being used by manufacturers in their design processes. One of the major applications is with generative design. In generative design, the AI application generates output, in this case, the designs, based on criteria set by the user. The AI learns through each iteration of output which designs work and which do not work for the product it’s being asked to design. As the AI solution delivers more iterations, it “learns” based on user-given feedback and the designs gradually become more and more usable.
  • Mitigating environmental impact: Manufacturers are always on the lookout for ways to lessen their carbon footprint because it provides a competitive edge and ensures the sustainability of the company. While AI can be used to improve efficient energy use, it’s important to understand that the AI must be trained and used correctly. That is, training AI is a process that consumes a lot of energy and can, at first, appear less efficient. However, if AI training and implementation are done well, the result is a significant decrease in environmental impact. AI can be used to design more efficient systems, spot where waste is being generated and make sure raw materials are being used as effectively as possible.

4 Use Cases for AI in Manufacturing

BMW

BMW is implementing AI in their quality control processes in their plants. As production progresses, AI technology gathers component images and evaluates them in real time. If the data presented in the component images contains a deviation from available data or there is no related data known to the program, the plant’s inspection team is notified and can take a look to ensure the product is coming along as it should be. This system reduces the time needed for quality inspection and increases overall throughput.

Schneider Electric

Schneider Electric is working toward worker safety, sustainability and cost reduction goals with a solution that applies the Internet of Things, or IoT. The energy firm partnered with Microsoft Azure, a cloud computing software with open source capabilities, on the project. With machine learning in automated machinery, Schneider Electric aims to make their oil and gas wells safer and more efficient.

Nissan

Nissan spearheaded the application of generative design in the mid-2010s. The program is used not only to design entirely new car models, but also to update and extend the lifespan of existing models. Generative design algorithms are applied with parameters set on industry compliance and regulatory requirements, giving Nissan a leg up on sustainability and reducing time-to-market for new models.

Nokia partnering with Telia and Intel

Nokia worked with Telia and Intel to create the world’s first 5G smart manufacturing factory in Oulu, Finland. The telecommunications company converted the entire factory process to raw data that was used to train AI to monitor daily operations, from robotic components to human-performed manual tasks. The program works with machine operators to notify them of inconsistencies in processes, machinery and output to maintain consistency and check for necessary maintenance before the machine needs to be shut down.

4 Tips for Implementing AI in Manufacturing

  1. If you don’t have staff AI expertise, third-party vendors can help you tackle implementation.
    Teams of AI experts are available worldwide and are dedicated to helping manufacturers apply AI in their daily operations. Look to local startups or trusted multinational consulting firms like McKinsey for their assistance in a major project.
  2. Start small.
    Whether your manufacturing business hires a third party or manages the project in-house, start with small goals to gauge how the overarching implementation will progress. Set a goal to solve a single specific problem that is measured with KPIs. Then, the next steps in the overall application of AI in your manufacturing process will become clearer. You don’t need to become a smart factory yourself to make the most of AI technology.
  3. If your business is scaled to handle the project in-house:
    Hire project managers and experts in AI and data science if none are immediately available. Keep the experts in your manufacturing process, like high-level engineers and operations managers, directly involved in the implementation project. 
  4. Leverage human intelligence, too. There may be mounds of data immediately available from machinery on the assembly line, but human operators and shop floor workers know the process with nuance that even the most advanced AI can’t understand. Keep workers abreast of the project and ask for their input on which problems should be solved with AI.

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Author

Kathryn Kosmides

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