How AI improves productivity in manufacturing companies?

The fourth industrial revolution has begun, and manufacturing companies are competing to gain a competitive advantage by incorporating AI-driven technologies in their manufacturing processes. In fact, 14.6% of companies have already deployed AI capabilities in their production processes, with another 31% planning to implement the technology in the next five years [1][2].

Artificial intelligence capabilities like machine learning, predictive analytics, robotics, and automation can optimize operations and streamline processes at every level of the value chain across the manufacturing sector.

Below, we will explore the role of AI in the manufacturing industry, particularly its impact on productivity and the benefits it provides to the industry as a whole.

The role of artificial intelligence in the manufacturing industry

Manufacturing companies face numerous bottlenecks in production, distribution, and marketing. In a bid to solve these issues, many companies have embraced artificial intelligence as part of their digital transformation. A McKinsey report predicts that the competitive advantage gap between companies leveraging AI capabilities in their production processes will grow significantly over time [3].

Here is a brief look at the role of AI in the manufacturing industry and how it is streamlining operations throughout the production process.

Manufacturing process automation

The manufacturing industry has been around since the industrial revolution of the 19th century [4]. Up until recently, companies in the sector have relied solely on human employees to handle production operations, including calibrating and maintaining machinery, monitoring multiple signals from different machinery, and even checking the finished products for defects.

Although this has worked, for the most part, introducing a human element in such intricate processes is bound to create several issues arising from human error and negligence from taking shortcuts. The result is often frequent equipment failure and labor shortages due to difficulty finding a skilled workforce.

AI capabilities like machine learning and robotics can boost productivity, improve workshop efficiency, and reduce operational budgets. Additionally, the level of automation provided by AI systems can save time, automatically detect and react to machine malfunctions, effectively scale the production line, and make work much easier for human employees.

Siemens

Take Siemens, for instance. Through its partnership with Google, the company was able to leverage AI capabilities like cloud-based analytics and computer vision, which, in turn, enhanced its production exponentially [5].

AI technologies like machine learning can automatically use training data to find and eliminate bottlenecks in the production process. Other AI-based tools can also allow manufacturers to track productivity and system performance across multiple facilities from a single location.

Likewise, AI-powered robots can execute repetitive tasks on the production line independently. The result is a significant reduction in cycle times, improved accuracy, and improved employee morale and productivity. In fact, a McKinsey report estimates that leveraging AI capabilities to automate processes can increase yields by up to 30% [6].

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Assebmly line integration and automation

Most of the machinery in the production line sends diagnostic data to the cloud. Unfortunately, most of this data comes in different formats, making it difficult to analyze. By leveraging AI capabilities like analytics, companies can analyze the data and use the information to identify bottlenecks and other redundancies and improve processes.

AI in the manufacturing industry can also help automate certain processes, like identifying equipment failure and taking necessary steps. Some systems can also alert supervisors when human machine operators show signs of fatigue, thus preventing fatigue-induced human error and work-related accidents.

AI-based connected factory

Connected factories, also called smart factories, comprise numerous connected systems that work simultaneously to achieve production goals with little to no human intervention. These factories enable automated processing at all stages, from product design and assembly to storage.

Smart factories have centralized information silos that effectively minimize errors and information duplication. They also enable fast information exchange, which helps operators take necessary actions immediately in emergencies.

With an AI-based connected factory, human workers can focus on other important duties such as monitoring assembly line processes, product quality, and distribution – all in real time. The result is more streamlined factory processes, which positively impact other areas of businesses dealing with inventory management and marketing.

General Electrics is a good example of a company leveraging AI capabilities in its smart factory. The company recently set up a smart factory in Pune, India, in a bid to reduce machine downtime and improve productivity. The result was a 45% to 60% increase in general machine efficiency and effectiveness across all connected machines [7].

Benefits of AI in manufacturing and production processes

Manufacturing companies’ greatest challenge is the need to deliver consistently high-quality products at low cost. Fortunately, they can achieve this and much more with AI capabilities. Here are some advantages of leveraging AI capabilities in the production process.

Augment human capabilities

The past decade has seen an alarming number of fears that machines may one day replace human workers. While this might be partially true, it’s not really the case. The primary purpose of leveraging artificial intelligence in production is to enhance human workers’ effectiveness in their duties.

Manufacturing companies can make work less tedious and repetitive by using industrial robots alongside people in the production line. It also leads to a more accurate and efficient production process.

Canon

Take Canon, for instance. The company uses Assisted Defect Recognition, a combination of computer vision, machine learning, and predictive analytics, to supplement the skills of its human workforce. The software examines manufactured components through images and x-rays to determine their internal structure and integrity.

The process only requires a few specialized technicians to simplify the highly laborious and error-prone examination process. With machine learning and computer vision technologies, Assisted Defect Recognition can analyze inspected parts’ images, identify potential defects, and learn and improve the technology’s accuracy through the correlation of results and human acceptance [8].

Refine product inspection and quality control

The highest priority in the manufacturing sector is ensuring product quality. To achieve this, most companies use automated optical inspection (AOI) machines [9] to identify defects. Unfortunately, these machines have a low accuracy rate of about 60-70%. While this may seem passable in a school setting, the manufacturing process requires stellar results, with a quality assurance accuracy of close to 100%.

Augmenting AI in product inspection and quality control processes like AOIs can enable the systems to learn to recognize patterns, leading to significant improvements in process automation.

Take injection-molding machines, for instance. The temperature, speed, and pressure of the injection directly impact the quality of the finished product. By leveraging AI in the manufacturing industry, companies using the machines can analyze the parameters and accurately track the pressure and temperature of each injection to detect common deviations.

Additionally, high-resolution cameras equipped with AI-based image recognition software can perform quality checks at multiple points across the production process and allow companies to identify the most common points where products become defective. The result is a faster, more efficient production process that yields high-quality products.

BMW Group

A good example of a company leveraging this technology is the BMW Group. The company uses AI to evaluate component images from the production line. This way, it can spot deviations in quality standards in real time. At the final inspection area, the company uses an AI application to compare the live image of the newly produced car with order data. If it posts any deviation, it sends a notification to the inspection team, who can then take the necessary steps [10].

Enable preventive maintenance

18% of unscheduled equipment downtime is caused by operator error [11]. That’s probably why many manufacturing companies are adopting AI capabilities for predictive maintenance. Currently, up to 30% of AI use cases in the manufacturing sector are related to maintenance.

Predictive maintenance analyzes machine historical performance data to predict when they are likely to fail. This way, companies can significantly reduce the amount of time a machine is out of service and accurately pinpoint the root cause of the issue.

Additionally, leveraging AI-driven technologies like yield-energy-throughput (YET) analytics can ensure that all machines in the production line run as efficiently as possible, thus increasing yield and reducing energy consumption.

AI-driven technologies can process massive amounts of data, including video and audio, thus quickly identifying anomalies to prevent breakdowns. According to a report by McKinsey, predictive maintenance can lessen machine downtime by up to 30-50% and increase its lifetime by 20-40% [12].

Advanced inventory and layout planning

Manufacturing companies have a lot of inventory. To manage it effectively, they need to stock enough inventory to fulfill demand without letting it get too much that it’s taking up all the space. Unfortunately, fluctuations in demand are hard to predict, and even minor inventory management mistakes can have drastic consequences. That’s where predictive demands analytics come in.

By leveraging AI in the manufacturing industry, companies can develop more effective predictive models, thus enabling them to manage inventory effectively through a complex global supply chain. These models can also help companies estimate future demands and delivery times based on available historical data.

The advantages of predictive analytics go beyond inventory management into other essential areas of business, like factory layout. Many companies are already experimenting with smart factory technologies that enable them to track items throughout the production line. This way, they can better identify any bottlenecks in the production line and develop a more efficient layout.

Read more about AI in inventory management

Improved safety measures

The machines used in production lines are huge, and if not used correctly, they can be outright dangerous. In fact, injuries in the manufacturing sector make up just over 16% of major injuries and about 10% of fatalities in all sectors [13]. Manufacturing companies can help avoid some of these accidents by simply introducing smart machines in the production process. With these machines, human employees only have to ‘instruct’ and monitor the machines, which can perform the tasks accurately and with limited risk of accidents.

Final thoughts

In a market dominated by increasing customer demands for high-quality products and increasing competition from well-equipped competitors, AI has become a vital necessity for the manufacturing industry. AI can streamline the production process, enabling faster, safer, and more efficient production processes. It can also save manufacturing companies a lot of money by reducing labor requirements and work-related accidents. See our AI consulting services to find out more.

References

[1] Forbes.com. AI Stats News: Only 146 of Firms Have Deployed AI Capabilities in Production. URL: https://www.forbes.com/sites/gilpress/2020/01/13/ai-stats-news-only-146-of-firms-have-deployed-ai-capabilities-in-production/?sh=635810da2650. Accessed November 4, 2022
[2] Fortunebusinessinsights.com. AI in Manufacturing Market. URL: https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-manufacturing-market-102824. Accessed November 4, 2022
[3] Mckinsey.com. AI In Production. URL: https://mck.co/3NLtRd9. Accessed November 4, 2022
[4] Corporatefinanceinstitute.com. What is Manufacturing. URL: https://bit.ly/3zUX2EE. Accessed November 4, 2022
[5] Zdnet.com. Siemens Partners With Google to Offer Industrial AI Tools. URL: https://www.zdnet.com/article/siemens-partners-with-google-to-offer-industrial-ai-tools/, Accessed November 4, 2022
[6] Mckinsey.com. Smartening Up With AI. URL:  https://mck.co/3FYndOW. Accessed November 4, 2022
[7] Ge.com. Brilliant Manufacturing Technology. URL: https://invent.ge/3WBBdDU. Accessed November 4, 2022
[8] Usa.Canon.com. Learning. URL: https://www.usa.canon.com/learning. Accessed November 4, 2022
[9] Researchgate.net. A Review of Analytics of AOIs. URL: https://bit.ly/3DNXR3m. Accessed November 4, 2022
[10] Press.bmwgroup.com. AI in BMW Group Production. URL:  https://bit.ly/3hol3Oe. Accessed November 4, 2022
[11] Plantengineering.com. Maintenance Survey. URL: https://www.plantengineering.com/articles/2018-maintenance-survey-playing-offense-and-defense/. Accessed November 4, 2022
[12] Mckinsey.com. Manufacturing Analytics Unleashes Productivity and Profitability. URL: https://mck.co/3DPm7SH.  Accessed November 4, 2022
[13]  Trantercleere.co.uk. Industrial, Factory & Construction Accident Statistics. URL: https://bit.ly/3fIXC1J.