Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs and plan future financial actions to progress on their AI transformation.
Why is AI important in the manufacturing industry?
Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.
The same study also reveals that most popular AI use cases in manufacturing are improving:
- maintenance (29% of manufacturing AI use cases)
- quality (27%)
This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.
What are the common AI applications in manufacturing?
A digital twin is a virtual representation of a real-world product or asset. Thanks to digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance. There are typically 4 applications of digital twins in manufacturing as highlighted in the next examples.
Manufacturers can use digital twins before its physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.
Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.
Shop floor performance improvement
A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.
Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process
Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing the sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.
Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.
Price forecasting of raw material
The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI powered software like Kantify can predict materials prices more accurately than humans and it learn from its mistakes.
Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.
Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.
Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyzed data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:
- Improving production quality and yield
- Detecting early signs of deteriorating performance and risk of failure
- Tracking worker health and safety by using wearables
Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.
Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning. AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.
AI powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes. For instance, timely and accurate delivery to a customer is the ultimate goal in the manufacturing industry. However, if the company has several factories in different regions, building a consistent delivery system is difficult. By using an AI-powered process mining tool such as QPR’s ProcessAnalyzer, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. They streamline processes and identify where the bottlenecks are so that manufacturers can take action.
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