Industrial production in the U.S. has already exceeded the pre-pandemic level, and growth will remain high in 2022. The demand for digital technology implementation is strong since smart manufacturing empowered by robotics, AI, and 5G improves operational efficiency.
AI-based systems will perform even more tasks related to industrial automation, machinery inspection, and diagnostics in the near future, thereby saving time and costs and eliminating the need for human intervention. Businesses may see an ROI from digital transformation, and the potential market size of AI in manufacturing is projected to reach 16.3 billion dollars by 2027, according to MarketsAndMarkets. However, there are other equally important insights, and gaining access to these will provide you with a better understanding of Artificial Intelligence benefits for production plants.
AI in manufacturing: market insights
Automation in manufacturing is boosted by industrial IoT. The system analyzes data received from devices and with the help of AI algorithms, creates instructions for machines.
Artificial Intelligence here would comprise NLP, deep learning, and context-aware computing. This smart methodology has already been introduced by General Motors. The automotive manufacturing company applies IoT devices to check the level of humidity in production areas. AI checks whether the humidity is appropriate for paint work. If it doesn’t correspond with standards, the vehicle is moved to other operations.
According to Deloitte, 93% of companies expect that AI will be a driver of the growth and innovations in manufacturing. AI-based solutions may be applied for predictive analytics, quality control systems, preventive maintenance, and so on. However, the difficulties in collecting reliable data and limitations related to infrastructure can be obstacles for implementing AI technology at many plants.
Robotic process automation
Robotic process automation (RPA) provides the use of bots that fulfill repetitive tasks. RPA has the following advantages:
- Cost reduction (savings are estimated at up to 40% of operational costs)
- Seamless control of the processes
- Consistently high product quality
- Reduction of employment demand per product unit
How can RPA be applied in manufacturing? A set of tasks that are performed with its help include purchase orders automation, stock control, and communication with vendors and customers.
Let’s consider a use case to gain a better understanding of RPA. Vendor invoice processing requires a lot of time and effort since employees must manually extract data to fill in forms. ML solutions with object character recognition capabilities allow the company to simplify the process by accurately extracting data. Users can then check and amend information in the pre-filled forms.
Kitchen manufacturer MVI Maskinfabrik exemplifies another RPA use case. According to Fortune Business Insights, the company has invested in cobots (collaborative robots) that replaced old welding robots, thus doubling the speed of welding processes by at least twice.
RPA has already proven its efficiency in the automation of any rule-based process. Besides invoice processing, the technology is capable of the optimization of supply chains, inventory management and reporting, and execution of orders. RPA can also be used for data management, which is significant since engineers spend 37% of their time on manual data acquisition and handling according to Supply & Demand Chain Executive.
Predictive maintenance with deep learning
IoT devices serve for gathering data, and predictive maintenance algorithms assess the status of equipment, enabling manufacturers to align maintenance processes with production activities. The system is designed in such a way as to ensure that key performance indicators – Mean Time Between Failure, Gross Running Time, Temperature, Speed – correspond to business standards. When any deviation emerges, changes in the maintenance plan are introduced. Predictive maintenance includes a variety of analytical approaches, such as infrared checks, acoustic inspections, and vibration analysis.
Manufacturers who implement Predictive Maintenance can reduce the volume of machine breakdowns and durability of repair, improving the industrial infrastructure. To remain competitive in the business environment, manufacturers apply Machine Learning solutions that automate anomaly detection. The essence of this technology comes down to training models that define issues and inform staff about them. This requires taking into account the history of errors and repairs along with the characteristics of the equipment.
An example of the solution is IBM Predictive Maintenance and Quality service. IoT devices collect parameters, which can be then analyzed by an IBM system that predicts downtime, optimal service life cycles, and preventive measures.
Enhancing quality control with computer vision
Computers can analyze what images and video represent, and this capability, known as computer vision, allows enhancing quality control and optimizing the processes. Let’s dive deeper into use cases of computer vision adoption. Along with other branches of AI in manufacturing, it empowers vision-guided robots that control the positioning of goods on the production line.
The next use case is directly related to quality control and implies the detection of anomalies. Machines check images and assess deviations from the standard dataset, preventing product quality drops. This also enables prevention of emergencies in the factories.
Some manufacturers apply computer vision capabilities to inspect the packaging, scan barcodes, and track items. The advantages of its implementation are reduced costs, accuracy, and time efficiency.
Other computer vision AI use cases in manufacturing include:
- Supply chains optimization
- Monitoring of workforce
- Lean manufacturing
There are hundreds of reasons to use AI in business, and manufacturing is no exception. Accurate industrial AI applications will help manufacturers to increase profitability, and data here serves as a source for obtaining business benefits. Using AI, manufacturers may achieve such objectives as predicting disruptions, forecasting delivery dates, spotting inefficiencies of the equipment, etc. Artificial Intelligence is a crucial part of Industry 4.0 where smart technology and bold ideas meet to revolutionize business.