March 1, 2021
Digital transformation is revolutionising every aspect of the way manufacturers cater the needs of the consumers. Robotics are replacing manual labour and artificial intelligence is helping companies make smarter decisions. The manufacturing processes of the 20th century are outdated and inefficient. Compared to AI-optimised facilities, older assembly lines experience increased downtime, waste, accidents, defects, and fraud. Here is a closer look at the importance and industry adoption of machine learning and digitisation in manufacturing.
What Is DigitalTransformation?
Digital transformation refers to the implementation of digital technologies to replace manual labour or analogue processes. Digital transformation is pushing through in almost every industry and people’s daily lives. Most people now walk around with smartphones that hold more computing power than desktop computers from a decade ago.
Smart manufacturing is the latest example of digital transformation in manufacturing. It incorporates big data analytics, artificial intelligence (AI), machine learning, industrial internet of things (IIoT), and robotics into manufacturing processes.
Companies that use smart manufacturing can predict errors before they occur, meet customer demands with increased flexibility, and streamline manufacturing processes to become more profitable. The latest technologies allow businesses to develop products more quickly, offer more customisation options, and improve supply chain management.
Digital Transformation Leads to Agile Manufacturing
Many experts refer to smart manufacturing as the Fourth Industrial Revolution or Industry 4.0. Customers are becoming more discerning. Instead of mass-produced products, customers want specific and individualised features. Manufacturers are relying on digital transformation to meet these needs.
About 79% of companies are currently in the process of digital transformation. Traditionally, customers pay more when they request smaller production runs. However, digital transformation allows manufacturers to adapt their processes. They can develop agile manufacturing practices to quickly adjust production runs with fewer resources and labour.
Key Components of Digital Transformation
Leaner manufacturing processes and mass customisation rely on the latest digital technologies. Some of the most common components include:
● Industrial internet of things
● Big data
● Artificial intelligence
● Machine learning
Automation is one of the older examples of digital transformation in manufacturing. Companies have used robotics to replace manual labour since the first assembly line was built. However, modern robotics are more sophisticated and capable of taking on quite complex tasks.
If you look at the typical automotive assembly line, the most repetitive and dangerous tasks are handled by machines. Human workers may guide some of the robotic arms or monitor the assembly line, but most of the work is automated.
Along with automation, manufacturers are using sensors and scanners to improve manufacturing processes. The sensors and scanners are referred to as industrial internet of things (IIoT) devices. As with the smart devices that are becoming more common in homes, IIoT devices collect and monitor data.
At the most basic level, a manufacturer may use sensors to remotely monitor the performance or temperature of a machine. However, the greatest advantages of digitisation come from analysing large volumes of data (big data).
Artificial intelligence (AI) and machine learning (ML) software provide the tools for using big data to streamline manufacturing practices. Some of the largest manufacturers are using machine learning to dramatically increase their efficiency. For example, after adopting ML software, General Electric increased its production capacity by 20% and lowered material consumption rates by 4%.
Proven Advantages of Machine Learning and AI in Manufacturing
From reducing labour costs to improving workforce productivity, AI offers many advantages in the manufacturing sector. AI is improving manufacturing processes by detecting issues such as bottlenecks and potentially unprofitable production runs.
The advantages of ML start before production during the product development phase. ML software can improve the design and planning of new products and improve existing products by analysing big data. This incorporates data from previous production lines, including manufacturing data and sales data.
Machine learning (ML) can also enhance the quality of products. One study found that ML can increase product quality by up to 35%. Another study found that ML may increase defect detection by up to 90%.
How Many CompaniesUse AI and ML in Manufacturing?
At least 20% of the leading manufacturers already use AI, IIoT, ML, and blockchain applications to automate processes. Close to 60% of manufacturers use AI to support revenue growth by reducing waste and increasing productivity.
Experts estimate that 50% of companies that adopt AI in the next five years may double their revenue. Those that choose not to use AI and ML face many potential risks. As other manufacturers become more efficient and produce better products at lower costs, companies that avoid the latest technologies will fail to keep up with the competition.
Biggest Challenges of Digital Transformation for Manufacturers
Many manufacturers have already started implementing AI and machine learning technologies. A Forbes survey of the automotive and manufacturing sectors found that 49% of companies believe that AI will be critical to success in the coming years.
Despite the recognition of the importance of AI, some manufacturers are cautious about investing in new technologies. The main challenges that manufacturers face include:
● Increased IT demands
● Resource management
● Training and labour
Implementing new technologies places additional demands on information technology (IT)departments. The IT team is often responsible for ensuring that sensors and scanners continue to send data to the AI and ML software. They must also deal with a variety of security challenges to ensure that the data remains secure.
Purchasing new equipment or retrofitting existing equipment involves downtime. Manufacturers need to plan for the logistics of switching equipment, installing new software, and adapting to smart manufacturing processes.
Adapting to the changes may also require training and changes to the workforce. For example, a manufacturer may need to gradually cut its manual labour for the assembly line floor while increasing its IT labour. However, the advantages of digitisation outweigh the logistical challenges.
Working with the external partners can also help manufacturers address potential challenges. Specialists in the fields of digitalisation and machine learning listen to your concerns, analyse your practices, and create a plan that fits your budget and resources.
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