Automated factories: Manufacturing is learning

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  Quantumrun Foresight
• November 14, 2022

Insight summary

The Fourth Industrial Revolution (4IR or Industry 4.0) has resulted in the fully automated factory model. This system comprises of the Internet of Things (IoT), sensors, cameras, and highly mobile collaborative robots (cobots). However, this development has reduced the number of blue-collar human workers, and more employees are being re-trained as machine supervisors.

Automated factories context

An automated factory is a facility where machines and robots conduct most of the production tasks. Automation has been gradually introduced into factories, but it was only in the 2000s that facilities realized automation’s full potential. Automated factories can often operate with little human intervention.

The heart of an automated factory is its control system, which manages the entire production process. The control system is connected to a network that links the factory to the outside world, allowing managers to monitor and control production remotely. Because of the increased efficiencies in these facilities, they tend to produce more with fewer resources and are generally safer for human workers.

Some experts believe that the automated factory system will continue to improve into the 2030s. In addition to transitioning from global outsourcing models to regionalized supply chains, manufacturers are adopting intelligent automation solutions to be more flexible and resilient while earning an increased return on investment (ROI). 

Software-defined automation companies can reprogram a line, modify production output as market conditions change, and even easily copy processes across facilities. They can avoid downtime and start-up expenses that are usually limiting when considering a capacity increase. With this type of programmability, as well as modular hardware and adaptive robotics, manufacturers can make the most out of their production lines.

Disruptive impact

Some tech experts believe that rapid developments in the automated factory system are underway. The first is the increasing use of digital twins of machines to optimize performance, predict maintenance needs, and troubleshoot issues. At the same time, machine-level intelligence is moving from being individualized inside each machine/robot to a more centralized system that uses cloud computing.

This transition allows manufacturers to take full advantage of artificial intelligence (AI) in their operations. However, these developments require more complicated computing, communications, and infrastructure systems to manage data processing and latency (the time it takes for a signal to reach devices). With all edge applications, there is a demand for micro data centers explicitly built for the application, which allows the technology to be more manageable and deployed quickly.

Another development is combining the hybrid human-cobot workforce, the ability to coordinate activities, human labor, and intellect with technologies like autonomous mobile robots for jobs people don’t want or need to perform. Examples are machine vision systems that automate compliance and quality control processes with advanced cameras and software and radio-frequency identification (RFID) to track inventory. These kinds of technology enhance human abilities and empower frontline staff instead of completely replacing them. 

Implications of automated factories

Wider implications of automated factories may include: 

• A complimentary movement toward reshoring manufacturing facilities, as automated factories negate the benefits that cheap human labor from developing nations provide multinational corporations.
• Onshoring leading to revenue declines in the developing world for nations dependent on foreign investment.
• The increasing use of IoT and 5G to help human supervisors make crucial decisions and prevent downtime or real-time accidents.
• The deployment of more micro data centers near or within the factories to ensure continuous cloud computing and enabling near real-time applications.
• The deployment of more green technologies in factories to reduce energy consumption and carbon emissions and recycle rejected materials or defective products.
• Employees upskilling from manual labor to machine troubleshooting and operating more complicated but user-friendly cobots.
• AI systems like Google Cloud’s Visual Inspection AI being heavily integrated in facilities to oversee line production, including detecting product defects.

Questions to consider

• What other types of factories or sectors might implement automation efforts? How might that affect the workforce?
• How else has automation affected the way people work in factories?