How Advanced Technologies Are Transforming Manufacturing

Summary

When it comes to breakthrough technological advancements, everyone is talking about artificial intelligence (AI). When ChatGPT, a language generating AI model, was first launched as a prototype to the public in November 2022, it quickly grew to over 100m users by January 2023, making it the most rapidly adopted piece of software ever made.

Though many are divided on how this technology should be used, it's clear that powerful new technologies can quickly and permanently transform the way we live and work. 
 
In manufacturing, we’re seeing a parallel transformation. Three elements of advanced manufacturing have risen to define the next era of manufacturing: real-time decision making, real-time modifications and smart factory lines.
 

How does machine connectivity work?

A precursor to machine connectivity is ensuring that operations are stable by confirming that the fundamentals of standard manufacturing processes are working and can be executed repeatedly. Once this is in place, you can look at how machines connect with one another. 
 
The machines in a factory–and machines in factories in different locations–must be able to collect data from their environments, analyse it and connect with other machines to share that data and turn it into actionable insights.
 
In addition to machines, data can be collected from people and processes, varying from numerical and video data to sensors and shift times of line operators. Much of this data is already being collected, but forward-looking companies can invest in AI models to make better, real-time decisions with existing data.  
 
Think of machine connectivity as a nervous system. Nerves extend to all parts of the body and transmit signals back and forth. The machines in a manufacturing ecosystem represent body parts, but if the nerves stop transmitting data to the brain (or to your factory’s digital backbone), then that data will not be analysed properly or acted upon in real-time.

Making decisions in real-time to optimize operations

When machine connectivity is functioning properly, it allows for real-time decision-making and reduced downtime. Here are some questions that you should be asking when determining the most advantageous set-up:

• What decisions need to be made and at what frequency? - Frequency of decisions can vary widely–anywhere from every 15 days when predicting maintenance for an injection moulding machine to every second for a highly automated healthcare production line to ensure that components are properly connected.
• What should the KPIs be? - For example, if you are testing the heat performance of chipsets, numerical KPIs can be used. But if you are measuring something on the move, video KPIs that measure movement or dynamic visualisation would be more appropriate.
• How will we track and adjust? - With so much upstream data, AI models can indicate that something is going to happen before it does. In the example of the highly automated healthcare product, connectivity must be fast enough to act in the milliseconds to stop a faulty product from coming off the line. 

Consider technologies that monitor machine performance to alert about issues or maintenance required, empowering factory workers to step in and deploy strategies to either avoid downtime or minimize it.

This is crucial, as being able to make informed, data-driven decisions in real-time results in substantial cost savings. A recent report from Deloitte estimated that unplanned downtime costs manufacturers US$50bn each year.
 

How do you adjust in real-time?

Manufacturers must also invest in technologies that enable real-time modification, such as deploying IIoT and AI/ML (artificial intelligence/machine learning) to eliminate bottlenecks in the PCBA testing process.

Typically, if a PCBA test fails, the product is sent back to the factory floor for debugging before undergoing the entire testing process again. This adds manufacturing time and creates bottlenecks in throughput. To make the testing process more efficient and reliable, AI/ML can be deployed to develop an optimized reordering of test steps that reduces overall test time in the case of failures. 

Using AI/ML technology can also improve the quality inspection process, for example using vision data in an AI model to ensure each component is placed properly. Using AI and video, it is possible to see if an operator placed the components in the correct spot and in real-time provide feedback to fix any issues. This results in greater performance, yield and quality while reducing scrap by identifying issues before a part is sent to another step in the line.
 

Smart factory lines

With machine connectivity and AI/ML, along with other transformative technologies, comes the opportunity to optimize and build smart factories. Imagine a factory line where a faulty machine is causing defects in products. Instead of waiting until the inspection process to discover these defects – at which point, all the products will have to be discarded or reworked–sensors might recognie the defect the first time it appears.

At that point, an alert can be sent in real-time, and through AI/ML, the factory line can be stopped, recalibrated and continued once the issue is resolved. This will make the manufacturing process smarter, more efficient and more sustainable over time. In allowing AI/ML to make lower-level decisions, we will be able to free up human workers to use their expertise and critical thinking to handle more complex or challenging scenarios. 
 

Growth and development

As we move forward, we must consider the bigger picture, learn from mistakes and apply our learnings across our entire manufacturing ecosystem. To reap the benefits of technologies like AI, companies must determine which areas of the business can benefit from its use, standardize on vendors and usage, and include all stakeholders like IT for data and security.

Like ChatGPT and other AI platforms poised to have an enormous impact on our world, advanced manufacturing technologies are transforming our industry so that machines, processes and people can continuously learn and make faster, more informed decisions.