How Manufacturers Can Leverage AI for Smarter Production

Numerous studies, including a 2024 paper on how machine learning can prevent cascading failures, point to the ever-expanding benefits of artificial intelligence. Errors in production can halt entire assembly lines, causing massive failures, and that is exactly what AI can fix.

AI came in at a point where manufacturers had already begun seeking alternative solutions. With the rise in both technical and technological expertise, it had become easier to introduce a startup to the market and ensure it quadrupled in sales in just a year. That was before AI…

AI applications in manufacturing have become common knowledge over the years. While some have adopted it, others have shied away from the advancement, preferring traditional methods. This fuels the competition, pushing AI users one step ahead. You too can utilize AI for smarter production and reap its benefits! Here’s how;

1. Digital Twins

You don’t have to wait for issues to occur before you fix them. With the implementation of digital twin technology, you can essentially use AI to create a digital replica of your production processes and assess their efficiency in real time.

This involves the usage of AI to create a virtual copy of your processes, everything from the production line all the way up to the supply chain. These digital twins can be used to create simulations of probabilities and resultant effects can be analyzed.

By studying these effects on the replicas, manufacturers can recognize whether a new step in the production process or the implementation of a new technique will work out, before it is even added to the actual production line.  

2. Failure Forecasting

What is your primary goal as a manufacturer? Preventing failures should be one of your top priorities and that’s exactly where AI comes in. AI can be used to analyze sensor data from machinery and forecast system-wide failures before they occur.

Utilizing big data, digital twin technology, or machine learning, these systems are able to alert manufacturers regarding potential issues. The process is essentially halted before the breakdowns escalate.

The food processing industry, for example, often utilizes predictive maintenance by collecting data on key equipment so issues can be nipped in the bud. Vibration levels, temperature, current and voltage usage, as well as operating speeds are all monitored by attaching IoT devices to the machinery. When the system detects an issue, it immediately generates a report, notifying manufacturers and urging them to take action

3. Collaborative Robots and Labor

Contrary to popular belief, the concept of collaborative robots (cobots) isn’t new. In fact, cobots were first introduced in 1999 in the form of an Intelligent Assist Device (IAD) at General Motors. They were designed to assist humans in automotive assembly processes.

The introduction of AI as well as advancements in automation have only added to the efficiency and skill of cobots. They are known to work well with human labor, using guidance and instructions to perform complicated tasks with near-perfect accuracy.

Cobots are currently utilized in precise component placement for electronics manufacturers, assembly of small parts for automotive manufacturers, sophisticated surgery assistance in healthcare, detailed material handling in logistics, the packaging of fragile items in the food and beverage industry, and countless other industries.

4. Tracking Customer Preferences

AI has made it much easier for manufacturers to track inventory. By integrating generative AI into the supply chain, manufacturers can figure out the demand for the product based on past trends and order inventory to match the demand minimizing wastage through the Just-in-Time (JIT) approach.

Moreover, product features can also be easily adapted to customer preferences through AI support. By integrating AI into customer feedback systems, apparel manufacturers can create digital renditions of what a customer may ideally demand compared to the current product. This allows producers to tweak the product’s features in the future.

5. Consistency and Quality Control

The ability to control quality is perhaps one of the best implementations of AI in manufacturing. AI can support your production process by utilizing machine learning to identify product defects before they are rolled out to the public.

AI systems can record and analyze product images, comparing it to digital twins within their virtual systems and flagging any inconsistencies they come across. This ensures all products meet strict specifications.

Pro Tip: The implementation of AI can lead to numerous benefits that add efficiency to the production line. However, all that can fall apart without expert IT support for manufacturers.

This is essentially the backbone of the entire AI integration within the manufacturing process. In fact, you’d need IT support before you even consider integrating AI into the process and tracking data for better results. If you don’t have in-house IT experts consider bringing on board external IT consultants who can help you with the entire setup!

Your Journey Starts Today

Who doesn’t want more productivity? We all want to produce a lot more using the same resources we had before. There’s one small difference between those who can achieve this feat and those who only dream of it. Artificial intelligence!

Manufacturers who recognize the benefits of AI integration in manufacturing report higher levels of production, less outages, and far less errors. It is safe to say that AI is here to stay. Are you willing to take the leap