The Role of Artificial Intelligence in Manufacturing
Artificial intelligence plays a significant role in manufacturing. Keep reading to learn more about how AI works in the manufacturing industry and the benefits of incorporating machine learning in manufacturing.
AI In Manufacturing
Although it’s easy to think that artificial intelligence is a thing of the future, AI is already being implemented across the manufacturing industry.
The automaker BMW utilizes machine learning models to evaluate images of components on the production line to pinpoint any deviation from standards – in real-time.
Another company actively utilizing artificial intelligence is Caterpillar. Their marine division saves up to $400,000 per ship – per year – Using big data to determine how often the hulls should be cleaned to maximize efficiency.
Even company is like Nokia are using AI in manufacturing to alert operators of any inconsistencies during their production processes. Their unique platform uses machine learning coupled with video applications to correct problems as they occur on the assembly line.
AI in manufacturing also allows companies to improve demand forecasting and ensure that they are accurately ramping up and down production as necessary. The French multinational food manufacturer Danone Group can attest to this – they are utilizing machine learning models to improve coordination across departments like supply chain, marketing, and sales. Their AI Implementation led to a 30% reduction in lost sales and a 20% reduction in forecast error!
How Artificial Intelligence can Benefit the Manufacturing Industry
So, how can artificial intelligence benefit the manufacturing industry?
The primary focus of AI in manufacturing is limiting downtime and ensuring that production lines continue to operate effectively.
For example, when a machine learning model notices anomalies during a specific activity or other trends that indicate a problem with machinery, it uses predictive intelligence to determine if human action is required. Based on the explanation for the unusual trend, the model may determine that a production line needs to be shut down or perhaps run at a lower capacity until replacement parts are received.
Companies utilize artificial intelligence platforms to make data-driven decisions that reduce plant downtime and save them hundreds of thousands of dollars in production.
AI can be used to predict when replacement parts need to be ordered. This ability reduces the downtime of plant machinery and prevents expensive parts from piling up in inventory. Machine learning models can do this by aggregating large amounts of data to recognize trans and patterns that may indicate a potential part failure.
This analysis can be taken a step further, and plant maintenance and repairs can be scheduled based on when the models predict would be the most effective time.
Artificial intelligence can also benefit the manufacturing industry because it gets better over time. As the machine learning models continue to analyze data specific to your business and manufacturing process, they will become more accurate and be better able to predict failures and anomalies.
What does a Manufacturing AI Implementation Look Like?
There are many common misconceptions regarding manufacturing AI implementation. Typically, this includes the idea that artificial intelligence is too expensive or difficult to implement. However, this is not the case.
The first step to implementing artificial intelligence into your manufacturing process is to work with a vendor like LogicPlum to analyze your current equipment and existing data.
The data you already have will be used as a baseline, and it will be processed through machine learning models to determine what trends can be identified. If gaps in the information may require additional sensors or data collection equipment to be purchased and installed.
Next, your AI vendor will provide appropriate algorithms that will best suit your business needs and work with you to build processes around the predictions.
If you want your manufacturing AI implementation to be truly successful, there must be a specific and measurable problem that you are trying to solve. There must also be data available to this problem and a commitment from your leadership to work with the right tools.
In other words, the manufacturer must work closely with the AI provider to optimize the implementation and scale the applications across the entire business.
Currently, almost 30% of all AI implementations in the manufacturing industry are related to the maintenance of machinery and production assets. These implementation projects’ primary goals are to install tools that can predict when a machine might fail. They can also recommend the best time for technicians to perform maintenance
Take General Motors, for example. They successfully implemented machine learning models that use imaging from cameras along the assembly line to pinpoint failing components before unplanned outages occurred.
Benefits of AI in Manufacturing
There are many benefits of AI in manufacturing, including quality assurance, preventive maintenance, predictive forecasting, and enhanced monitoring.
Perhaps the main benefit of artificial intelligence in manufacturing is concerning quality assurance. Companies can use machine learning models to detect any deviations from the standard design specs and identify errors or discrepancies that the average person may not be able to see.
Incorporating machine learning into the quality assurance process improves the quality of your products and results in cost and time savings.
Another benefit of machine learning comes in the form of preventative maintenance. When the AI platform can predict the parts that need to be replaced before an outage occurs, you can illuminate problems before they arise and ensure that production does not need to stop due to equipment failure.
One of the most important aspects of running a manufacturing plant is forecasting demand to produce enough products to fulfill outstanding orders.
Artificial intelligence platforms can manage the entire supply chain And predict any shifts in demand that can affect your production or distribution processes. The goal is to have a proactive strategy to satisfy demand rather than reacting after the fact.
Machine learning algorithms also allow you to manage manufacturing plants in real-time. You can obtain invaluable data about inefficiencies or bottlenecks in your process and use the predictions provided to improve production and save time.
Is AI the Future of Manufacturing?
As you can see, the benefits of using machine learning models in the manufacturing industry are significant.
Predicting potential machinery failures or one specific equipment that requires maintenance can allow businesses to handle problems before they arise. This will save them time and money in the long run and enable them to focus on maximizing production and reducing downtime.
There is still a lot of work to be done when implementing AI in manufacturing applications. As businesses become comfortable enhancing human labor with technology, they will obtain even more benefits.
The goal of utilizing artificial intelligence in production is not to replace humans but rather to free up their time to focus on more strategic and value-added tasks.
AI is the future of manufacturing – although it is considered a competitive advantage now, the adoption rate of these tools is quickly increasing and will soon saturate the entire industry.
According to MarketWatch, the use of artificial intelligence and manufacturing is expected to grow by over 50% each year through 2027!
Artificial intelligence in manufacturing is here to stay. If you’d like to learn more about implementing machine learning models in your organization, contact us at LogicPlum today! We are experts in crafting AI solutions that can help your manufacturing process become more efficient and effective.