Use Cases of Machine Learning in Manufacturing
Machine learning has revolutionized many industries, including healthcare, advertising, and e-commerce. But the impact on manufacturing hasn’t yet been fully realized; instead, it’s just getting started.
If your company manufactures products, you likely use some sort of manufacturing software to streamline the process and make sure your clients receive their orders on time and in perfect condition. If you’re considering adding or updating your manufacturing software, consider how machine learning can take this software to the next level and give you even more insight into your operations and your bottom line. Let’s check out ML use cases in manufacturing.
#1 Reducing Unplanned Downtime
Reducing Unplanned Downtime by Automating Factory Maintenance Routine by predicting when component failure is likely to occur. This allows for proactive maintenance before an incident occurs, reducing costs related to safety inspections, insurance, etc. Machine learning companies can help you here.
#2 Pattern Analysis
With pattern analysis, specific patterns can be detected that offer information on potential outcomes. That’s not to say that data scientists will get everything right, but it does mean that they’ll have some insight into what may happen. Data scientists might not be able to predict your next move, but they can find indicators of certain outcomes or behaviors based on your past decisions and habits.
#3 Improving Operational Efficiency
One of the most popular applications for machine learning is streamlining operational processes. This includes anything from hiring to inventory management, but one important application is automating production. One recent example is an algorithm developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) that can predict—within 10 seconds—which parts will break first on a factory line. That information could help factories prioritize which components to fix first, reducing downtime and improving efficiency.
#4 Supply Chain Management
One of modern supply chain management’s biggest challenges is dealing with data. Whether it’s managing inventory, shipping routes, or predicting buyer behavior, manufacturers are constantly swimming in a sea of numbers. To help companies better cope with volume and variety, many businesses are turning to machine learning.
#5 Quality Control
Let’s say your factory is producing 10,000 units per day, but your customers are returning 1,500 units every day. Now that machine learning has come into play, your machine can take care of mundane tasks like identifying defective products quickly and effectively. That way you can focus on what really matters to business growth—attracting new customers! Machines are cost-effective in performing repetitive tasks.
You can use machine learning (ML) for everything from optimizing your supply chain to increasing efficiency on your factory floor. This guide will give you an overview of some specific ML use cases in manufacturing so you can figure out if it’s right for your company. If you want to learn more about how AI and machine learning can help manufacturers like yourself, get in touch with a software consultant company.