Three ways AI is changing the manufacturing industry

People often use the terms AI and machine learning interchangeably, but they’re two very different things. Machine learning puts data from different sources together and helps you understand how the data is acting, why, and which data correlates with other data. It helps you solve a particular problem by taking historic evidence in the data to tell you the probabilities between various choices and which choice clearly worked better in the past. It tells you the relevance of all this, the probabilities of certain outcomes and the future likelihood of these outcomes. In the webinar, Rick described AI use cases featuring several manufacturers he has worked with including Precision Global, Metromont, Rolls-Royce, JTEKT and Elkem Silicones. Since 2017, Delta Bravo has worked on about 90 projects and has learned what works best and produces significant return on investment (ROI), especially for smaller manufacturers.

Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments. The appropriate space distribution within the warehouse is equally as crucial to manufacturers as the other factors outlined above. Analytics Insight® is an influential platform dedicated to insights, trends, and opinion from the world of data-driven technologies.

The Forrester Wave: Multimodal Predictive Analytics And Machine Learning Solutions

It optimizes workflows, augments supply chains, and fosters collaborative environments where humans and AI-driven robotics harmonize for unparalleled productivity. The predictive software serves as an early-warning mechanism, enabling Airbus to swiftly halt machines, thereby preventing time and financial resource wastage. Through this, the company has effectively established buffers to guarantee the availability of parts, consequently streamlining assembly lead times. AI-powered defect detection processes empower the company to identify issues early, effectively mitigating potential disruptions in aircraft production.

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

Inventory management prevents bottlenecks

By focusing on specific use cases, organizations can prioritize data quality improvements where they matter most, thereby gradually refining and improving their datasets. The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job. For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning. If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind.

A predictive maintenance company called Augury worked with Colgate-Palmolive to use predictive maintenance, and they saved 2.8 million tubes of toothpaste. They looked at their power plants and the overall efficiency, what they call the heat rate. They were able to reduce energy consumption by about 1 percent, which doesn’t sound like a lot, but you realize they generate enough energy for 20 million households. Finally, Amgen uses visual inspection to look at filled syringes, and they were able to cut false rejects by 60 percent. Large manufacturers typically have supply chains with millions of orders, purchases, materials or ingredients to process.

7 production in dark factories

AI-enabled devices and tools that can also manage and track fleet operations efficiently. The most common use of AI and ML in manufacturing is to improve equipment efficiency. A product might look perfect from the outside, but it offers low performance when we use it. To embark on this journey of AI transformation in manufacturing, consider enrolling in our BB+ Program.

For example, certain machine learning algorithms detect buying patterns that trigger manufacturers to ramp up production on a given item. This ability to predict buying behavior helps ensure that manufacturers are producing high-demand inventory before the stores need it. Our advanced artificial intelligence mobility solutions also improve employee productivity. We are experts in developing AI-powered solutions that tackle equipment maintenance and warehouse management. Using Artificial intelligence-powered manufacturing robotics and self-driving vehicles across production and logistics operations, manufacturers can reduce dependency on the human workforce and improve productivity. The use of artificial intelligence in supply chain management is rapidly increasing.

Top use cases of AI in manufacturing Industry

Usually, such machines in continued use today are slow, bulky, and only remain in use to save money on a replacement in many cases. Last, what you should learn is, what type of problem are you trying to solve and what types of problems are solvable by machine learning? Is that a clustering problem or am I trying to take groups of things and group them together very much like we did in this study? Prediction—am I trying to predict if something will fail in the field in the future, even if it’s working just fine now? Or an anomaly detection, which is something really different than something else.

Most importantly, organizations will be more resilient to future supply chain shocks. Combining AI/ML with other technologies like sensors, robots, and human inputs would enhance operations considerably and lead to new kinds of innovation and productivity in the business. While your business may lack the requisite skill sets, don’t allow that to stop you from investing in commercial AI/ML solutions to get you started.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place. As we mentioned, there are many different applications of AI within manufacturing. According to Accenture, the manufacturing industry stands to gain $3.78 trillion from AI by 2035. Fostering a safe work environment is crucial for a handful of reasons like protecting employees and boosting morale. Between visual data surveillance, crossing legal hurdles, and the effects on worker morale to name just a few incremental challenges, the road to clean, physical data is paved in high promise and lacking results.

The intelligent factory systems will automatically alert when orders arrive, inventory runs short, and whatever particular KPI you set up. In addition, various cloud techniques allow easy scaling of the manufacturing processes alongside fast tech integrations. AI algorithms analyze historical sales data, market trends, and supply chain dynamics to determine optimal inventory levels.

Digitally Transforming Data and Processes With Product Lifecycle Management

Read more about Cases of AI in the Manufacturing Industry here.