Unleashing the power of AI in manufacturing

The manufacturing industry often faces the challenges of rising business costs, fluctuating demand, and unpredictable supply scenarios. Luckily, this industry has always been inclined toward adopting innovative technologies to, for example, increase efficiency and automation. As we dive into the era of AI (Artificial Intelligence), its potential to revolutionize various processes in manufacturing is highly promising.

Use cases

Manufacturing processes often generate large volumes of data, which when using AI to gain insights and trends in these data, can lead to significant optimizations. Machine Learning (ML) and Deep Learning (DL) neural networks can enhance data analysis and decision-making in this sector.

It can help you deal with trends such as cost reduction and efficiency, inflation and supply chain security, and sustainability. They heavily impact businesses but luckily, adjusting the technologies you use as a company can help you deal with those trends.

Let us explore some key areas in which manufacturing organizations can harness the power of AI.

Artificial Intelligence use cases in the manufacturing industry

1. AI-driven product design & development

In unexpected situations, AI makes it possible to act quickly. Over the past years, we have seen high inflation rates as well as supply chains being disrupted. Generative design can help you redesign parts faster in case materials need to be changed for example.

It is clear that the potential of Generative AI in product design and development is significant.

2. AI supporting production planning

A fundamental aspect of effective production planning and inventory management is accurate demand forecasting. Traditional forecasting models often underperform, as they rely solely on historical data and linear models. These methods can lead to inaccurate estimates and fail to recognize patterns and correlations.

AI-based systems, on the other hand, leverage machine learning algorithms that continuously learn from historical data and identify hidden correlations that can significantly improve demand forecasting accuracy. Manufacturing businesses can enhance their production processes and allocate resources more efficiently with this solution.

Production planning is a complex process that involves a myriad of activities, such as forecasting, supply chain management, inventory tracking, and job scheduling. It requires an elevated level of expertise to efficiently plan production, and sometimes even necessitates real-time rescheduling due to unforeseen issues like breakdowns or quality lapses. ERP systems traditionally play a significant role in assisting with production planning, but a new player has arrived on the scene: AI-powered systems.

AI-powered systems offer a more dynamic solution in comparison to traditional ERP systems, as they employ machine learning algorithms and advanced analytics to swiftly adapt to changes and optimize processes. By implementing AI, potential bottlenecks can be eliminated, resources can be judiciously organized, and prompt reactions can be made to changes in the production environment. However, the output quality of AI systems is reliant on the availability of high-quality data.

3. Optimize your assembly line with AI

In addition to optimizing the machinery itself, AI can also significantly enhance the capabilities of production workers, providing them with valuable insights and data-driven recommendations. A prime example is software solutions designed to gather data and suggest actions to improve manufacturing performance. Similar systems allow workers to ask context-specific questions, which it then responds to by generating relevant queries and providing actionable insights.

Another interesting human-machine collaboration is found in AI-infused robots. They can assist workers by transporting tools and essential items, regardless of the changing positions of both the tool and the worker. This dynamic support system not only boosts productivity but also reduces the risk of workplace accidents and human error.

4. AI to improve quality control

Manual quality control is labor-intensive and an error-prone process. AI makes it easier to build detection models. Cameras near your production line in combination with AI algorithms make it possible to automatically detect defects. This vision-based automated quality inspection can ensure that every part is free of defects, while manual inspection is often based on spot checks. The detection model could also use incoming measurement data instead of images. Anomaly detection identifies and detects data points (e.g. incoming measurement data) that are outside the norm or rare. If you can immediately detect such a deviation, you can prevent an entire batch from going wrong.

5. Maintenance supported by AI

AI can support the switch from reactive to predictive maintenance. Solutions such as 9A Connected Factory & Insights can provide historic and real-time data. Powered by AI, it is possible to predict future breakdowns of machinery. In the end, predicting downtime of machines will result in improved reliability and lower the waste in your production processes. Here again, qualitative IoT (Internet of Things) data is indispensable.