With industry 4.0, not only terms such as machine learning or artificial intelligence gained popularity, but also older concepts were elevated to the new standard under new names such as SMART factory, SMART manufacturing, SMART warehousing, SMART production as well as SMART supply chain. Industry 4.0 refers to the use of technologies based on the principle of data sharing. These technologies include innovations such as the Internet of Things, cloud computing, and artificial intelligence.

The Smart Factory concept provided by SAP

The purpose of this article is to provide an overview of some of the AI innovations currently being deployed or soon to be deployed in manufacturing, which in turn will provide guidance to industry leaders and make it easier for them to assess the fit of such technologies to their business.

Quality assurance inspections

With automated quality assurance systems, quality remains constant, which has the effect for companies of increasing customer satisfaction and decreasing production times and costs associated with a product or service, examples are the BMW Group and Bosch. There are several machine learning approaches for such methods. For products that have a limited number of features, supervised learning can be used to distinguish between specific features.

With sufficient data, it is, therefore, possible to carry out classification and thus find quality defects more quickly. These classifications can be optimized by neural networks and trained to perfection. A general system architecture can be seen in the diagram provided by Azure:

Diagram, explaining one example of a quality assurance solution in the cloud by Azure

Preventive maintenance

One of the main reasons predictive maintenance could be helpful is predicting when a mechanical part needs to be replaced. Combined with historical data, machine learning results in an algorithm that identifies potential problems when they occur and helps the organization’s staff take the necessary steps to eliminate problems that can delay or even interrupt development.

Predictive maintenance for industrial IoT solution in the cloud by Azure

In predictive and predictive maintenance, statistical methods have been used for decision-making for some time. In areas with many variables, machine learning methods such as neural networks.

Predictive forecasting

To remain competitive in today’s ever-changing climate, companies must remain alert to even minor changes in market patterns that indicate significant fluctuations in demand in the future, as this can lead to serious upstream production problems.

All elements of the purchasing and supply management system can be adopted by AI algorithms to help companies adjust to changes in demand that might otherwise affect production and delivery.

Real-time monitoring

One of the most valuable benefits of AI in manufacturing is real-time monitoring, as it provides a more accurate description of where and if inefficiencies exist in the production chain and what is causing the bottleneck. Identifying the exact process that needs to be adjusted helps companies solve the problem quickly, resulting in time and cost savings.

Monitoring solution provided by Azure

It has been shown that cloud manufacturing as a real-time monitoring method can lead to higher resource efficiency by detecting the current machine condition and minimizing system downtime using real-time condition-based tracking by analyzing the received sensor data.


Manufacturing and purchasing and procurement management are perfect for the use of artificial intelligence. Even though the Industry 4.0 revolution is still in its infancy, we are already seeing great benefits from AI. This technology is set to change the way we produce goods and manage materials forever – from the design process to manufacturing, supply chain, and administration.