Global Artificial Intelligence in Manufacturing market from the perspective of all its current trends that are influencing it is important to understand in order to obtain the most rounded solution for business strategies in it. Industrial companies need to become digital. Applying Artificial intelligence to manufacturing requires a number of key, foundational technologies and process innovations.

Use Cases

Enhanced monitoring and auto-correction

Self-learning monitoring makes the manufacturing process more predictable and controllable, reducing costly delays, defects or deviation from product specifications. There is huge amount of data available right through the manufacturing process, which allows for intelligent monitoring.

Predictive maintenance

Predictive analytics can analyze sensor data right from the production processes and machines to determine if there is an upcoming problem with a machine which is likely to lead to a problem soon. Such a model can then be used to predict if a new failure is likely to happen before the next maintenance interval and should be addressed better now than later.

Supply chain management

Making the most of supply chain and production opportunities requires all parties to have the necessary technology and be ready to collaborate. Only the biggest and best-resourced suppliers and manufacturers are up to speed at present. Supply chain management helps to identify the highest risks for failure and what would be the expected impact in case of failure. Predictive analytics can also be used for demand forecasting and hence for improving logistics but also timely negotiations with suppliers.

Optimizing throughput rates

The manufacturing industry even has started to connect data mining and predictive analytics with the control centers of their factories. Based on the sensor data describing the production process itself and also the input to this process, those models find the optimal settings for the process in real time in order to optimize for quality, or higher throughput rates, or even both at the same time.

Quality assurance

Predictive models use the data describing the process and combine it with sensor data describing the current state of an item in order to predict the quality of the final outcome.


  • Indirect benefits from more flexible, responsive and custom-made manufacturing of goods, with fewer delays, fewer defects and faster delivery
  • Greater automation of a large number of production processes
  • Intelligent automation in areas ranging from supply chain optimisation to more predictive scheduling
  • Using prescriptive analytics in product design – solving problems and shaping outcomes, rather than simply predicting and responding to demand in product design
  • Time saving