AI

Artificial intelligence solutions have the advantage that they become ever more accurate in predicting events and outcomes.

What are the principles that make this growth in accuracy possible?

• data-driven: to work properly, Artificial Intelligence does not need a priori business hypotheses, but rather uses information derived from data or concealed in them
  
  

• power: performance continuously improves, in a measurable way, both experimentally and in the field
  

• extracting value from complexity: AI is able to grasp non-obvious relationships, which may be too complicated or recondite to be grasped by even the most experienced human rules.
  

• dynamism: artificial intelligence solutions create a system capable of self-learning, because it metabolises the data it is fed.
  

What mix of ingredients enables Artificial Intelligence to be truly effective?

• Business knowledge: understanding of business processes and related dynamics;
  
  
• Analytical skills: robust methodology and ability to create and maintain models;
  
  
• Software tools and algorithms for automating solutions
  
  
• Collaboration between diverse skills (analytical/business) in the team
  
  In addition to these factors, for AI projects to be successful, it is crucial to adopt the right methodology to maximise conformity with business objectives.
  

Every manufacturing company needs to modify its processes (rolling metal, in the case of metallurgy, or processing grain, in the case of agribusiness, or any other component of a production line).

The focus is on the search for the underlying causes of any product non-conformities.

Starting from a pilot line, sensors are identified that provide useful information to the data collection system during production, employing an Industry 4.0 approach as follows:

• the root-cause analysis process is made efficient
  
  
• diagnostics are complemented by predictive models that anticipate faults with 80% accuracy.