AI applied to sensorization

Industry 4.0 is characterized by the incorporation of enabling technologies such as the Internet of Things, Cloud, Artificial Intelligence and Robotic process automation into production processes and facilities, leading to:

• Greater availability of information for decision making.
• Increased analysis capabilities thanks to advanced modeling and more valuable information.
• Higher level of convergence between the physical and the digital worlds.

Within this context, sensorization is the placement of devices to measure different parameters (such as position, movement, temperature, etc.) at different points in the production process to capture data on both the process operation and the physical environment. These data are sent through communication networks, processed by Cloud or Edge Computing technology platforms and  translated into functionality via various digital process management applications.

Use cases for sensorization can be sorted into three main groups:

• Uses for the operation of the productive process: such as remote monitoring and control, digital twins or predictive maintenance
• Uses in connection with supply chain management: such as vehicle fleet management, materials tracking and traceability or inventory measurement
• And uses related to product lifecycle management: from improvements in product design and development to identifying customer behavior and preferences.

These applications can bring multiple benefits in terms of efficiency and productivity, including a reduction in downtime, increased quality and safety (such as fewer occupational accidents) and development of new business models (such as data monetization models).

Our value proposition in this area is structured into four service lines:

• Development of Artificial Intelligence models, providing our clients with our capabilities in this area and our toolkit of Artificial Intelligence algorithms to address all types of business cases.
• Interpretability and dashboarding of results, helping to add value to the data through interpretability dashboards and Key Performance Indicators.
• Deployment of our proprietary systems Modelcraft for automated machine learning, and Gamma for model lifecycle governance.
• ​​​​​​​And infrastructure design, approach and implementation, accompanying our clients in the most appropriate cloud platform approach and addressing challenges related to cybersecurity, compliance and change management.