Maximizing the potential of data

Visualization of an immediate snapshot of key metrics related to production quality.
The goal is to address critical points in the production process as they arise, directly contributing to the continuous improvement of product quality.

Use of collected data to adapt production processes to new market needs and to stimulate innovation, customizing products based on customer demands.

Collection and recording of data to trace the entire life cycle of products, ensuring regulatory compliance and a swift response to quality issues.

Use of data visualization tools for an accurate forecast of revenue in the upcoming years.

Data management enables in-depth analysis of production costs, identifying savings opportunities and improving overall profitability.

Integration of Balanced Scorecard to elevate process quality, improve reputation with customers, and optimize operational efficiency.

Using advanced tools to gain a precise view of future trends in sales and orders, improving resource planning and management.

We have developed a specific tool to provide our clients with the ability to track the real-time progress of outsourced data projects.

This transparency enables more effective communication, improving collaboration and ensuring that expectations are always aligned with the actual project progress.

The adoption of Industry 4.0 machinery not only tracks critical aspects but also certifies processes, drastically reducing the risk of human errors.

A tangible example is the injection curve analysis at Formula Foundry. The software we designed - based on the patented algorithm by Roberto Camerin - optimizes the die-casting process.

Specifically, we analyze the injection curves of the press, processing information to correctly interpret process variables. The software helps the foundry achieve proper mold filling, reducing waste and increasing production efficiency.

Data collected from Industry 4.0 machines can be used to monitor machinery performance in real-time. This allows for the timely identification of any anomalies or malfunctions, reducing machine downtime and improving overall efficiency.

Analyzing data from machinery enables the implementation of predictive maintenance strategies. This means intervening before a component actually fails, reducing maintenance costs and increasing machinery availability.

Data collection can be employed to monitor, control, and certify product quality in real-time. This is particularly crucial in sectors where precision and consistency are paramount, such as in the automotive industry.

Data collection and analysis can contribute to improving the energy efficiency of machinery and processes, reducing environmental impact and associated energy costs.