Thanks to intuitive visual reports, statistical charts, and graphs with customizable timeframes and filters (by features, shifts, lanes, etc.), it is possible to fully leverage 100% of product metrics and process variables that would otherwise remain hidden under large volumes of data.

Historical data serves as the foundation for predictive and prescriptive analytics. Basic insights can be extracted from visual reports, as graphs and dashboards provide an overview. They help identify trends and patterns by detecting recurring behaviors. Additionally, they contribute to quality control by analyzing defect rates, deviations, and anomalies, ultimately enhancing product consistency and reducing waste.

By utilizing real-time product and process data visualization, any anomalies can be detected at an early stage, which helps to prevent unexpected food waste and downtime. These data can be displayed on large dashboard screens, fully customizable based on customer requirements, located at various positions on the production line, ensuring that all employees can view data and status in real-time.

DATAVIEW is also designed to alert the operator in case of specific events or warnings. When triggered, the operator is provided with recommended actions, predefined based on root cause analysis diagram: the “Easy Alert Setup” tool allows for customizing messages (checks and instructions) depending on measured values for different features.

It is advisable to integrate these functionalities with mobile devices such as tablets or smartphones, so that operators can access information and receive notifications wherever they are, without the need for a fixed terminal.

True data-driven decision-making requires advanced analytics tools like statistical software, AI models, and real-time data processing systems — all of which can be effectively integrated into a Digital Twin platform in the DATAMIND license.

A Digital Twin is a dynamic virtual model of a process that continuously updates using real-time data from key variables, such as product measurements, process parameters, and machine configurations. It enables closed-loop control, simulation, monitoring, and optimization of the process through algorithms, physics-based models, or machine learning techniques.

Before deploying the system on the physical production line, it is crucial to validate it by developing user-friendly dashboards for operators. These dashboards should allow real-time monitoring of inputs and outputs, process parameters, and system alerts.

The Digital Twin enables advanced pattern recognition to detect inefficiencies or defects, providing the foundation for data-driven correlations. By leveraging these insights, the system can recommend optimal corrective actions and adaptive process improvements, either by notifying operators or automatically adjusting machine settings (e.g. setting temperatures of the different heating units on ovens, fryers, and setting parameters on other processing and packaging machinery). This reduces manual interventions and enhances overall efficiency.

The Digital Twin enables predictive modeling by analyzing historical and real-time data to forecast production outcomes based on specific settings and operational variables. This allows for the identification of the most efficient configurations to maximize productivity or maintain output stability.

By leveraging the Digital Twin, simulations can be conducted to assess the impact of various scenarios on production. This strategic tool allows decision-makers to evaluate the potential consequences of different configurations and operational changes before implementing them in the live production environment, reducing risks and improving efficiency.

The Digital Twin anticipates maintenance needs and prevents failures before they occur by leveraging machine learning algorithms for real-time data analysis. By detecting patterns and predictive signals of potential malfunctions, the system enables preemptive interventions, reducing downtime and extending equipment lifespan.