Paradisiotis, a leading enterprise in the poultry industry in Cyprus, often faces challenges in managing production effectively due to variability in growth rates and market demands. To address these issues, a sophisticated, data-driven approach using high-performance data analytics has been proposed, revolutionizing the way poultry management is conducted.
The proposed system employs a robust model that leverages population-based estimates instead of single-point data to forecast poultry growth and optimize collection schedules. By integrating advanced analytics and simulations, enabled by HPC resources -like our local HPC system, Cyclone – the model provides precise, dynamic forecasts. In the plots below, the left panel shows the estimated mean poultry weight on day 25 (top) and day 32 (bottom). The middle panels display the data points, the model fit (blue line), and the model’s future predictions (red lines). Finally, the right panel shows the expected distribution of the mean poultry weight on day 32 (top) and day 40 (bottom). These predictions allow for adjustments in real-time, aligning production closely with market demands and reducing waste due to overproduction or shortages.
This success story underscores the potential of integrating sophisticated computational tools and analytical models into traditional industries like poultry farming. The approach demonstrates a significant step forward in how companies can use technology to drive efficiency, profitability, and sustainability in their operations, setting a benchmark for innovation in agricultural management.