A roadmap for AI adoption within De Heus

De Heus is confronted with a substantial difficulty caused by the deficiencies of existing manual procedures, leading to unsatisfactory financial returns and suboptimal animal welfare outcomes. The traditional methods, while widely accepted, fail to fully harness the capabilities of advanced data analytics and predictive modeling techniques, resulting in a decline in the efficiency of broiler performance. This research project aims to analyze the business background and present performance status of De Heus in the broiler industry. It seeks to identify the reasons behind any issues and potential remedies by examining relevant use cases within the same industry. The ultimate goal is to develop AI solutions that can enhance broiler performance. Furthermore, our research has determined that environmental variables such as temperature and humidity have had a significant impact on broiler performance. As a result, we have developed a time-series forecasting model to predict broiler weight based on factors such as broiler age, feed intake, temperature, and humidity in order to validate our findings. Significantly, this project will develop an AI adoption path for De Heus, enabling the use of our solutions to enhance broiler performance. Additionally, it will propose the inclusion of missing metrics that De Heus has not incorporated into their data gathering system.