Forecasting milk delivery to dairy – How modern statistical and machine learning methods can contribute

•Unique dataset from Norway’s largest dairy company.•Analysis based on domain knowledge and machine learning models.•Identification of important dairy herd features in forecasting milk deliveries.•Machine Learning models performs well in forecasting milk deliveries.•Discussion of models’ property for short- and long-term forecasts. Accurate forecasts of milk deliveries to dairy are crucial to secure a stable milk supply to customers, the effective use of raw milk, and the reduction of logistics costs and greenhouse gas emissions. The primary aim of this study was to improve the prediction of the total milk supply from Norwegian dairy farmers to dairies, and to do so by utilizing a traditional time series model, as well as several modern statistical models and machine learning algorithms. The secondary aim was to explore the usefulness of dairy herd features as explanatory variables to forecast the target variable milk delivery to dairy. Several statistical learning models and machine learning algorithms were applied for forecasting, in addition to a traditional time series model and qualitative domain knowledge. Historical data of monthly milk deliveries were collected from Norwegian dairy companies, together with detailed dairy farm herd records. The herd records contain features or explanatory variables, such as the number of cows and inseminations that can influence the response variable – the monthly milk deliveries. Our results show that some of the models applied can predict monthly milk deliveries to dairies with a mean absolute prediction error of 1–2% up to 24-months ahead. While a generalized linear model performed the best up to 3-months ahead, the machine learning models performed the best up to one year ahead. Two years ahead a traditional seasonal autoregressive model performed the best. Because some of the models select the most important features to predict milk delivery, our analytical results also provide valuable information about which features are the most important to predict future milk delivery.