Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.

Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.

Abstract: A system and method are disclosed to generate causal inference machine learning models employing statistical background subtraction. Embodiments include a server comprising a processor and memory. Embodiments receive historical sales data for one or more past time periods and corresponding historical data for one or more causal variables. Embodiments deconfound the cause-effect relationship of historical sales data and historical data on the one or more causal variables. Embodiments define one or more sample weights for statistical background subtraction of the historical data and perform statistical background subtraction on the historical data. Embodiments train a first machine learning model to predict an absolute individual causal effect on a considered demand quantity in relation to the one or more causal variables and one or more sample weights.

Abstract: A system and method are disclosed to generate causal inference machine learning models employing statistical background subtraction. Embodiments include a server comprising a processor and memory. Embodiments receive historical sales data for one or more past time periods and corresponding historical data for one or more causal variables. Embodiments deconfound the cause-effect relationship of historical sales data and historical data on the one or more causal variables. Embodiments define one or more sample weights for statistical background subtraction of the historical data and perform statistical background subtraction on the historical data. Embodiments train a first machine learning model to predict an absolute individual causal effect on a considered demand quantity in relation to the one or more causal variables and one or more sample weights.

Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.

Abstract: A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.