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The majority of warehouse expenses is driven by labour cost. Therefore, efficient management of labour resources is required. To do so, workforce planning is used to match the workforce capacity with the incoming workload. While doing so, it is often wrongly assumed that each worker has the same and constant capacity or performance. Addressing this, we build a model to predict the employee-based productivity of newly hired warehouse personnel that will support workforce planning by incorporating multiple data sources. To this end, we develop a framework to identify relevant variables in four categories: warehouse, operator, shift and product. We demonstrate that Extreme Gradient Boosting, using these variables may reduce the root mean squared error of the prediction by more than 50%. A comprehensive scenario analysis shows that improving productivity predictions translates into substantial cost savings. Furthermore, a sensitivity analysis identifies which variable categories should be favoured in the data collection process to achieve the best prediction results. Abbreviations:  CMA: Cumulative Moving Averages; EGB: Extreme Gradient Boosting; GB: Gradient Boosting; LSP: Logistics Service Provider; MAE: Mean Absolute Error; RF: Random Forest; RMSE: Root Mean Squared Error; SMA: Simple Moving Averages

Integrating operational and human factors to predict daily productivity of warehouse employees using extreme gradient boosting