Improving Operational Workforce Scheduling in a Warehouse Using Time Series Forecasting

Abstract

In order to differentiate from competitors in terms of customer service, warehouses accept late orders from customers while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. In order to reduce the order picker travel time per order, the warehouse can be divided into different order picking zones. Furthermore, an improved workforce planning can contribute to an effective and effcient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders the assumption of a constant given demand is reconsidered in this paper. The objective of this study is to present time series forecasting models which perform well in a warehouse context. Time series models are used to forecast daily number of order lines from a large international warehouse. The forecast of order lines, along with order picker's productivity, can be used by decision makers to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up, and top- down approach are evaluated in order to find the best performing method of forecasting in terms of RMSE, MAPE and MASE.