Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost

Year 2021, Volume: 4 Issue: 2, 19 - 24, 31.12.2021

İrem Kalafat Mustafa Hekimoğlu Ahmet Deniz Yücekaya Nilay Ay Habib Gültekin

Abstract

Effective management of warehouse processes is essential in order to maintain high-level service quality and keep the costs at optimum. Each item passes through numerous workstations during their journey in warehouses from the entrepot to the shipping area. Accurate estimation of workload at stations allows personnel assignment optimization and the increase of the warehouse performance. Otherwise, it causes personnel shortages at stations, delays in shipment commitment dates and disruptions in warehouse activities. In this paper, time series forecasting models are used to estimate the load in each workstation for a better operation. The proposed methodologies are applied to an automotive spare part warehouse in Turkey. The classical time series method, which performs best in estimating the workload of each workstation, is presented and these results are compared with the XGBoost model. Thus, the models that give the best results for each station are shown. The proposed research covers part acceptance, storage, order picking and packaging processes and their substations, which were not considered in previous studies.

Keywords

Machine Learning, Warehouse Management, Workload Prediction, Time Series, XGBoost

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