Congestion in the Reversed Order Picking System of Royal FloraHolland

In picker-to-parts order picking systems interactions between workers occur as pick face blockings, indicating that two workers try to access the same location at the same moment in time. Existing literature on warehouse congestion mostly considers theoretical modelling, making it difficult for warehouse managers to assess their operations’ performance. Therefore, this thesis bridges the gap between theory and practice by creating real-life insights in warehouse congestion through a data-driven approach. The case study on a reversed order picking system shows that data mining is required in order to retrieve valuable information on warehouse congestion. This data mining enables the identification of pick face blockings and quantification of the time that is lost due to congestion accordingly. Moreover, regression analysis shows that congestion is caused by the number of stops made by workers in the system, total time spent on transferring products and the number of locations that has been accessed.