Abstract
Goods turnover is the core of digital warehouse operation, including many processes, such as receiving, picking, and packing of goods. Analyzing goods turnover data can generate valuable insights for optimizing warehouse management, thereby improving operation efficiency. However, most existing methods focus on partial processes, making it hard for warehouse managers to understand the operation state and the goods turnover patterns, which often require the analysis of the interrelated processes of goods turnover. In this paper, we abstract six types of goods turnover events to describe the warehouse operation workflow and present WarehouseLens, a visual analytics system to analyze goods turnover from an overall perspective. To understand the warehouse operation state, we propose a temporal visualization method consisting of a novel state calendar view and an improved circular heat map to reflect the trend and periodicity pattern of the operation state. To explore the goods turnover patterns, we provide an improved parallel coordinate plot for users to view the attribute distribution of goods to filter key goods and a tailored mode circle view to discover the frequent outbound mode of goods. Three case studies and expert interviews on a real-world warehouse dataset demonstrate the usefulness and effectiveness of WarehouseLens in revealing the warehouse operation state and goods turnover patterns.
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Acknowledgements
This research is partially supported by the School-City Cooperation Special Fund Project (2020CDSN-02) and School-City Strategic Cooperation Project (2021CDSN-13). We would like to thank the industry sponsor Sichuanwulianyida Technology Co., Ltd. for providing with the data.
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Chen, F., Li, J., Wang, F. et al. WarehouseLens: visualizing and exploring turnover events of digital warehouse. J Vis 26, 977–998 (2023). https://doi.org/10.1007/s12650-023-00913-7
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DOI: https://doi.org/10.1007/s12650-023-00913-7