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Data-driven warehouse design: a decision tree approach with integrated mobile application

A. Sumesh et al.

International Journal of Management Science and Engineering Management2025https://doi.org/10.1080/17509653.2025.2523262article
ABDC C
Weight
0.48

What the paper says

The complexity of handling several customers’ materials, assemblies, and products with diversified parameters has posed challenges to effectively designing warehouses. Warehouse design plays a crucial role in the supply chain by buffering material flow to ensure smooth operations despite fluctuations in demand and unforeseen disruptions. This research proposes a novel method to optimise warehouse design that utilises decision tree algorithms by incorporating critical factors such as the available space, product size and type, material handling equipment needs, inventory volume, and order frequency. The research involves developing a viable and user-friendly mobile application for warehouse professionals. The application can provide optimised recommendations for racking systems, layout configurations, and the number of aisles required. The system is validated through a case study of a telecom company’s warehouse, which revealed significant improvements: a 20% increase in space utilisation, a 15% reduction in order-picking time, and a 10% decrease in operational costs compared to traditional methods, which highlights significant improvements over traditional design methods. This research contributes to warehouse management by offering a practical, data-driven tool for designing efficient and adaptive layouts.

5 citations

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https://doi.org/https://doi.org/10.1080/17509653.2025.2523262

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@article{a.2025,
  title        = {{Data-driven warehouse design: a decision tree approach with integrated mobile application}},
  author       = {A. Sumesh et al.},
  journal      = {International Journal of Management Science and Engineering Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/17509653.2025.2523262},
}

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Data-driven warehouse design: a decision tree approach with integrated mobile application

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.