AutoML Approach to Stock Keeping Units Segmentation
Name
jtaer-17-00076.pdf
Size
1.06 MB
Format
Adobe PDF
Checksum (MD5)
773fa30d352bc0f4041863499b15eca5
Author(s)
Jackson, Ilya
Date Issued
November 15, 2022
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Journal of Theoretical and Applied Electronic Commerce Research 17 (4): 1512-1528 (2022)
Version
Final published version
Abstract
A typical retailer carries 10,000 stock-keeping units (SKUs). However, these numbers may exceed hundreds of millions for giants such as Walmart and Amazon. Besides the volume, SKU data can also be high-dimensional, which means that SKUs can be segmented on the basis of various attributes. Given the data volumes and the multitude of potentially important dimensions to consider, it becomes computationally impossible to individually manage each SKU. Even though the application of clustering for SKU segmentation is common, previous studies do not address the problem of parametrization and model finetuning, which may be extremely tedious and time-consuming in real-world applications. Our work closes the research gap by proposing a solution that leverages automated machine learning for the automated cluster analysis of SKUs. The proposed framework for automated SKU segmentation incorporates minibatch K-means clustering, principal component analysis, and grid search for parameter tuning. It operates on top of the Apache Parquet file format, an efficient, structured, compressed, column-oriented, and big-data-friendly format. The proposed solution was tested on the basis of a real-world dataset that contained data at the pallet level.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/jtaer17040076
