Optimizing Clustering Techniques for Retail Industry: One-Dimensional Time Warping Method for Demand Uncertainty

Mochamad Nizar Palefi Ma'Ady, Prita Meilanitasari, Nisa Isrofi, Iwan Vanany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Product availability is a crucial factor in maintaining customer satisfaction and securing revenue streams for retailers and suppliers. By clustering products based on demand patterns and inventory turnover, retailers can enhance efficiency and improve customer satisfaction. This study proposes optimizing product clustering by comparing dynamic time warping and k-means methods to find the best clustering solution. The results show that dynamic time warping outperforms the k-means algorithm, with a higher Silhouette score of 0.8406 compared to 0.7578. This indicates that our proposed method is more efficient in optimizing product clustering. Furthermore, the time complexity is faster compared to k-means. We also provide a numerical demonstration of the table-filling procedure for a small problem. Overall, this study offers insights into how retailers can improve their product clustering strategies to better meet customer needs and maximize revenue.

Original languageEnglish
Title of host publicationSIET 2023 - Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology
PublisherAssociation for Computing Machinery
Pages64-70
Number of pages7
ISBN (Electronic)9798400708503
DOIs
Publication statusPublished - 24 Oct 2023
Externally publishedYes
Event8th International Conference on Sustainable Information Engineering and Technology, SIET 2023 - Bali, Indonesia
Duration: 24 Oct 202325 Oct 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Sustainable Information Engineering and Technology, SIET 2023
Country/TerritoryIndonesia
CityBali
Period24/10/2325/10/23

Keywords

  • demand uncertainty
  • dynamic time warping
  • k-means algorithm
  • one-dimensional clustering
  • retail industry

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