TY - GEN
T1 - Optimizing Clustering Techniques for Retail Industry
T2 - 8th International Conference on Sustainable Information Engineering and Technology, SIET 2023
AU - Ma'Ady, Mochamad Nizar Palefi
AU - Meilanitasari, Prita
AU - Isrofi, Nisa
AU - Vanany, Iwan
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/10/24
Y1 - 2023/10/24
N2 - 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.
AB - 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.
KW - demand uncertainty
KW - dynamic time warping
KW - k-means algorithm
KW - one-dimensional clustering
KW - retail industry
UR - http://www.scopus.com/inward/record.url?scp=85182397112&partnerID=8YFLogxK
U2 - 10.1145/3626641.3627602
DO - 10.1145/3626641.3627602
M3 - Conference contribution
AN - SCOPUS:85182397112
T3 - ACM International Conference Proceeding Series
SP - 64
EP - 70
BT - SIET 2023 - Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology
PB - Association for Computing Machinery
Y2 - 24 October 2023 through 25 October 2023
ER -