A Knowledge-based Decision Support Framework for Wave Put-away Operations of E-commerce and O2O Shipments

DOI

10.2991/iwama-16.2016.16How to use a DOI?

Keywords

Logistics and supply chains; E-commerce; Wave management; Case-based reasoning

Abstract

Warehouse put-away and storage operation is one of the typical internal operations in a warehouse or a distribution center, alongside receiving, picking, packing and shipping. Under today's e-commerce business environment, logistics practitioners are required to efficiently handle e-commerce shipments in warehouses. However, owing to the fundamental differences in the order nature and handling requirements between traditional orders and e-commerce orders, logistics service providers are struggled to handle e-commerce shipments competently. In this paper, a knowledge-based wave put-away decision support system is proposed, which incorporates cloud database for real-time data update and retrieval, and case-based reasoning technique for generating put-away solutions for e-commerce orders based on historical put-away knowledge. The proposed system is validated through a pilot study in a case company. The efficiency of order put-away operation is enhanced by grouping fragmented e-commerce orders at the inbound for performing put-away operations in a wave pattern, which enables logistics practitioners to yield cost-saving and operating efficiency in e-commerce order handling.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - K.H. Leung
AU  - K.L. Choy
AU  - Migar M.C. Tam
AU  - Y.Y. Hui
AU  - H.Y. Lam
AU  - Y.P. Tsang
PY  - 2016/11
DA  - 2016/11
TI  - A Knowledge-based Decision Support Framework for Wave Put-away Operations of E-commerce and O2O Shipments
BT  - Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SP  - 86
EP  - 91
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.16
DO  - 10.2991/iwama-16.2016.16
ID  - Leung2016/11
ER  -