@inproceedings{0a01d51219b946079110f925c66faa79,
title = "A Machine Learning Predictive Model for Shipment Delay and Demand Forecasting for Warehouses and Sales Data",
abstract = "In the era of Industry 4.0, various technologies have been great assisting tools for different industries to improve their work efficiency and enhance customers' satisfaction and loyalty. This paper discusses the issue of shipment delay and sales prediction by understanding how various attributes, based on data from the retail industry, will impact on their result. It was found that the locational factors and product category have been determining fluctuating sales or prominent delays. The results of machine learning algorithms are also discussed on how a better correlation in forecast and attribute relationships can be attained.",
keywords = "Demand forecasting, Machine Learning, Predictive model, Shipment delay, Warehouse",
author = "Keung, {K. L.} and Lee, {C. K.M.} and Yiu, {Y. H.}",
note = "Funding Information: This work was supported by the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong. Our gratitude is also extended to the Research Committee and the Department of Industrial and Systems Engineering (RK2F), The Hong Kong Polytechnic University, Hong Kong. The authors would like to express their appreciation to the anonymous case company for their assistance with the data collection. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021 ; Conference date: 13-12-2021 Through 16-12-2021",
year = "2021",
month = dec,
doi = "10.1109/IEEM50564.2021.9672946",
language = "English",
series = "2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1010--1014",
booktitle = "2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021",
}