Daifeng Li , Fengyun Gu , Xin Li , Ruo Du , Dingquan Chen , Andrew Madden
{"title":"基于自动学习和弹性调整机制的动态销售预测,用于库存优化","authors":"Daifeng Li , Fengyun Gu , Xin Li , Ruo Du , Dingquan Chen , Andrew Madden","doi":"10.1016/j.is.2023.102259","DOIUrl":null,"url":null,"abstract":"<div><p>The ability to predict product sales is invaluable for improving enterprises’ routine decisions of inventory optimization. The most effective solution of sales prediction in real applications is ensemble learning. One challenge of the solution is that it is hard to make timely and accurate predictions because of inadequate decision information and complicated and changeable sales environments. Besides, seeking optimal model combinations from the candidate set is often inaccurate and time-consuming. Another important challenge is that the predicted sales seldom consider “replenishment” of the inventory, which may lead to even higher cost. To address the challenges, we propose a novel dynamic sales prediction model with Auto-Learning and Elastic-Adjustment mechanisms (DSP-FAE): Dynamic sales prediction model can capture dynamic changing patterns of sales time series more effectively. Auto-Learning is used to automatically customize the optimal ensemble learning strategy for each warehouse-product combination in a more efficient way. Elastic-Adjustment is proposed to design a deep neural network-based adjustment factor to correct the predicted sales, which can significantly reduce inventory costs. Extensive offline and online experiments are conducted to verify the performance of the proposed model on two real-world datasets: Galanz and Cainiao. Experimental results show that the proposed DSP-FAE performs better than the selected 10 state-of-the-art baselines significantly in terms of MAE, RRSE and CORR. More importantly, it can save more than 20% inventory cost compared with traditional solutions.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic sales prediction with auto-learning and elastic-adjustment mechanism for inventory optimization\",\"authors\":\"Daifeng Li , Fengyun Gu , Xin Li , Ruo Du , Dingquan Chen , Andrew Madden\",\"doi\":\"10.1016/j.is.2023.102259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The ability to predict product sales is invaluable for improving enterprises’ routine decisions of inventory optimization. The most effective solution of sales prediction in real applications is ensemble learning. One challenge of the solution is that it is hard to make timely and accurate predictions because of inadequate decision information and complicated and changeable sales environments. Besides, seeking optimal model combinations from the candidate set is often inaccurate and time-consuming. Another important challenge is that the predicted sales seldom consider “replenishment” of the inventory, which may lead to even higher cost. To address the challenges, we propose a novel dynamic sales prediction model with Auto-Learning and Elastic-Adjustment mechanisms (DSP-FAE): Dynamic sales prediction model can capture dynamic changing patterns of sales time series more effectively. Auto-Learning is used to automatically customize the optimal ensemble learning strategy for each warehouse-product combination in a more efficient way. Elastic-Adjustment is proposed to design a deep neural network-based adjustment factor to correct the predicted sales, which can significantly reduce inventory costs. Extensive offline and online experiments are conducted to verify the performance of the proposed model on two real-world datasets: Galanz and Cainiao. Experimental results show that the proposed DSP-FAE performs better than the selected 10 state-of-the-art baselines significantly in terms of MAE, RRSE and CORR. More importantly, it can save more than 20% inventory cost compared with traditional solutions.</p></div>\",\"PeriodicalId\":50363,\"journal\":{\"name\":\"Information Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306437923000959\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437923000959","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Dynamic sales prediction with auto-learning and elastic-adjustment mechanism for inventory optimization
The ability to predict product sales is invaluable for improving enterprises’ routine decisions of inventory optimization. The most effective solution of sales prediction in real applications is ensemble learning. One challenge of the solution is that it is hard to make timely and accurate predictions because of inadequate decision information and complicated and changeable sales environments. Besides, seeking optimal model combinations from the candidate set is often inaccurate and time-consuming. Another important challenge is that the predicted sales seldom consider “replenishment” of the inventory, which may lead to even higher cost. To address the challenges, we propose a novel dynamic sales prediction model with Auto-Learning and Elastic-Adjustment mechanisms (DSP-FAE): Dynamic sales prediction model can capture dynamic changing patterns of sales time series more effectively. Auto-Learning is used to automatically customize the optimal ensemble learning strategy for each warehouse-product combination in a more efficient way. Elastic-Adjustment is proposed to design a deep neural network-based adjustment factor to correct the predicted sales, which can significantly reduce inventory costs. Extensive offline and online experiments are conducted to verify the performance of the proposed model on two real-world datasets: Galanz and Cainiao. Experimental results show that the proposed DSP-FAE performs better than the selected 10 state-of-the-art baselines significantly in terms of MAE, RRSE and CORR. More importantly, it can save more than 20% inventory cost compared with traditional solutions.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.