Partial information sharing in supply chains with ARMA demand

IF 1.9 4区 管理学 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Naval Research Logistics Pub Date : 2024-09-09 DOI:10.1002/nav.22227
Vladimir Kovtun, Avi Giloni, Clifford Hurvich, Noam Shamir
{"title":"Partial information sharing in supply chains with ARMA demand","authors":"Vladimir Kovtun, Avi Giloni, Clifford Hurvich, Noam Shamir","doi":"10.1002/nav.22227","DOIUrl":null,"url":null,"abstract":"In this paper we suggest a novel mechanism for information sharing that allows a retailer to control the amount of shared information, and thus to limit information leakage, while still assisting the supplier to make better‐informed decisions and improve the overall efficiency of the supply chain. The control of the amount of leaked information facilitates information sharing because, absent such control, a retailer may refrain from sharing information due to the concern of information leakage. Specifically, we analyze a supply chain in which a retailer observes Autoregressive Moving Average (ARMA) demand for a single product where all players use the myopic order‐up‐to policy for determining their orders. We introduce a new class of information sharing arrangements, coined partial‐information shock (PaIS) sharing. This new class of information sharing agreements extends the previously studied mechanisms of demand sharing and full‐information shock sharing. We demonstrate that the retailer can construct a PaIS sharing arrangement that allows for an intermediate level of information sharing while simultaneously controlling the amount of leakage. We characterize when one PaIS arrangement will be more valuable to the supplier than another. We conclude with a numerical study that highlights that there does not necessarily need to be a tradeoff between the supplier having a better forecast and the retailer experiencing a higher level of leakage.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Naval Research Logistics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1002/nav.22227","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper we suggest a novel mechanism for information sharing that allows a retailer to control the amount of shared information, and thus to limit information leakage, while still assisting the supplier to make better‐informed decisions and improve the overall efficiency of the supply chain. The control of the amount of leaked information facilitates information sharing because, absent such control, a retailer may refrain from sharing information due to the concern of information leakage. Specifically, we analyze a supply chain in which a retailer observes Autoregressive Moving Average (ARMA) demand for a single product where all players use the myopic order‐up‐to policy for determining their orders. We introduce a new class of information sharing arrangements, coined partial‐information shock (PaIS) sharing. This new class of information sharing agreements extends the previously studied mechanisms of demand sharing and full‐information shock sharing. We demonstrate that the retailer can construct a PaIS sharing arrangement that allows for an intermediate level of information sharing while simultaneously controlling the amount of leakage. We characterize when one PaIS arrangement will be more valuable to the supplier than another. We conclude with a numerical study that highlights that there does not necessarily need to be a tradeoff between the supplier having a better forecast and the retailer experiencing a higher level of leakage.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
具有 ARMA 需求的供应链中的部分信息共享
在本文中,我们提出了一种新的信息共享机制,它允许零售商控制共享信息的数量,从而限制信息泄漏,同时还能帮助供应商做出更明智的决策,提高供应链的整体效率。对泄漏信息量的控制有利于信息共享,因为如果没有这种控制,零售商可能会因为担心信息泄漏而不共享信息。具体来说,我们分析了一个供应链,在这个供应链中,零售商观察到单一产品的自回归移动平均(ARMA)需求,所有参与者都使用近视的 "从订单到订单"(order-up-to)策略来决定他们的订单。我们引入了一类新的信息共享安排,称为部分信息冲击(PaIS)共享。这一类新的信息共享协议扩展了之前研究过的需求共享和完全信息冲击共享机制。我们证明,零售商可以构建一种 PaIS 共享安排,在控制信息泄露量的同时,实现中间水平的信息共享。我们描述了何时一种 PaIS 安排比另一种安排对供应商更有价值。最后,我们通过数值研究强调,在供应商获得更好的预测和零售商经历更高水平的泄漏之间,并不一定需要权衡利弊。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Naval Research Logistics
Naval Research Logistics 管理科学-运筹学与管理科学
CiteScore
4.20
自引率
4.30%
发文量
47
审稿时长
8 months
期刊介绍: Submissions that are most appropriate for NRL are papers addressing modeling and analysis of problems motivated by real-world applications; major methodological advances in operations research and applied statistics; and expository or survey pieces of lasting value. Areas represented include (but are not limited to) probability, statistics, simulation, optimization, game theory, quality, scheduling, reliability, maintenance, supply chain, decision analysis, and combat models. Special issues devoted to a single topic are published occasionally, and proposals for special issues are welcomed by the Editorial Board.
期刊最新文献
Partial information sharing in supply chains with ARMA demand Efficient online estimation and remaining useful life prediction based on the inverse Gaussian process Double‐sided queues and their applications to vaccine inventory management Optimal condition‐based parameter learning and mission abort decisions Single machine scheduling with the total weighted late work and rejection cost
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1