新产品系列需求规划:解决 SKU 级价差偏差

IF 11.2 2区 管理学 Q1 MANAGEMENT Journal of Business Logistics Pub Date : 2024-02-18 DOI:10.1111/jbl.12373
Lance W. Saunders, Jason R. W. Merrick, Chad W. Autry, Mary C. Holcomb
{"title":"新产品系列需求规划:解决 SKU 级价差偏差","authors":"Lance W. Saunders,&nbsp;Jason R. W. Merrick,&nbsp;Chad W. Autry,&nbsp;Mary C. Holcomb","doi":"10.1111/jbl.12373","DOIUrl":null,"url":null,"abstract":"<p>New product supply chain planning is challenging, primarily due to the lack of historical demand data. Rarely, however, do the academic literature or companies differentiate the demand forecasting process for new products from existing ones, despite their increased reliance on judgmental estimates. This research focuses on how judgmental errors lead to an under-estimation of the difference between the highest- and lowest-demand stock-keeping units (SKUs), and consequently negatively impact supply chain planning for new product family introductions. A generalized empirical model and accompanying discrete event simulation are developed and applied to data from a major consumer packaged goods (CPG) firm during the launch of a new cosmetics product family. This application allows us to identify a focal type of judgmental error (identified as the SKU-level spread bias) inherent to new product forecasting and to provide a new theoretical understanding of how this type of bias harms supply chain performance. Via an empirically driven theory-building approach that iterates between the simulation outcomes and existing literature, SKU-level spread bias is demonstrated to harm demand forecasts and, thereby, supply chain plans. Our unique theory-building approach advances theory by identifying planner SKU-level spread bias as a new source of bias that firms should seek to mitigate when introducing new product families.</p>","PeriodicalId":48090,"journal":{"name":"Journal of Business Logistics","volume":"45 2","pages":""},"PeriodicalIF":11.2000,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"New product family demand planning: Addressing SKU-level spread bias\",\"authors\":\"Lance W. Saunders,&nbsp;Jason R. W. Merrick,&nbsp;Chad W. Autry,&nbsp;Mary C. Holcomb\",\"doi\":\"10.1111/jbl.12373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>New product supply chain planning is challenging, primarily due to the lack of historical demand data. Rarely, however, do the academic literature or companies differentiate the demand forecasting process for new products from existing ones, despite their increased reliance on judgmental estimates. This research focuses on how judgmental errors lead to an under-estimation of the difference between the highest- and lowest-demand stock-keeping units (SKUs), and consequently negatively impact supply chain planning for new product family introductions. A generalized empirical model and accompanying discrete event simulation are developed and applied to data from a major consumer packaged goods (CPG) firm during the launch of a new cosmetics product family. This application allows us to identify a focal type of judgmental error (identified as the SKU-level spread bias) inherent to new product forecasting and to provide a new theoretical understanding of how this type of bias harms supply chain performance. Via an empirically driven theory-building approach that iterates between the simulation outcomes and existing literature, SKU-level spread bias is demonstrated to harm demand forecasts and, thereby, supply chain plans. Our unique theory-building approach advances theory by identifying planner SKU-level spread bias as a new source of bias that firms should seek to mitigate when introducing new product families.</p>\",\"PeriodicalId\":48090,\"journal\":{\"name\":\"Journal of Business Logistics\",\"volume\":\"45 2\",\"pages\":\"\"},\"PeriodicalIF\":11.2000,\"publicationDate\":\"2024-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Business Logistics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jbl.12373\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Logistics","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jbl.12373","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

摘要

新产品供应链规划具有挑战性,主要原因是缺乏历史需求数据。然而,尽管新产品越来越依赖于判断估计,学术文献或企业却很少将新产品的需求预测过程与现有产品的需求预测过程区分开来。本研究的重点是判断错误如何导致对最高需求量和最低需求量库存单位(SKU)之间的差异估计不足,从而对新产品系列推出的供应链规划产生负面影响。我们开发了一个广义的经验模型和相应的离散事件模拟,并将其应用于一家大型消费包装品(CPG)公司在推出新的化妆品产品系列时的数据。通过这一应用,我们发现了新产品预测中固有的一种焦点判断错误(即 SKU 级价差偏差),并从理论上对这种偏差如何损害供应链绩效提供了新的认识。通过在模拟结果和现有文献之间反复推敲的经验驱动型理论构建方法,SKU 级价差偏差被证明会损害需求预测,进而损害供应链计划。我们独特的理论构建方法将计划人员的 SKU 级价差偏差确定为一种新的偏差来源,企业在引入新产品系列时应设法减少这种偏差,从而推进了理论的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
New product family demand planning: Addressing SKU-level spread bias

New product supply chain planning is challenging, primarily due to the lack of historical demand data. Rarely, however, do the academic literature or companies differentiate the demand forecasting process for new products from existing ones, despite their increased reliance on judgmental estimates. This research focuses on how judgmental errors lead to an under-estimation of the difference between the highest- and lowest-demand stock-keeping units (SKUs), and consequently negatively impact supply chain planning for new product family introductions. A generalized empirical model and accompanying discrete event simulation are developed and applied to data from a major consumer packaged goods (CPG) firm during the launch of a new cosmetics product family. This application allows us to identify a focal type of judgmental error (identified as the SKU-level spread bias) inherent to new product forecasting and to provide a new theoretical understanding of how this type of bias harms supply chain performance. Via an empirically driven theory-building approach that iterates between the simulation outcomes and existing literature, SKU-level spread bias is demonstrated to harm demand forecasts and, thereby, supply chain plans. Our unique theory-building approach advances theory by identifying planner SKU-level spread bias as a new source of bias that firms should seek to mitigate when introducing new product families.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
14.40
自引率
14.60%
发文量
34
期刊介绍: Supply chain management and logistics processes play a crucial role in the success of businesses, both in terms of operations, strategy, and finances. To gain a deep understanding of these processes, it is essential to explore academic literature such as The Journal of Business Logistics. This journal serves as a scholarly platform for sharing original ideas, research findings, and effective strategies in the field of logistics and supply chain management. By providing innovative insights and research-driven knowledge, it equips organizations with the necessary tools to navigate the ever-changing business environment.
期刊最新文献
The Impact of Product Packaging Characteristics on Order Picking Performance in Grocery Retailing Issue Information Consumer-Centric Supply Chain Management: A Literature Review, Framework, and Research Agenda Supply Chain Plasticity: A Responsive Network Capability to Ensure Resilience Last-Mile Delivery: A Process View, Framework, and Research Agenda
×
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