减少碳足迹和促进资源的可持续性,在零售行业通过防止死库存

IF 3.7 Q2 ENVIRONMENTAL SCIENCES
Richard Li, Anthony SF. Chiu, Rosemary Seva
{"title":"减少碳足迹和促进资源的可持续性,在零售行业通过防止死库存","authors":"Richard Li,&nbsp;Anthony SF. Chiu,&nbsp;Rosemary Seva","doi":"10.1016/j.clrc.2023.100150","DOIUrl":null,"url":null,"abstract":"<div><p>In the retail industry, majority of the traditional inventory management methods commonly focus on minimizing business costs but fail to consider implications on environmental sustainability. Existing inventory classification methods have failed to consider the interaction among the different performance indicators for a timely recognition of the potential dead stocks and dead stocks in the inventory. This study aims to identify and effectively classify potential dead stocks and dead stocks through an enhanced inventory classification method that integrates the remaining product shelf life as an indicator for timely recognition of dead stocks. The study also aims to provide guidelines in setting up decision rules needed in objectively classifying inventory based on each performance indicator through an automated algorithm with decision rules. Using data sets taken from open databases that provide real-world data, results show that the automated algorithm can effectively detect the presence of dead stocks and distinguish such inventory from potential dead stocks in the system which existing inventory classification methods are unable to do. One-Way ANOVA Tests performed showed that the proposed classification method can consistently classify inventory across different retail data sets while Monte Carlo simulation was used to simulate the amount of inventory that becomes dead stocks in a multi-period inventory management system. Results show that 17% of these potential dead stocks, if undetected, become dead stocks in the following period. Over a ton of carbon emissions is prevented with lesser dead stock waste disposal. The value of this study lies in the fact that classifying inventory to detect the presence of potential dead stocks is an essential first step in solving the dead stock problem not just for business profitability but also for sustainable resource consumption and a sustainable environment.</p></div>","PeriodicalId":34617,"journal":{"name":"Cleaner and Responsible Consumption","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666784323000517/pdfft?md5=ee67b3a42ef6b7c04d01003a0032dbb9&pid=1-s2.0-S2666784323000517-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Reducing carbon footprint and promoting resource sustainability in the retail industry through the prevention of dead stocks\",\"authors\":\"Richard Li,&nbsp;Anthony SF. Chiu,&nbsp;Rosemary Seva\",\"doi\":\"10.1016/j.clrc.2023.100150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the retail industry, majority of the traditional inventory management methods commonly focus on minimizing business costs but fail to consider implications on environmental sustainability. Existing inventory classification methods have failed to consider the interaction among the different performance indicators for a timely recognition of the potential dead stocks and dead stocks in the inventory. This study aims to identify and effectively classify potential dead stocks and dead stocks through an enhanced inventory classification method that integrates the remaining product shelf life as an indicator for timely recognition of dead stocks. The study also aims to provide guidelines in setting up decision rules needed in objectively classifying inventory based on each performance indicator through an automated algorithm with decision rules. Using data sets taken from open databases that provide real-world data, results show that the automated algorithm can effectively detect the presence of dead stocks and distinguish such inventory from potential dead stocks in the system which existing inventory classification methods are unable to do. One-Way ANOVA Tests performed showed that the proposed classification method can consistently classify inventory across different retail data sets while Monte Carlo simulation was used to simulate the amount of inventory that becomes dead stocks in a multi-period inventory management system. Results show that 17% of these potential dead stocks, if undetected, become dead stocks in the following period. Over a ton of carbon emissions is prevented with lesser dead stock waste disposal. The value of this study lies in the fact that classifying inventory to detect the presence of potential dead stocks is an essential first step in solving the dead stock problem not just for business profitability but also for sustainable resource consumption and a sustainable environment.</p></div>\",\"PeriodicalId\":34617,\"journal\":{\"name\":\"Cleaner and Responsible Consumption\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666784323000517/pdfft?md5=ee67b3a42ef6b7c04d01003a0032dbb9&pid=1-s2.0-S2666784323000517-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cleaner and Responsible Consumption\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666784323000517\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner and Responsible Consumption","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666784323000517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

在零售业中,大多数传统的库存管理方法通常侧重于最小化业务成本,而没有考虑对环境可持续性的影响。现有的库存分类方法未能考虑不同绩效指标之间的相互作用,无法及时识别库存中的潜在死库存和死库存。本研究旨在通过以产品剩余货架期为指标,及时识别死库存的增强型库存分类方法,对潜在死库存和死库存进行识别和有效分类。通过具有决策规则的自动化算法,为基于各绩效指标客观分类库存提供决策规则的建立指南。使用开放数据库提供的真实数据集,结果表明,自动化算法可以有效地检测系统中是否存在死库存,并将这些库存与潜在的死库存区分开来,这是现有库存分类方法无法做到的。单因素方差分析表明,所提出的分类方法可以对不同零售数据集的库存进行一致的分类,而蒙特卡罗模拟用于模拟多周期库存管理系统中成为死库存的库存数量。结果表明,如果未被发现,这些潜在的死股中有17%在接下来的一段时间内成为死股。减少了超过一吨的碳排放,减少了垃圾处理。本研究的价值在于,对库存进行分类以检测潜在死库存的存在是解决死库存问题必不可少的第一步,这不仅是为了企业盈利,也是为了可持续的资源消耗和可持续的环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing carbon footprint and promoting resource sustainability in the retail industry through the prevention of dead stocks

In the retail industry, majority of the traditional inventory management methods commonly focus on minimizing business costs but fail to consider implications on environmental sustainability. Existing inventory classification methods have failed to consider the interaction among the different performance indicators for a timely recognition of the potential dead stocks and dead stocks in the inventory. This study aims to identify and effectively classify potential dead stocks and dead stocks through an enhanced inventory classification method that integrates the remaining product shelf life as an indicator for timely recognition of dead stocks. The study also aims to provide guidelines in setting up decision rules needed in objectively classifying inventory based on each performance indicator through an automated algorithm with decision rules. Using data sets taken from open databases that provide real-world data, results show that the automated algorithm can effectively detect the presence of dead stocks and distinguish such inventory from potential dead stocks in the system which existing inventory classification methods are unable to do. One-Way ANOVA Tests performed showed that the proposed classification method can consistently classify inventory across different retail data sets while Monte Carlo simulation was used to simulate the amount of inventory that becomes dead stocks in a multi-period inventory management system. Results show that 17% of these potential dead stocks, if undetected, become dead stocks in the following period. Over a ton of carbon emissions is prevented with lesser dead stock waste disposal. The value of this study lies in the fact that classifying inventory to detect the presence of potential dead stocks is an essential first step in solving the dead stock problem not just for business profitability but also for sustainable resource consumption and a sustainable environment.

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
Cleaner and Responsible Consumption
Cleaner and Responsible Consumption Social Sciences-Social Sciences (miscellaneous)
CiteScore
4.70
自引率
0.00%
发文量
40
审稿时长
99 days
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信