揭示逆向物流决策:发展中国家与发达国家电子行业的社会媒体分析研究

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
M. Shahidzadeh, Sajjad Shokouhyar
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引用次数: 8

摘要

摘要人口的增长导致自然资源的浪费和枯竭。此外,提供一些资源的成本大幅增加。因此,制造商正试图专注于计划收回旧的或部分/完全不可用的产品,并对其做出最佳处置决定。本研究旨在使用社交媒体分析中的深度学习方法建立一个多行业应用模型,以在逆向物流中对退货产品做出最佳决策,同时考虑可持续性和循环经济问题。此外,我们概述了社交网络分析在将消费者的期望与供应链政策、战略和决策相一致方面的用途。通过将所提出的模型应用于不同的行业,可以获得有关循环经济概念的行业基准。我们提出了一个可推广的模型,使用社交媒体分析、消费者情绪分析、逆向物流和循环经济理论,以实现关于可持续性问题的循环供应链。将所提出的模型应用于电子行业作为案例研究,通过Twitter对发展中国家与发达国家笔记本电脑设备的数据分析,进一步验证了该模型。在15个月的时间里,我们使用Twitter应用程序编程接口(API)收集了7000多万条推文。研究结果通过利用推特地理位置属性从发展中国家和发达国家提取推特数据,批准了所提出的模型。此外,该模型足够通用,可用于各个行业的供应链,并为管理者和决策者提供了对逆向物流决策的深入见解。在未来的工作中使用实时分析并提高准确性将是一件有趣的事情。我们为循环经济背景下的逆向物流决策做出了原创性贡献。先前的研究侧重于供应链决策,通过为社交媒体分析和循环经济生态系统提供理论和实践启示,得到了扩展。因此,通过仔细审查消费者的需求和期望,我们建议对退货产品做出最佳决定,以关闭开放式供应链,实现循环经济。此外,我们分别得出了发展中国家和发达国家的行业基准。结果表明,发展中国家退货的最佳决策与发达国家不同。我们建议高级管理人员和决策者在发展中国家和发达国家使用社交媒体分析来提高供应链的可持续性,以大幅优化浪费和公司利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shedding light on the reverse logistics’ decision-making: a social-media analytics study of the electronics industry in developing vs developed countries
ABSTRACT Growing population leads to generating more waste and depletion of natural resources. Moreover, the cost of supplying some resources has increased substantially. Hence, the manufacturer is trying to focus on planning to get back old or partially/wholly unusable products and make the best disposition decisions on them. This research aims to build a multi-industry applied model using the deep learning method in social media analysis to make the best decision for returning products in reverse logistics, along with the sustainability and circular economy concerns. Furthermore, we outline the usage of social network analytics in aligning consumers’ expectations with supply chain policies, strategies, and decisions. An industry benchmark concerning circular economy concepts can be attained by applying the proposed model to different industries. We have proposed a generalisable model using social media analytics, consumer sentiment analysis, reverse logistics, and circular economy theory to attain a circular supply chain regarding sustainability concerns. Applying the proposed model to the electronics industry as a case study, the model was further validated with Twitter data analysis of developing versus developed countries for laptop devices. We collected over 70-million tweets using the Twitter Application Programming Interface (API) over fifteen months. The results approved the proposed model by leveraging the Twitter geolocation attribute to extract Twitter data from developing and developed countries. Moreover, the model is general enough to be used on various industries’ supply chains and provides managers and policymakers with deep insight into reverse logistics’ decision-making. It would be interesting to use real-time analytics and improve accuracy in future works. We made original contributions to reverse logistics decision-making in the circular economy context. Previous research, which has focused on supply chain decision-making, has been extended by providing theoretical and practical implications for social media analytics and the circular economy ecosystem. Thus, by scrutinising the consumers’ needs and expectations, we suggested the best decision on returned products to close an open-ended supply chain and achieve a circular economy. Furthermore, we derived industry benchmarks for both developing and developed countries separately. The results showed that the best decision on returning products in developing countries is different from developed countries. We advise top managers and policymakers to improve supply chain sustainability using social media analytics in developing and developed countries to substantially optimise waste and companies’ profits.
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来源期刊
International Journal of Sustainable Engineering
International Journal of Sustainable Engineering GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
7.70
自引率
0.00%
发文量
19
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