基于使用爱因斯坦模糊方法的大数据技术在食品供应链中实现循环
Rohham Farzadnia1, Payam Shojaei2, Seyed Hadi Mirghaderi1
1Department of Management, Shiraz University, Shiraz, Iran.
Scientific reports
|August 30, 2025
概括
循环供应链可以减少浪费, 大数据解决方案,特别是社交网络分析和优化模型, 是克服伊朗食品行业这些挑战的关键.
科学领域:
- 供应链管理
- 工业工程
- 数据科学
背景情况:
- 供应链中的循环增强效率,但面临采用风险.
- 伊朗食品供应链中循环性障碍的研究有限.
- 大数据分析和工业4.0技术是提出的解决方案.
研究的目的:
- 确定和验证伊朗食品行业循环供应链的障碍.
- 优先考虑大数据驱动的解决方案来克服已识别的障碍.
- 应用新的模糊MCDM方法来管理不确定性.
主要方法:
- 用于识别障碍的文献审查和内容有效率分析.
- 模糊的SWARA (步骤评估比率分析) 用于屏障加权.
- 爱因斯坦的模糊的WASPAS (权重总和产品评估) 用于大数据技术的优先级.
主要成果:
- 主要障碍包括缺乏组织基础设施和可追溯性系统.
- 社交网络分析 (SNA) 和优化模型是最有效的大数据解决方案.
- 这项研究是首次在CSCM中应用爱因斯坦的模糊WASPAS.
结论:
- 大数据技术,特别是SNA和优化,可以缓解循环供应链障碍.
- 这些研究结果为食品行业的管理者提供了指导,帮助他们采用循环化的智能技术.
- 这项研究为CSCM管理不确定性提供了框架.
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