通过分析部门级用户特征改进供应链人机系统

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
Kun Yang
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引用次数: 0

摘要

在本研究中,作者旨在通过以用户为中心的系统设计,提高 A 公司(中国制造业的重要企业)的供应链效率。该方法包括分析部门级用户特征,为开发优化的人机界面提供信息。为此,他们开展了焦点小组和卡片分类活动,确定了计划、采购、收货、仓储、生产和质量控制等部门的具体用户需求。将这些见解应用到系统设计中,显著改善了运营。值得注意的是,收货(74.44%)和质量控制(41.86%)等部门节省了时间。这种量身定制的方法强调了理解用户特征并将其融入供应链系统的重要性,从而提高了生产率和竞争优势。这项研究证明了以用户为中心的系统设计对提高供应链管理效率和用户满意度的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics
In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management.
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来源期刊
Information Resources Management Journal
Information Resources Management Journal INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
3.60
自引率
7.10%
发文量
44
期刊介绍: Topics should be drawn from, but not limited to, the following areas, with major emphasis on the managerial and organizational aspects of information resource and technology management: •Application of IT to operation •Artificial intelligence and expert systems technologies and issues •Business process management and modeling •Data warehousing and mining •Database management technologies and issues •Decision support and group decision support systems •Distance learning technologies and issues •Distributed software development •E-collaboration •Electronic commerce technologies and issues •Electronic government •Emerging technologies management
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