信息资源管理学报 ›› 2018, Vol. 8 ›› Issue (1): 104-封3.doi: 10.13365/j.jirm.2018.01.104

• 研究论文 • 上一篇    

基于知识图谱分析的库存管理研究

叶勇   

  • 收稿日期:2017-04-25 出版日期:2018-01-26 发布日期:2018-01-26
  • 作者简介:叶勇,男,硕士生导师,副教授,研究方向为物流情报决策、物流信息技术研究,Email:yeyong@ahau.edu.cn。
  • 基金资助:

    本文系国家自然科学面上基金“基于知识图谱的农业大数据碎片化知识发现方法研究”(31771679)、安徽省高等学校省级教学研究重点项目 “基于知识图谱构建的《仓储与库存控制》课程教学优化及实践”(2016jyxm0308)的研究成果。

Knowledge Mapping Analysis on the Inventory Management

Ye Yong   

  • Received:2017-04-25 Online:2018-01-26 Published:2018-01-26

摘要:

本文主要针对库存管理的研究热点构建知识图谱,为该领域的研究提供可视化文献依据和参考。本文以Web of Science核心数据库1986—2017年库存管理领域的期刊为研究对象,使用 Citespace 5.0.R4和VOSviewer绘制库存管理的知识图谱并提取聚类主题词,基于共词分析抽取出库存优化策略、库存定价、库存技术三大专题集群,然后分析了聚类的结构及时间演化。用书目共现分析系统BICOMB提取出库存管理研究领域“期刊”和“研究者”关键字词频,通过被引半衰期、中心度、频次和关键字等参数对三个专题集群进行数据挖掘,结果表明:库存管理领域研究所关注的经典主题包括经济订购批量、动态定价、设计技术等方面,新兴主题包括渠道协调、价格分级仿真技术等方面。

关键词: 库存管理, 知识图谱, 共词分析, 聚类分析

Abstract:

The research mainly aims at identifying the hotspots of the inventory management by drawing knowledge maps and providing the visualized reference in this field. Firstly, this study takes inventory management articles in web of sciences during 1986 to 2017 as the research objects. Then it gives the inventory management knowledge maps and extracts the clustering keywords by using Citespace 5.0.R4 and VOSviewer. Based on co-word analysis, the three special clusters are found: inventory optimization strategy, inventory pricing and inventory technology. In the end, it analyzes the structure and evolution of the three clusters. Bibliographic item co-occurrence matrix builder (BICOMB) is used to extract keywords of the “journal” and “researcher” in the inventory management research fields. Setting three parameters such as the cited half-life, centrality, frequency and keywords for data mining, it shows that inventory management research focused on the classic topics including economic order quantity, dynamic pricing, design and technology, etc, and the new topics such as channel coordination, hierarchical price and simulation.

Key words: Inventory management, Knowledge mapping, Co-word analysis, Clustering analysis

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