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[1]李勇坚.“数据要素×”赋能制造业:理论逻辑与实现路径[J].长安大学学报(社科版),2024,(02):54-70.
　LI Yongjian.“Data element ” empowers manufacturing industry:theoretical logic and implementation path[J].Journal of Chang＇an University(Social Science Edition),2024,(02):54-70.
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《长安大学学报（社科版）》[ISSN:1671-6248/CN:61-1391/C]

卷:
期数:
2024年02期
页码:
54-70
栏目:
经济学·“数据要素×”研究
出版日期:
2024-04-20

文章信息/Info

Title:
“Data element ×” empowers manufacturing industry:theoretical logic and implementation path
文章编号:
1671-6248(2024)02-0054-17
作者:
李勇坚1; 2
(1. 中国社会科学院 财经战略研究院,北京 100006; 2. 中国社会科学院大学 应用经济学院,北京 102488)
Author(s):
LI Yongjian1; 2
(1. Institute of Financial Strategy, Chinese Academy of Social Sciences, Beijing 100006, China; 2. School of Applied Economics, Chinese Academy of Social Sciences, Beijing 102488, China)
关键词:
“数据要素×; ”;  数据要素乘数效应;  制造业;  数据化;  数据生态;  工业互联网;  数据文化;  数据安全
Keywords:
“data element ×; ”;  the multiplier effect of data elements;  manufacturing;  digitization;  data ecology;  industrial Internet;  data culture;  data security
分类号:
F49; F424
DOI:
-
文献标志码:
A
摘要:
数据要素在工业制造领域具有很大的应用空间,《“数据要素×”三年行动计划(2024—2026年)》将工业制造作为发挥数据要素乘数效应的重点领域。从理论上看,数据要素在制造业领域的乘数效应发挥,其理论基础是数据化理论、工业互联网理论、数据生态理论、数据驱动决策理论、商业模式创新理论。数据要素赋能制造业,可以从制造业的研发、生产制造与服务能力等流程全面应用数据要素实现,也可以从数据要素为制造业提供模拟、仿真、优化、控制、预测等维度来实现。在政策上,要强化数据文化,推动数据互通、共享、复用; 在制造业数据相关的底层技术方面加大研发投入,开发出适用制造业数据开发的通用工具; 推动算力、算法、存储等相关配套设施与数据要素协同,支持数据要素相关的数据经纪人、数商、交易服务机构等协同发展,建立健全良好的数据生态; 推动建设国家制造业数据中心,完善国家、地方、行业、团体的分级体系,对公共数据、企业数据、个人数据形成区别化的分级分类制度; 强化制造业数据的安全保护标准,引导、推动行业协会等社会组织加强数据安全自律,完善数据安全体系建设。
Abstract:
The application potential of data elements in industrial manufacturing is vast. The “Data Element ×” Three-Year Action Plan(2024—2026)identifies industrial manufacturing as a key area to leverage the multiplier effect of data elements. Drawing from the digitization theory, industrial Internet theory, data ecology theory, data-driven decision-making theory, and business model innovation theory, it elucidates how the multiplier effect of data elements can be played out in manufacturing. Empowering the manufacturing industry entails a comprehensive integration of data elements across various stages, including R&D, manufacturing, and service capabilities, as well as leveraging simulation, optimization, control, prediction, and other dimensions. Policy-wise, fostering a robust data culture and facilitating interoperability, sharing, and reuse is imperative. Additionally, there is a need to augment R&D investments in foundational technologies pertinent to manufacturing data and to develop versatile tools conducive to data development in manufacturing. Enhancing computing power, algorithms, storage, and allied infrastructure to synergize with data elements is essential, as is nurturing a sound ecosystem involving data brokers, merchants, and transaction service entities. Establishing a robust national manufacturing data center and refining classification systems for public, enterprise, and personal data at national, local, industrial, and organizational levels are vital steps. Strengthening security standards for manufacturing data, guiding, and encouraging self-discipline among social organizations such as industry associations to enhance data security are also crucial for fortifying data security systems.

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备注/Memo

备注/Memo:
收稿日期:2024-01-10
基金项目:中国社会科学院创新工程项目(2024CJY0103)
作者简介:李勇坚(1975-),男,研究员,教授,博士研究生导师,经济学博士。
更新日期/Last Update: 2024-04-20