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论文编号：	15790
作者编号：	2320224207
上传时间：	2025/12/10 9:31:22
中文题目：	大数据背景下固定资产管理优化研究 －以M共享单车为例
英文题目：	Optimizing Fixed Asset Management in the Big Data Era: A Case Study of M Bike-Sharing Company
指导老师：	王志红
中文关键字：	固定资产管理；大数据；共享出行；固定资产维护
英文关键字：	Fixed Asset Management; Big Data; Shared Mobility; Fixed Asset Maintenance.
中文摘要：	随着我国网络经济和技术的发展，涌现出了多家大型互联网公司与新兴网络经济模式，其业务依靠网络生态优势覆盖多个领域，并呈现复杂化、规模化、智能化的行业特点，随之而来的经营及财务数据也迎来了大爆发的时代。其中在共享经济等重资产领域，传统的固定资产管理体系已无法适应互联网公司共享出行业务海量资产下的管理需求，随着行业规模持续扩张和市场竞争日益激烈，如何实现固定资产的更高效配置和运营已成为企业发展的关键。各大头部企业均希望利用自身的技术优势，通过信息技术手段，提升共享经济固定资产管理水平，提高固定资产使用效率，进一步降本增效，适应行业即时性、高速度、大数据、精细化的业务管理特点。 本文以M公司共享出行业务固定资产为研究对象，首先对固定资产管理理论、大数据管理等理论进行梳理，分析国内外研究现状，学习相关进步观点，明确关键概念及研究思路，为后续研究提供理论支撑。其次通过实地调研资产状况、访谈关键管理岗位、收集分析公司行业数据资料，对该公司共享出行固定资产的管理现状进行描述，明确该公司的业务模式、技术状况、资产特点、业务管理诉求、组织管理模式等，深入分析当前固定资产管理体系在固定资产采购确认、固定资产运营维护、资产财务核算方面的痛点，并归纳导致这些问题的原因。最后根据上述原因从大数据与固定资产管理相结合的角度，利用企业可行的大数据采集融合、大数据挖掘等手段，提出基于大数据的固定资产管理措施，实现全过程的固定资产多源成本分摊、预防性维护、动态调度优化、处置流程优化、财务折旧模型重构等优化建议及配套保障措施。 通过对 M 公司共享出行业务的固定资产管理优化案例研究，提出基于大数据的智能化固定资产管理措施，提高固定资产信息化、自动化水平，有助于企业提升资产使用效率，挖掘资产数据价值，以满足网络经济背景下的固定资产管理的新需求。
英文摘要：	With the development of China's internet economy and technology, numerous large internet companies and emerging internet-based economic models have emerged. Their businesses leverage network ecosystem advantages to span multiple domains, exhibiting industry characteristics of complexity, scale, and intelligence. Consequently, operational and financial data have entered an era of explosive growth. In capital-intensive sectors like the sharing economy, traditional fixed asset management systems have become inadequate for addressing the massive asset management demands of internet companies' shared mobility services. As industry scale continues to expand and market competition intensifies, achieving more efficient allocation and operation of fixed assets has become crucial for corporate development. Leading enterprises aspire to leverage their technological advantages and IT solutions to enhance fixed asset management in the sharing economy, improve asset utilization efficiency, reduce costs, and adapt to the industry's real-time, high-speed, big data-driven, and refined operational management requirements. This paper focuses on the fixed assets of M Company's shared mobility business. First, it examines theories of fixed asset management and big data management, analyzes domestic and international research status, synthesizes relevant progressive perspectives, and clarifies key concepts and research frameworks to establish theoretical foundations. Subsequently, through field investigations of asset status, interviews with key management personnel, and analysis of corporate/industry data, it describes the current state of fixed asset management in M Company's shared mobility operations. This includes clarifying the company's business model, technological status, asset characteristics, operational requirements, and organizational management structure. The study conducts an in-depth analysis of pain points in the existing fixed asset management system regarding procurement verification, operational maintenance, and financial accounting, while identifying root causes. Finally, building on these findings and integrating big data with asset management perspectives, it proposes optimized big data-driven fixed asset management measures. These include multi-source cost allocation, preventive maintenance, dynamic scheduling optimization, disposal process improvements, and financial depreciation model reconstruction, supported by feasible big data collection/analysis techniques and implementation safeguards. Through this case study of fixed asset management optimization in M Company's shared mobility business, the paper proposes intelligent big data-enabled fixed asset management solutions. These measures enhance digitalization and automation in asset management, helping enterprises improve asset utilization efficiency and unlock data value to meet new fixed asset management requirements in the internet economy era.
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