智能运营管理实验室（IOM）以计量分析和设计科学为方法论核心，构建了涵盖“数据—算法—协同—平台—商业模式”全链条的供应链管理研究体系。该体系重点探索数智供应链中的数据要素流通管理、智能算法优化、人机协同机制、平台生态治理及商业模式创新等前沿科学问题。实验室通过融合计量因果推断、设计科学和实验研究等多种方法，致力于搭建管理理论创新与管理实践应用之间的桥梁，推动管理学学术前沿的突破，创造社会经济价值，旨在成为具有国际影响力的智能运营管理研究中心。

The Intelligent Operations and Management (IOM) Laboratory is grounded in economics and design science as its core research paradigms, systematically exploring the integrated architecture of data, algorithms, collaboration mechanisms, digital platforms, and business models within the field of intelligent supply chain management. The laboratory is dedicated to addressing critical and emerging research questions related to the valuation and utilization of data elements, the optimization of intelligent algorithms, the design of human–machine collaboration frameworks, governance of platform-based ecosystems, and the innovation of digitally enabled business models in the context of supply chain management. Drawing on a multi-methodological approach, including quantitative causal inference, experimental design, and design science research, the IOM seeks to bridge theoretical advancements with empirical applications. Its research agenda is oriented toward generating original scholarly contributions while promoting socio-economic impact through translational research. The laboratory is committed to establishing itself as a leading academic center with international influence in the domains of the digital economy and intelligent operations management.

以数据要素为研究核心，深入探讨数智供应链中的数据确权、定价、流通、交易、隐私保护及治理机制。系统研究供应链中数据要素的流通路径与市场制度创新，旨在推动数据在数字经济中的价值充分释放。

Focusing on data as a key factor of production, this research direction investigates the mechanisms underpinning data ownership, valuation, circulation, transaction, privacy protection, and governance in digital intelligent supply chains. It emphasizes exploring the data element circulation pathways and market system innovations in supply chain ecosystems, with the ultimate goal of enabling effective value realization of data assets in the context of the digital economy.

领域：供应链数据流通机制设计、供应链数据资产定价、供应链数据安全与隐私保护、供应链动态数据交易与治理。

Topics: Supply chain data flow mechanism design, supply chain data asset pricing, supply chain data security and privacy protection, supply chain dynamic data trading and governance

基于计算设计科学，开发并优化供应链数智化运营算法模型，解决供应链中涉及的采购、生产、销售，物流与库存等复杂运营问题。探索强化学习、图神经网络、预测模型等方法在供应链智能运营中的应用，推动供应链运营效率与可持续发展。

Rooted in computational design science, we develop and optimize digital intelligence operation algorithm models for supply chains to tackle complex operational challenges in procurement, production, sales, logistics, and inventory management. By delving into the application of reinforcement learning, graph neural networks, predictive modeling, and other methodologies in intelligent supply chain operations, we drive efficiency improvements and sustainable development in supply chain management.

领域：算法设计与优化、数智化供应链、预测建模与强化学习

Topics : Algorithm design and optimization, intelligent supply chain operations, predictive modeling, and reinforcement learning.

探索供应链数智运营中人类与人工智能（尤其是生成式AI和多Agent系统）的交互及协作机制，聚焦于供应链场景下的人机信任、责任分配、情感交互与组织绩效等行为机理和作用机制。

Explore the interaction and collaboration mechanism between human and artificial intelligence (especially generative AI and multi-Agent system) in the intelligent operation of supply chain, focusing on the behavioral mechanisms and action mechanisms such as human-machine trust, responsibility allocation, emotional interaction and organizational performance in the supply chain scenario.

领域：人机信任与算法管理、AI Agent 行为模式、多Agent 协作机制、AI等新兴技术对组织与用户行为的影响

Topics: Human-AI trust and algorithm management, AI agent behavioral patterns, multi-agent collaboration mechanisms, and the impact of emerging technologies such as AI on organizational and human behavior.

聚焦数字平台生态中的算法作用与治理机制，研究推荐系统、公平性、透明性、多平台 multihoming 策略与生态竞争，探索如何实现平台的可持续发展与负责任治理。

Focusing on the role of algorithms and their governance within digital platform ecosystems. It investigates core issues such as algorithmic recommendation systems, fairness and transparency, cross-platform multihoming strategies, and competitive dynamics within and across ecosystems. The overarching aim is to advance understanding of how algorithmic design and platform governance shape user behavior, market structure, and ecosystem sustainability, thereby contributing to the responsible and long-term development of digital platforms.

领域：平台推荐与个性化算法、算法歧视与公平性治理、跨平台行为、平台生态竞争与治理、全渠道管理。

Topics: Platform recommendation and personalization, algorithmic fairness and discrimination governance, cross-platform multihoming behaviors, platform ecosystem competition and governance, and omnichannel management.

研究数字技术（AI、大数据、平台、数据要素）如何推动企业转型与新型创业。重点关注服务化转型、C2M模式、平台开源创新、AI赋能商业模式与数字化创业路径。

Focusing on exploring how digital technologies, such as artificial intelligence, big data, digital platforms, and data as a production factor, enable enterprise transformation and foster new forms of entrepreneurship. It emphasizes service-oriented digital transformation, customer-to-manufacturer (C2M) production models, platform-enabled open innovation, AI-driven business model innovation, and the emergence of novel digital entrepreneurship pathways. The goal is to understand the mechanisms through which digitalization reshapes organizational structures, value creation logics, and entrepreneurial ecosystems in the digital economy.

领域：C2M 商业模式、平台开源创新、AI赋能商业模式创新、母子公司关系对创新的影响、数字创业中的隐私与治理。

Topics: Customer-to-manufacturer (C2M) business models, platform-based open innovation, AI-enabled business model innovation, parent-subsidiary dynamics in innovation, and privacy and governance in digital entrepreneurship.