制造企业数据交易与数据外包的策略组合优化研究

Optimization Research on Manufacturing Enterprise Data Transaction and Data Outsourcing Strategy Combination

摘要: 制造企业目前普遍面临内部数据处理能力薄弱与外部市场需求不确定的双重约束，数据要素作为制造业数字化转型的核心驱动力，其价值实现取决于企业能否有效应用数据。本文通过构建Stackelberg博弈模型，分析了传统策略(NN)、数据交易策略(TN)、数据外包策略(ND)及组合策略(TD)下的企业定价与利润均衡。研究发现：(1) 数据交易策略的价值受交易成本与内部数据转化能力的双重约束，适度引入外部数据可优化需求预测，但过度采购将导致边际收益递减；(2) 数据外包策略的有效性依赖于外包商的数据处理能力，仅当其能力突破阈值时，专业化效率提升才能覆盖外包成本；(3) 数据交易与外包的组合策略在内部能力适中、时效性损失较低且外部成本可控时，可通过“数据广度获取–专业化深度处理”的协同路径实现企业利润最大化。本研究为制造企业破解“能力–需求”双维约束提供了“策略–能力–成本”三位一体的决策框架，对深化数据要素市场化配置理论与实践具有重要参考价值。

Abstract: Manufacturing enterprises currently face prevalent dual constraints: weak internal data processing capabilities and uncertain external market demands. As the core driving force behind the digital transformation of the manufacturing industry, the value realization of data elements depends on enterprises’ effective application of data. By constructing a Stackelberg game model, this study analyzes the pricing and profit equilibria of enterprises under four strategies: the traditional strategy (NN), data transaction strategy (TN), data outsourcing strategy (ND), and combined strategy (TD). The findings are as follows: (1) The value of the data transaction strategy is dually constrained by transaction costs and internal data conversion capabilities. Moderate introduction of external data can optimize demand forecasting, but excessive procurement leads to diminishing marginal returns; (2) The effectiveness of the data outsourcing strategy relies on the data processing capabilities of outsourcing providers. Only when their capabilities exceed a threshold can the improvement in professional efficiency offset the outsourcing costs; (3) When internal capabilities are moderate, timeliness losses are low, and external costs are controllable, the combined strategy of data transaction and outsourcing can achieve enterprise profit maximization through the synergistic path of “acquiring data breadth-conducting professional in-depth processing”. This study provides a “strategy-capability-cost” trinity decision-making framework for manufacturing enterprises to address the dual-dimensional constraints of “capability-demand”, and offers important reference value for deepening the theory and practice of market-oriented allocation of data elements.