A Cost-Based System Model for Data Asset Valuation: A Case Study of a Chinese Supply Chain Company

In the digital economy, data have become a new factor of production, making a rigorous and feasible data asset valuation system a practical necessity. As the supply chain sector advances toward digitalization and intelligent transformation, data assets have become a key source of competitive advantage for supply chain companies. Based on the modified cost approach, this study develops a valuation system comprising three subsystems: cost reconstruction, return enhancement, and value adjustment. First, data asset input costs are aggregated and converted into replacement costs. The cost–expense profit ratio is then introduced to estimate a theoretical benchmark value incorporating operating returns. Four groups of influencing factors—data quality and scale, the data asset market, data asset application, and value realization risk—are incorporated. The analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) are combined to derive a value adjustment coefficient. Finally, the coefficient is applied to the theoretical benchmark value to obtain the data asset value. The system is applied to Company G, a supply chain company in China, as a case study. The results show that the replacement cost is RMB 1,804,126, the value adjustment coefficient is 0.8371, and the final estimated value is RMB 1,529,111.80. Sensitivity analysis reveals that direct and indirect costs exhibit the highest sensitivity coefficients of 0.566 and 0.434, respectively, followed by data quality and scale and data asset application. This study contributes to the methodological development of differentiated data asset valuation from a value formation perspective and provides practical insights for identifying data asset management priorities in supply chain companies.