Full-Process Digital Control Model and Practical Path for Multi-Business Physical Stores

Abstract

Although multi-business chain operation can expand customer groups and disperse operational risks, it is restricted by business heterogeneity, fragmented operations and inconsistent data standards. Traditional control and single-business digital solutions cannot adapt to large-scale operations, and existing research lacks a universal full-life-cycle closed-loop model. Based on the theories of full-life-cycle management, closed-loop data governance and system dynamics, this paper selects panel data of 127 mixed-business stores from 2021 to 2025 (Huber M & Fisher L., 2022), and conducts empirical research by adopting the entropy weight TOPSIS method, system dynamics simulation and mediating effect model. The study finds that data governance, intelligent decision-making, process closed-loop management and dynamic iteration can significantly improve store control efficiency; business adaptation exerts partial mediating effect, and the sensitivity of control parameters varies across different business formats. On this basis, this paper constructs a layered and adaptive implementation path, and verifies the feasibility of the model combined with enterprise cases. The research can fill the theoretical gap of digital control for multi-business stores, and provide theoretical support and replicable practical schemes for differentiated collaborative governance of chain enterprises.