Data-Driven Risk Modeling and Profit Optimization in Omni-Channel E-Commerce Supply Chains Under Demand Uncertainty and Information Asymmetry
DOI:
https://doi.org/10.63646/EHVQ3795Keywords:
Omni-channel supply chain; Risk management; Data-driven analytics; Information asymmetry; Demand uncertainty; B2B and B2C dual channel; Game theory; Profit optimizationAbstract
The rapid growth of e-commerce and the proliferation of digital channels have fundamentally transformed supply chain structures, requiring firms to simultaneously manage demand uncertainty, supply disruptions, information asymmetry, and cybersecurity threats. This study develops a comprehensive data-driven risk modeling framework for an omni-channel e-commerce supply chain serving both business-to-consumer (B2C) and business-to-business (B2B) market segments under price-dependent stochastic demand. Five analytical models are constructed that progressively incorporate demand risk, supply-side uncertainty, information asymmetry between the e-commerce platform and its logistics partner, and cybersecurity risk with corresponding mitigation investment strategies. Each model is solved using classical optimization techniques, and global optimality is validated analytically. Numerical experiments using realistic e-commerce parameter values quantify the financial impact of each risk dimension. Results indicate that unmitigated risks and information asymmetry impose a cumulative profit loss of 7.8%, of which 6.2% is recoverable through structured mitigation strategies. Sensitivity analysis reveals that demand risk is the most damaging single factor, while cybersecurity investment yields the highest marginal return among mitigation options. These findings offer both theoretical contributions to supply chain risk analytics and practical guidance for e-commerce platform managers.
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