Driven by mobile Internet and big data technology, the omni channel retail model has developed rapidly. Its core feature is that product portfolio decisions directly affect profitability. Faced with challenges such as diversified customer demands, high uncertainty in the supply chain system (including daily operational risks and extreme event risks), inconsistent customer behavior time (such as perceived product value changing with the lifecycle), and risk avoidance characteristics of decision-makers, the traditional assumption of "rational economic man" is no longer applicable. This study focuses on the decision-making problem of omni channel supply chain operations. By constructing distributed robust optimization models, stochastic programming models, and dual objective robust optimization models, combined with duality theory, linearization techniques, Monte Carlo simulations, and other methods to deal with uncertainty, we specifically use mean CVaR to measure risk, WMCVaR to characterize cost fluctuations, quasi hyperbolic discount functions, and latent category Logit models to characterize demand time inconsistency and customer heterogeneity selection processes. The results show that the distributed robust optimization model performs better in risk avoidance and profit risk trade-off; The hybrid order fulfillment strategy (such as combining SFS and BOPS) is superior to a single strategy, as it can provide diversified products and is not affected by changes in unit fulfillment costs; The parameters such as demand ratio, inventory capacity, and fulfillment cost significantly affect the decision results (such as saving more costs when the online demand ratio is high). The model demonstrates effectiveness and robustness in small and medium-sized cases, and can cope with daily operational risks and extreme event risks. By integrating behavioral operation management and uncertainty planning theory, a decision-making framework for omni channel supply chain considering decision-makers' risk avoidance and customer time preferences was constructed. Technical methods for handling uncertainty were proposed, providing practical guidance for omni channel retail enterprises to formulate optimal operational strategies. In the future, behavioral factors such as disappointment avoidance and bounded rationality can be further expanded, uncertainties in transportation costs and delivery times can be included, multi-party competition coordination mechanisms can be studied, and the application value of the model can be verified by combining actual enterprise data.

Xinyan Qian. Supply Chain Collaboration Mechanisms Driven by Demand Orchestration in Omni-Channel Retail Environments. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 136-146. https://doi.org/10.38007/IJBMET.2026.070116.

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