Business Analytics for Delivery-Promise Optimization in Carbon-Constrained Online Retail Supply Chains

Abstract

Online retailers increasingly compete on the speed and reliability of their delivery promises while simultaneously facing pressure to decarbonize their operations. This dual demand creates a complex decision problem because consumer purchasing behavior is jointly shaped by retail price, promised delivery time, environmental impact, and the option to delay purchases in anticipation of price reductions. This study develops a unified business-analytics framework for delivery-promise optimization in a two-echelon carbon-constrained online retail supply chain composed of a manufacturer, an online retailer, and strategic consumers. Five progressive analytical scenarios are constructed: a benchmark with myopic consumers, a fixed delivery-time policy with strategic consumers, a dynamic delivery-time policy under low patience, the same policy under high patience, and a cost-sharing contract that aligns supply-chain incentives. Closed-form Stackelberg equilibria are derived under each scenario, validated through second-order conditions and numerical grid search, and translated into actionable analytics dashboards. Numerical experiments calibrated to typical e-commerce parameters show that a dynamic delivery-time policy lifts manufacturer profit by 8.1% and total system profit by 7.7% relative to the fixed-time benchmark, while simultaneously raising the carbon-reduction effort by 8.8%. The proposed cost-sharing contract enlarges the parameter region in which both supply-chain members benefit by 47%. The findings provide quantitative guidance for analytics-enabled delivery-promise management in sustainable e-commerce.

Keywords: Delivery-promise optimization; business analytics; carbon-constrained supply chain; strategic consumers; dynamic pricing; cost-sharing contract; Stackelberg equilibrium.