An intelligent inventory decision method for seasonal clothing e-commerce

CN122242949APending Publication Date: 2026-06-19GUANGDONG VOCATIONAL COLLEGE OF SCI & TRADE

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: GUANGDONG VOCATIONAL COLLEGE OF SCI & TRADE
Filing Date: 2026-03-18
Publication Date: 2026-06-19

AI Technical Summary

Technical Problem

The inventory preparation decision-making methods of seasonal apparel e-commerce have the problem that the error in demand forecasting cannot be effectively transmitted and corrected. This results in best-selling SKUs missing the replenishment window, while slow-moving SKUs are passively piled up in inventory, resulting in serious loss of gross profit.

Method used

We construct an integrated decision-making framework that combines demand forecasting, profit optimization, and dynamic correction. By using the objective function of maximizing gross profit throughout the season and combining it with real-time sales data to dynamically adjust order quantities, we introduce rolling time-domain optimization strategies and cross-regional allocation optimization to achieve real-time closed-loop response to forecasting errors.

Benefits of technology

It achieves optimal global profit across multiple regions and SKUs, enhances supply chain agility and profitability resilience, and reduces inventory backlog and stockout losses.

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Abstract

This invention discloses an intelligent inventory preparation decision-making method for seasonal apparel e-commerce, belonging to the field of e-commerce technology. It solves the problem in existing technologies of effectively transmitting and correcting demand forecasting errors to subsequent decision-making stages. This invention uses maximizing the gross profit for the entire season as a unified objective function, incorporating sales revenue, procurement costs, time-varying warehousing costs, and residual value of end-of-season inventory into the same optimization dimension, ensuring that the interests of each department are inherently aligned within the mathematical model. Furthermore, the key innovation of this invention lies in introducing a rolling time-domain optimization strategy, allowing the system to continuously collect real-time sales data throughout the sales cycle, dynamically update demand forecasts, and recalculate the optimal order quantity for subsequent periods, instantly transforming previous forecast deviations into executable strategy correction instructions. This flexible decision-making capability of observing, learning, and optimizing simultaneously enables e-commerce companies to automatically balance stockout losses and inventory holding costs in environments with fluctuating demand.

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