A Stochastic Stock-Linked Pricing Framework for Production–Inventory Optimization: Evidence of Profit Gains over Traditional Models

Volatile market conditions and the increasing integration of financial dynamics into supply chains underscore the need for inventory models capable of adapting pricing strategies in real time. This study presents a novel dual-scenario production–inventory framework that, for the first time, directly incorporates stock-based pricing—modeled through geometric Brownian motion (GBM)—into the inventory decision process, enabling a rigorous comparison with the traditional currency-based pricing paradigm. The model traces inventory evolution across production and consumption phases, jointly optimizing the initial stock price and production-to-consumption transition time to maximize total profit. Computational analysis reveals that the stock-based configuration achieves a +8.2% higher maximum profit and a 20.0% shorter optimal transition time relative to the monetary benchmark, yielding both economic and operational advantages. Sensitivity analysis on drift rate, volatility, and demand elasticity confirms the robustness of the stochastic framework, while convex profit surfaces verify the global optimality of the obtained solutions. By embedding market drift and volatility directly within the pricing mechanism, the proposed model establishes a resilient, market-aligned decision-support framework for industries facing financial uncertainty and rapid demand fluctuations.