Integrated production and quality dynamics under organizational learning: A hybrid continuous–discrete framework

This paper presents a hybrid continuous–discrete framework for optimizing production, pricing, quality, and learning investments under organizational learning, defined as efficiency gains through accumulated experience. Extending prior work on price-quality dynamics and production optimization, the model integrates real-time operational decisions with periodic strategic planning to address dynamic market challenges. It incorporates a stochastic initial learning rate to capture uncertainty in learning processes. In contrast to conventional discrete models that may exhibit qualitatively different dynamics (Vörös 2021), the hybrid framework is constructed to converge rigorously to the continuous-time optimum, ensuring consistent decision-making across planning horizons. Monte Carlo simulations provide insights into production increases, gradual price reductions, and quality improvements, with profit gains up to 14.7% in scenarios with high learning and quality sensitivity. The framework offers actionable guidance for industries like automotive, electronics, aerospace, and pharmaceuticals, enhancing cost efficiency and competitiveness through integrated strategic and operational optimization, though without explicit competitive interactions.