This study introduces a mathematical framework for modeling Expected Total Quality Loss within a stochastic manufacturing process control environment. It addresses a long-standing gap in the literature where the relationship between conformance rate and quality-related loss, as depicted by the well-known Lundvall-Juran curves, has been primarily conceptual. By integrating Taguchi's Quality Loss Function with a probabilistic model of stochastic process mean shifts, the research develops a nonlinear decreasing function expressing expected loss in terms of conformance rate. This function, which assumes a single quality characteristic, accounts for the probabilistic nature of process drifts and their economic impact. Its versatility is demonstrated by analyzing three representative cases where normal, mixed, and discrete distributions are used to model mean shifts to capture a range of realistic manufacturing scenarios. The effect of inspection is also investigated. While resembling classical conceptual models, the derived function departs from them as the conformance rate approaches 100%, becoming progressively steeper, and thereby indicating an accelerating rate of loss reduction. This analytical framework offers a powerful tool for managers to move beyond heuristic guidelines, enabling a quantitative evaluation of quality improvement initiatives and their return on investment.