Estimating the production volume of durable consumer goods as an input to manufacturing process selection

Estimating production volume is a critical task in product development, as it directly affects key engineering decisions such as manufacturing process selection and product architecture. However, production volume is often treated as an exogenous assumption, while existing literature addresses demand estimation and engineering design in separate domains. This paper proposes an integrated framework for production volume estimation combining three complementary approaches: (i) a demand-driven top-down model based on market size, penetration, usage intensity, and replacement dynamics; (ii) a bottom-up engineering model grounded in relationships between price, material cost, and production scale; and (iii) an empirical proxy based on customer reviews from e-commerce platforms, used to infer sales through estimated review rates. The framework is applied to multiple product categories, including consumer goods, industrial components, and vehicles. Production volumes are estimated using Monte Carlo simulation to account for parameter uncertainty and to generate plausible ranges. Results are used to assess consistency across models, identify key drivers, and evaluate sensitivity. Findings show that production volume emerges from a small set of fundamental mechanisms and that different products can reach similar cumulative volumes through distinct combinations of these factors. The proposed approach enables structured volume estimation under uncertainty and supports the integration of market considerations into engineering design, particularly in early-stage development and for novel products.