A decision support model for determining the level of product variety with marketing and supply chain considerations

We develop a decision support model to determine the optimal product variety for a manufacturer by accounting for both the marketing and supply chain perspectives. While the marketing perspective tends to focus on variety's salience to consumers, the supply chain perspective tends to focus on inventory management and distribution factors such as order fulfillment rates, fill rates, and related costs. In general, the supply chain costs increase as product variety increases, but this overall trend may be arrested to some extent by advanced manufacturing techniques such as modularization. These techniques often generate an irregular cost function that poses a modeling challenge. We address this issue by developing a piecewise ILP (integer linear program) model, and demonstrate its utility by applying it in a systematic managerial simulation study. The simulation examines how the optimal level of product variety and the corresponding selection of products depend on the revenue and cost characteristics of products.