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The purpose of this paper is to provide an approach for determining inventory levels that result in a minimum cost customer service level for specific products based on their demand characteristics and profit margin.

The paper uses logistic regression to quantify the relationship between customer service level and inventory on‐hand in relation to forecasted demand, as well to estimate the impact of factors such as forecast accuracy, customer lead‐times, and demand variability on this relationship. It then performs financial analysis in order to associate a cost with customer service level.

Empirical results based on data from a semiconductor manufacturer indicate significant cost‐savings can be achieved by applying the proposed method over the organization's current ad hoc practices.

The minimum cost customer service level identified via the methodology is based on values of dynamic factors that are specific to the time when data were collected. Therefore, frequent updating is necessary to ensure the customer service level remains close to the minimum cost. Future research could identify the ideal frequency for updating inventory levels based on cost minimization and production stability.

This research presents an inventory management methodology for organizations with variable, non‐stationary demand. In contrast to much of the current inventory modeling literature, in which service level goals are selected in an ad hoc or a priori manner, this research determines an ideal (minimum cost) customer service level from the supplier's perspective based on products' unique characteristics.