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It has been suggested that much of the potential inefficiencies associated with supply chain management (SCM) costs can be traced to wasteful practices such as inefficient, unnecessary, or redundant stocking practices, or inefficient transportation. The purpose of this paper is to develop a model which reconciles many of these inefficiencies by integrating production factors, purchasing, inventory, and trucking decisions for optimizing supply chain costs between first‐, and second‐tier suppliers and subsequent OEM customers.

The modeling technique is mathematical programming tested in a simulation model. In an effort to determine the significance of the transportation component of the proffered model, the fully developed model is differentially tested, including standard production variables varying transportation costs, paired with similar instances of the model in which the transportation costs are fixed.

Significant differences are found in the predictive abilities of the respective models, and this supplies pragmatic evidence of the important role that transportation issues play in the consideration of integrated SCM costs.

The key limitation to this finding lies in the validation process. As suggested by Sargent, Monte‐Carlo studies are useful for validation purposes, and the supply chain optimization model (MHSCM) is certainly confirmed through this particular simulation.

The managerial focus on transportation management and cost control in SCM can be highlighted as a critical implication of the study.

The structure of the MHSCM is robust, and may be useful for cost‐control planning purposes in a dynamic environment, subject to certain limitations accruing to the methodology.