Two critical decisions must be made daily when managing multiechelon repair and distribution systems for service parts: (1) allocating available repair capacity among different items and (2) allocating available inventories to field stocking locations to support service operations. In many such systems, procurement lead times for service parts are lengthy and variable, repair capacity is limited, and operational requirements change frequently-resulting in demand processes that are highly uncertain and nonstationary. As a consequence, it is common to have many items in short supply while others are abundant. In such environments, integrated real-time decision-support tools can provide significant value by reducing the impact of inventory imbalances and responding appropriately to the volatile nature of the demand processes. By "integrated" and "real-time," we mean (respectively) tools that simultaneously consider key aspects of the current state of the operating environment in deciding what items to repair, where to ship available units, and by what mode to ship them. In this paper, we develop an integrated real-time model for making repair and inventory allocation decisions in a two-echelon reparable service parts system. We formulate the decision problem as a finite-horizon, periodic-review mathematical program, we show it can be formulated as a large-scale linear program, and we develop a practical heuristic method for solving the problem approximately. By simulating the operation of a service parts supply chain, we demonstrate the value of employing integrated decision models over using separate repair and inventory allocation rules for a range of environments where inventory imbalances exist. We also show that our heuristic approach is highly effective and that its inventory allocation subroutine, used as a stand-alone tool for making distribution decisions, outperforms a commonly used inventory allocation rule in most circumstances tested.
