Introduction

We introduce the fundamental notion of the system approach and contrast it with the older, traditional method of calculating spares known as the item approach. We show that for high technology equipments, repairable items are more important than consumable or non-repairable items.1 This makes for some simplification, because there is only one decision variable on each item: when to order or, equivalently, the stock level. On the other hand, the problem is more complicated, because the support of complex systems requires us to be concerned about many items and stock levels at both bases and depots. This is known as a multi-echelon context. Furthermore, we want to optimize the mix of items and the sub-items of which they are composed, known as the multi-indenture problem.

The terms “failure” and “demand” are used interchangeably. We assume that when there is a demand a spare is needed. If no spare is on hand, some system has a “hole” and the “end item” is unavailable until a spare can be supplied. Instead of using the term end item we will use aircraft as a typical example, and a military context where these models first arose. But, it is important to realize that the models we develop in this text have many commercial applications including commercial airlines, power plants, radar installations, space station. In fact, the theory is applicable to any complex system where it is meaningful to talk about availability (the percent of time that the system is operational). The system of interest may not be the aircraft, say, but a sub-system of the aircraft such as the guidance, the propulsion, or the avionics. We use the term “item” to designate a specific type of part and “units” for the quantity of the item. We will show that the stock level on any item at any location can be thought of as the average number of units of the item in repair or resupply plus some safety level to protect against variability in the demand and repair processes. But the optimal stock level depends on a number of other variables also including item cost, location (base or depot), and indenture (item or sub-item) as well as system variables such as the desired availability. In later chapters we develop the theory necessary to include all of these factors.

We summarize field test experience using a variable protection level that demonstrated as much as fifty percent reduction in inventory cost to obtain the same performance level, even at a single base. Last, the chapter shows with a simple example what optimal item policies look like. We show that the optimal stock levels are different when there is cannibalization - consolidation of aircraft “holes” or backorders to the smallest number of aircraft possible by remove-and-replace maintenance