NRLA & MOD-Metric Models as Aids in Design Decisions

This paper reports partial results and extensions of a study at Clemson University from 1980 December to 1982 May. The purpose was to develop an integrated model set to aid in evaluating design alternatives on the availability and logistic support requirements of major weapon systems. The model set includes the Network Repair Level Analysis (NRLA) and MOD-Metric models. System design decisions concern the nominal reliability of components, the extent to which components are hierarchially designed, the accessibility of components, and whether the component will be repaired or discarded when it fails. System design includes the design of the logistic support system and its relation to the hardware. Design alternatives should be carefully investigated to determine the effects that each would have on ownership costs, performance, and availability. This model set was applied to the components of the air refueling boom of the KC-135A tanker aircraft. The 2-echelon, 2-indenture inventory system used at times by the US Air Force is assumed. In particular we report: 1) the effects of component reliability on spare stock levels and spare parts costs, and 2) the impact of component reliability on the level at which repair takes place. The models have similar data requirements and they yield useful conclusions. However, there are some difficulties. The spares decisions from MOD-Metric are not necessarily compatible with NRLA because the MOD-Metric analysis is far more sophisticated.     

Modified U Charts for Monitoring and Controlling Poisson Attribute Processes

Control charts in general and U charts in particular are commonly used in most industries. This article presents a method of modifying the U chart when the usual assumption of Poisson rate data is not valid. The method consists of partitioning the data into Poisson and non-Poisson sources and using this partitioning to construct a modified U chart. Examples of the common U chart for Poisson data and the common U chart for data that are not purely Poisson are presented. In addition, the conventional individuals chart method of dealing with the violation of the Poisson assumption is discussed. Finally, the partitioning method is presented and an example given.