International Journal of Reliability, Quality and Safety Engineering

The supplier selection problem consists of selecting the best of several possible suppliers for a product. The selection criteria, in the absence of considerations such as cost, are based on a quality metric such as the capability of the supplier's manufacturing process. Because quality metrics are estimated based on sample process data, the inherent variability in the estimates must be accounted for when selecting the best supplier. In this paper we consider a methodology based on the bootstrap that assigns confidence levels to each of the suppliers. These confidence levels are designed to reflect the amount of confidence we have that the supplier actually has the best quality metric, conditional on the observed samples. The methodology is demonstrated using two examples.

This paper provides guidance for the planners of a test of any system that operates in sequential stages: only if the first stage functions properly (e.g., a vehicle's starter motor rotates adequately) can the second stage be activated (ignition system performs) and hence tested, followed by a third stage (engine starts and propels vehicle), with further stages such as wheels, and steering, and finally brakes eventually brought to test. Each sequential stage may fail to operate because its design, manufacture, or usage has faults or defects that may give rise to failure. Testing of all stages in the entire system in appropriate environments allows failures at the various stages to reveal defects, which are targets for removal. Early stages' fault activations thus postpone exposure of later stages to test. It is clear that only by allowing the entire system to be tested end-to-end, through all stages, and to observe several total system successes can one be assured that the integrated system is relatively free of defects and is likely to perform well if fielded.

The methodology of the paper permits a test planner to hypothesize the numbers of (design) faults present in each stage, and the stagewise probability of a fault activation, leading to a system failure at that stage, given survival to that stage. If the test item fails at some stage, then rectification ("fix") of the design occurs, and the fault is (likely) removed. Failure at that stage is hence less likely on future tests, allowing later stages to be activated, tested, and fixed. So reliability grows.

To allow many Test and Fix (TAF) cycles is obviously impractical. A stopping criterion proposed by E. A. Seglie that suggests test stopping as soon as an uninterrupted run/sequence of r (e.g., 5) consecutive system successes has been achieved is studied quantitatively here. It is shown how to calculate the probability of eventual field success if the design is frozen and the system fielded after such a sequential stopping criterion is achieved. The mean test length is also calculated. Many other calculations are possible, based on formulas presented.

We present a fast and exact novel algorithm to compute maximum likelihood estimates for the number of defects initially contained in a software, using the hypergeometric software reliability model. The algorithm is based on a rigorous and comprehensive mathematical analysis of the growth behavior of the likelihood function for the hypergeometric model. We also study a numerical example taken from the literature and compare the estimate obtained in the hypergeometric model with the estimates obtained in other reliability models. The hypergeometric estimate is highly accurate.

This paper deals with the problem of determining an optimal age replacement by incorporating minimal repair, planned replacement, and unplanned replacement into a k-out-of-n system subject to shocks. In particular, two kinds of solution procedures are discussed to obtain the optimal replacement age, one is a straightforward procedure, and the other is a graphical approach based on the Total Time on Test (TTT) concept. A statistical solution procedure for estimating the optimal replacement time is proposed, and the optimal policies are obtained directly from real data.

This paper presents a comprehensive survey of discrete probability distributions used in reliability for modeling discrete lifetimes of nonrepairable systems. The basic properties of each model are given. A classification into two families is proposed, highlighting the interest of using a Pólya urn scheme. The quality of the estimation of models parameters is numerically assessed. Some criteria are given in order to select among the presented distributions the most useful for applications.

This paper briefly discusses the subject of human error in health care systems and presents a comprehensive list of references on the said subject.