Quality and Reliability Management and Its Applications

Most companies today depend on outsourcing to build their products. Outsourcing strategy has shown its effectiveness in increasing organizational profits through the development of better products when outsourced from the right supplier. The decision to outsource is made by a company’s procurement or purchasing department. The decision involves many factors, and it gets more complex as the number of factors increases.

With the growing emphasis and concerns on quality of health care, performance monitoring of care providers has received much attention recently. Performance measures used as monitors are typically clinical outcomes, utilization of health services, and cost. By monitoring these measures continuously, changes in the performance of care providers can be detected promptly to avoid serious consequences as well as provide valuable information on the care delivery system for quality improvement.

In the context of statistical quality control, process capability index (PCI) is one of the widely accepted approaches for assessing the performance of a process with respect to the pre-assigned specification limits. The quality characteristic under consideration can have differnt types of specification limits like bilateral, unilateral, circular and so on. Use of single PCI for all the situations could be misleading. Hence appropriate PCIs need to be chosen based on the characteristics of the specification limits. Similar situations may arise for multivariate characteristics as well. In the present chapter, we have discussed about some of the PCIs for different specification limits including some PCIs for multivariate characteristics. A few numerical examples are given to suppliment our theoretical discussion.

In recent years, research implications of reliability, availability, and maintainability (RAM) aspects of reliability engineering systems have increased substantially due to rising operating and maintenance costs. For industrial systems, the cost is considered to be the most significant factor and RAM is an increasingly important issue for determining the performance of the system. On the other hand, the information available from the collected databases or records is most of the time imprecise, limited, and uncertain, and the management decisions are based on experience. Thus it is difficult for job analysts to analyze the performance of the system by utilizing these uncertain data. Therefore, the objective of this chapter is to quantify the uncertainties that make the decisions realistic, generic, and extensible for the application domain. For this, an optimization model has been constructed by taking composite measure of RAM parameters called RAM index and system cost as an objective function and solved with evolutionary techniques algorithm. The obtained failure rates and repair times of all constituent components are used for measuring the performance of the system in terms of various reliability parameters using intuitionistic fuzzy set theory and weakest t-norm based arithmetic operations. Performance analysis on system RAM index has also been analyzed to show the effect of taking wrong combinations of their reliability parameters on its performance. The suggested framework has been illustrated with the help of a case.

This chapter studies the system reliability as a performance indicator to measure the demand satisfaction of the manufacturing system. Three characteristics are considered in this chapter: (i) multiple production lines in parallel, (ii) multiple reworking actions, and (iii) distinct defect rate of each station.

Industrial applications often observe the difference between laboratory reliability test in standard conditions and component or system reliability when it is set in motion through different environments and real-world conditions. As a matter of fact reliability variable is considerably influenced by environmental factors. Environmental factors may change failure rate, reliability, and availability of systems.

This chapter describes the relevant steps for the design of a preventive maintenance program in an oil refinery plant and its application. The method was developed during a period of 3 years in one of the main Italian refinery.

This chapter gives four age replacement models for an operating unit when it works successively for jobs with random working cycles: (1) The unit is replaced at a total operating time T; (2) the unit is replaced at a total operating time T or at a random working cycle Y, whichever occurs first; (3) the unit is replaced at a total operating time T or at a random working cycle Y, whichever occurs last; and (4) the unit is replaced at the first completion of some working cycles over a planned time T. As a particular case of (2)–(4), random replacement, where the unit is replaced only at a random working cycle Y, is considered. Optimal policies for each model are discussed analytically, and Polices (2)–(4) are compared with (1) from the viewpoint of cost rate to find that Policy (1) is the best when preventive replacement costs are the same. However, random replacement and Policy (4) should be better than standard policy when a lower random replacement cost is supposed.

Availability is a very important measure of system performance. A great deal of research works on it have been done. This article reviews the recent major results in this field. Systems whose failures are either self-announcing or not self-announcing are considered in these works, various repair methods and different inspection policies are explored as well. Some of these studies derive the expressions of the steady-state availability, limiting average availability, and others give the expressions of the instantaneous availability explicitly or recursively.

The application of surveillance systems is a great enhancement of security level to the monitored area by providing important reference for the security teams to make prompt actions against threats or incidents.

Reliability is an aspect of a product that exists whether or not it is actively managed, monitored, or controlled. Product failures do sometimes occur.

The global competitive business environment has placed great pressure on manufacturers to deliver products with more features and higher reliability in a shorter time and at a lower cost. Reliability, time to market, and cost are three critical factors that determine if a product is successful in the marketplace. Customers have demanding expectations for reliability because it affects the safety, availability, and ownership cost of the product.

Better quality leads to less waste, improved competitiveness, higher customer satisfaction, higher sales and revenues, and eventually higher profitability. Meeting the quality and performance goals requires that decisions be based on reliable tests and quantitative test data analysis. Statistical process control (SPC) is such a fundamental quantitative approach to quality control and improvement. Walter Shewhart in 1920s and 1930s pioneered the use of statistical methods as a tool to manage and control production.

A general method and procedure of fatigue reliability and life assessment of steam turbines using ultrasonic inspections is presented in this chapter. The basic structure of an automated ultrasonic inspection system using in turbine surface engineering is briefly introduced. Using the inspection information, a probabilistic model is developed to quantify uncertainties from flaw sizing and model parameters. The uncertainty from flaw sizing is described using a probability of detection model which is based on a classical log-linear model coupling the actual flaw size with the ultrasonic inspection reported size. The uncertainty from model parameters is characterized using Bayesian parameter estimation from fatigue testing data. A steam turbine rotor example with realistic ultrasonic inspection data is presented to demonstrate the overall method. Calculations and interpretations of assessment results based on risk recommendations for industrial applications are also discussed.

As systems and machines continue to become increasingly complex, the demand for automated tools to monitor them has also increased as ensuring the reliability of these machines plays an important role in maximizing availability and minimizing maintenance costs.