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Accelerated Life Testing (ALT) is now the primary source for obtaining initial information for predicting a product's reliability, durability and maintainability. This paper shows that such testing fails to offer relevant information, because the product's degradation process in testing conditions differs substantially from the product's degradation process under field conditions. Consequently, the mean-time-to-failure is skewed, resulting in problems with recalls, reliability, and maintainability. Accelerated reliability and durability testing technology (ART/ADT) is an approach to the problem which can improve the situation with more accurate prediction of reliability and durability.

This paper will discuss the problem of improving engineering culture for development reliability, quality, and testing of the automotive industry product. The basic approach relates to other industries too. The paper will consider why it is so important for engineers and managers, and how it relates to Systems Engineering, which simply stated is , a system which is an integrated composite of people, products, and processes that provides a capability to satisfy a stated need or objective. One of the basic problems of management is strategic thinking. Predicting is inaccurate when it is based on information obtained from using traditional approaches of accelerated life testing (ALT) data where the degradation (failure) processes differ substantially from the product’s degradation processes during service life under real world conditions.

This paper will discuss how accurate simulation of the real world conditions and ART/ADT (accelerated reliability/durability testing) technology is influencing accurate efficiency predicting as a final goal of product/process design, manufacturing, and development. The paper begins with the overview of current approaches of predicting the efficiency for a complete product and its components with an examples of life cycle costs (LCC), empirical reliability, physics-based reliability, their benefits and risks. It includes also the history of reliability prediction. As a result of the overview, it will be conclude that one cannot ensure that predicting results will not be misinterpreted or misapplied, even though all assumptions and rationale have been meticulously documented and clearly stated.