Quantifying Optimum Burn-in Period

Early life failures are usually attributed to problems in the manufacturing, packaging and assembling phases. After making improvements to the greatest possible extent in these phases, the only way to improve the products reliability is to eliminate defective units before they reach customers. A common approach used to weed out defective and weak parts before shipping them to customers is burn-in. Burn-in has become a frequently-used procedure in many industries, especially the electronics industry. One common problem that industries face when dealing with burn-in is quantifying and optimizing the burn-in testing period. This article introduces a simple method to optimize the burn-in period and illustrates how this analysis can be performed in Weibull++.

Monitoring Warranty Returns Using Statistical Process Control (SPC)

Warranty return predictions are important for companies because they help create an idea about the anticipated number of returns for each subsequent period, determine whether improvements are necessary if the expected returns are too high and determine how many spare parts to manufacture, allocate or ship to distributors in order to be better prepared for quick customer warranty claims fulfillment.

Warranty return predictions can also be used as a tool to verify whether actual experienced returns fall in line with expectations. By monitoring warranty return data, the analyst (and the organization) can detect specific return periods and/or batches of sales or shipments that deviate from the assumed model by comparing actual returns to the previously predicted returns. If the actual experienced returns number deviates significantly from the number that was predicted, this might raise a flag calling for further investigations or for actions to be taken to treat the problem. This provides the advantage of early notification of possible deviations in manufacturing, use conditions and/or any other factors that may adversely affect the reliability of the fielded product. Obviously, the motivation for performing such analysis is to allow for faster intervention to avoid increased costs due to increased warranty returns or more serious repercussions. This article will discuss a methodology for monitoring warranty returns using Statistical Process Control (SPC).