Designing an Early Detection Model for Product Reliability Defects Using Warranty Data Analysis and Production Line Quality Test Results (Case study of engine room)
Volume 8, Issue 2, Summer 2018, Pages 86-97
Amir Sharifpour, Kamyar Sabri Laghaei, Hamid Reza Izadbakhsh, Morteza Agah
Abstract Guarantee costs in production companies are very important and can have a great impact on the profit of the company. Putting effort into reducing these costs may result in profit increase. In this regard, detecting reliability related defects before their occurrence can be useful in reducing guarantee costs and also customer dissatisfaction. Reliability problems may be due to manufacturing defects. Removing defective items during production process can prevent high guarantee and customer dissatisfaction costs. In this paper a model is developed for early detection of reliability defects by means of guarantee data and qualitative parameters of the production line. A case study on TU5 engines manufactured by Irankhodro Company is also included.
The effect of random percentage of defective items on product reliability
Volume 7, Issue 4, Winter 2018, Pages 246-270
Kamiar Sabri Lagha, maryam Mazhar
Abstract The reliability of manufactured products can vary according to changes in production quality. Field failure data provide useful information for assessing whether changes in reliability are significant or identifying the cause of changes. In order to identify these errors, we need to model the effect of these errors on product reliability. In this research, we intend to predict product reliability behavior based on the percentage of different quality errors with which products may be produced. In this regard, two types of quality errors, namely non-compliant items and assembly error are examined separately. In order to model, it is assumed that the percentage of qualitative errors follow the beta distribution and the failure times follow the Weibull distribution. Reliability, risk rate and probability chart of products are studied under these two types of qualitative errors. Based on the results of this research, it is possible to guess the type and percentage of quality errors with which products are produced.