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Lifetime performance evaluation model based on quick response thinking
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Chaoyang University of Technology, Department of Finance, Taichung 41349, Taiwan, R. O. C.
National Chin-Yi University of Technology, Department of Industrial Engineering and Management, Taichung 411030, Taiwan, R. O. C
Asia University, Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan,R. O. C.
Chaoyang University of Technology, Department of Business Administration, Chaoyang University of Technology, Taichung 41349, Taiwan, R. O. C
Publication date: 2022-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(1):1–6
  • This study explored the lifetime of products based on type II censoring.
  • Adopted right censoring to find the bester estimator of the lifetime index.
  • The 1-α confidence interval and UMP test model of lifetime index were found.
  • A numerical example was demonstrated the application of the proposed model.
  • Under above methods, the products can take market share earlier.
In practice, lifetime performance index CL has been a method commonly applied to the evaluation of quality performance. L is the upper or lower limit of the specification. The product lifetime distribution is mostly abnormal distribution. This study explored that the lifetime of commodities comes from exponential distribution. Complete data collection is the primary goal of analysis. However, the censoring type is one of the most commonly used methods due to considerations of manpower and material cost or the timeliness of product launch. This study adopted Type-II right censoring to find out the uniformly minimum variance unbiased (UMVU) estimator of the lifetime performance index CL and its probability density function. Afterward this study obtained the 100×(1-α)% confidence interval of the lifetime performance index CL as well as created the uniformly most powerful (UMP) test and the power of the test for the product lifetime performance index. Last, this study came up with a numerical example to demonstrate the suggested method as well as the application of the model.
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