Abstract
In highly-competitive markets, product quality is a critical element of sustainable development for manufacturing enterprises. The pursuit of quality is in essence a commitment to meeting the needs and expectations of customers. Firms often express customer needs in terms of quality characteristics in order to evaluate, monitor, and improve process performance. Reliability is an important quality characteristic. Specifically, reliability is the probability that a product will function as intended and will not malfunction within a certain period of time under certain operating conditions. Clearly, reliability influences the final evaluation made by customers of product quality. Since reliability is a time-oriented quality characteristic, it follows an exponential distribution. The performance standard for most quality indices is 1. To bring evaluation of reliability in line with convention, we propose ratio-based lifetime performance index (RLPI) \(\theta_{L}\). However, time-oriented data are continuous random variables, which implies that the observed values of the data are not precise. Furthermore, the performance of a product in various aspects inevitably declines over time. Clearly, longer product lifetime is not always better; it depends on the type of product. We therefore propose a two-tailed fuzzy statistical test for the RLPI \(\theta_{L}\) to assist manufacturing enterprises in making more accurate evaluations of product reliability. An illustrative example is presented to demonstrate the practical applicability of our proposed approach.
Similar content being viewed by others
References
Borgoni, R., & Zappa, D. (2020). Model-based process capability indices: The dry-etching semiconductor case study. Quality and Reliability Engineering International, 36(7), 2309–2321.
Buckley, J. J. (2005). Fuzzy statistics: Hypothesis testing. Soft Computing, 9(7), 512–518.
Chakraborty, G., Srivastava, P., & Marshall, F. (2007). Are drivers of customer satisfaction different for buyers/users from different functional areas? Journal of Business and Industrial Marketing, 22(1), 20–28.
Chen, K. S. (2022). Fuzzy testing of operating performance index based on confidence intervals. Annals of Operations Research, 311(1), 19–33.
Chen, K. S., & Chang, T. C. (2020). A fuzzy approach to determine process quality for one-sided specification with imprecise data. Proceedings of the Institution of Mechanical Engineers Part B—Journal of Engineering Manufacture, 234(9), 1198–1206.
Chen, K. S., & Chang, T. C. (2021). A modified approach for Six Sigma quality assessment of product with multiple characteristics in intelligent manufacturing environments. Journal of Testing and Evaluation, 49(5), 3035–3053.
Chen, K. S., & Chang, T. C. (2022). Fuzzy testing model for the lifetime performance of products under consideration with exponential distribution. Annals of Operations Research, 312(1), 87–98.
Chen, K. S., Chen, D. F., Huang, M. C., & Chang, T. C. (2020). Analyzing processing quality of machine tools via processed product: Example of ball valve processing machine. Proceedings of the Institution of Mechanical Engineers Part E—Journal of Process Mechanical Engineering, 234(4), 331–341.
Chen, K. S., Chung, L., & Chang, T. C. (2021). Developing a quality-based supplier selection model from the buying company perspective. Quality Technology and Quantitative Management, 18(3), 267–284.
Chen, K. S., Huang, C. F., & Chang, T. C. (2017). A mathematical programming model for constructing the confidence interval of process capability index Cpm in evaluating process performance: An example of five-way pipe. Journal of the Chinese Institute of Engineers, 40(2), 126–133.
Chen, S. H. (2013). Devising appropriate service strategies for customers of different value: An integrated assessment model for the banking industry. International Journal of Human Resource Management, 24(21), 3939–3956.
Cohen, M. A., & Whang, S. (1997). Competing in product and service: A product life-cycle model. Management Science, 43(4), 535–545.
Dağsuyu, C., Polat, U., & Kokangül, A. (2021). Integrated process capability and multi-criteria decision-making approach. Soft Computing, 25(10), 7169–7180.
Ferdousi, F., Baird, K., Munir, R., & Su, S. (2019). Mediating role of quality performance on the association between organisational factors and competitive advantage. International Journal of Productivity and Performance Management, 68(3), 542–560.
Gail, ΜH., & Gastwirth, J. L. (1978). A scale-free goodness of fit test for the exponential distribution based on the Gini Statistic. Journal of the Royal Statistical Society Series b: Statistical Methodology, 40(3), 350–357.
Gunasekera, S., & Wijekularathna, D. K. (2019). Generalized confidence limits for the performance index of the exponentially distributed lifetime. Communications in Statistics—Theory and Methods, 48(3), 755–773.
Hesamian, G., & Akbari, M. G. (2018). Fuzzy process capability indices based on imprecise observations induced from non-normal distributions. Computational and Applied Mathematics, 37(5), 5715–5726.
Hu, X., & Gui, W. (2020). Assessing the lifetime performance index with Lomax distribution based on progressive type I interval censored sample. Journal of Applied Statistics, 47(10), 1757–1775.
Huang, C. C., Chang, T. C., & Chen, B. L. (2021). Fuzzy assessment model to judge quality level of machining processes involving bilateral tolerance using crisp data. Journal of the Chinese Institute of Engineers, 44(1), 1–10.
Jabbari Nooghabi, M. (2020). Process capability indices in normal distribution with the presence of outliers. Journal of Applied Statistics, 47(13–15), 2443–2478.
Jiang, R., & Murthy, D. N. P. (2009). Impact of quality variations on product reliability. Reliability Engineering and System Safety, 94(2), 490–496.
Juran, J. M., & De Feo, J. A. (2010). Juran’s quality handbook: The complete guide to performance excellence (6th ed.). McGraw-Hill.
Kashif, M., Aslam, M., Rao, G. S., Al-Marshadi, A. H., & Jun, C. H. (2017). Bootstrap confidence intervals of the modified process capability index for Weibull distribution. Arabian Journal for Science and Engineering, 42(11), 4565–4573.
Kathuria, R. (2000). Competitive priorities and managerial performance: A taxonomy of small manufacturers. Journal of Operations Management, 18(6), 627–641.
Kim, Y. H., & Schoenherr, T. (2018). The effects of supply chain integration on the cost efficiency of contract manufacturing. Journal of Supply Chain Management, 54(3), 42–64.
Lakhal, L. (2009). Impact of quality on competitive advantage and organizational performance. Journal of the Operational Research Society, 60(5), 637–645.
Lee, A. H. I., Wu, C. W., & Chen, Y. W. (2016). A modified variables repetitive group sampling plan with the consideration of preceding lots information. Annals of Operations Research, 238(1–2), 355–373.
Lee, W. C., Hong, C. W., & Wu, J. W. (2015). Computational procedure of performance assessment of lifetime index of normal products with fuzzy data under the type II right censored sampling plan. Journal of Intelligent and Fuzzy Systems, 28(4), 1755–1773.
Mahmoud, M. A. W., Kilany, N. M., & El-Refai, L. H. (2020). Inference of the lifetime performance index with power Rayleigh distribution based on progressive first-failure–censored data. Quality and Reliability Engineering International, 36(5), 1528–1536.
Mandal, P., Jain, T., & Chakraborty, A. (2021). Quality collaboration contracts under product pricing strategies. Annals of Operations Research, 302(1), 231–264.
Natarajan, M., Senthil, V., Devadasan, S. R., Mohan, N. V., & Sivaram, N. M. (2013). Quality and reliability in new product development a case study in compressed air treatment products manufacturing company. Journal of Manufacturing Technology Management, 24(8), 1143–1162.
Park, C., Dey, S., Ouyang, L., Byun, J. H., & Leeds, M. (2020). Improved bootstrap confidence intervals for the process capability index Cpk. Communications in Statistics: Simulation and Computation, 49(10), 2583–2603.
Pearn, W. L., & Chen, K. S. (1997). Multiprocess performance analysis: A case study. Quality Engineering, 10(1), 1–8.
Proschan, F. (2000). Theoretical explanation of observed decreasing failure rate. Technometrics, 42(1), 7–11.
Rasay, H., Naderkhani, F., & Golmohammadi, A. M. (2020). Designing variable sampling plans based on lifetime performance index under failure censoring reliability tests. Quality Engineering, 32(3), 354–370.
Rosillo-Díaz, E., Blanco-Encomienda, F. J., & Muñoz-Rosas, J. F. (2022). Analysis of the evolution and impact of product quality in business. Total Quality Management and Business Excellence, 33(7–8), 907–928.
Roussas, G. G. (1997). A Course in mathematical statistics (2nd ed.). Academic Press.
Shokouhyar, S., Shokoohyar, S., & Safari, S. (2020). Research on the influence of after-sales service quality factors on customer satisfaction. Journal of Retailing and Consumer Services, 56, 102139.
Sun, F., Yang, H., Chen, J., & Wang, F. (2021). Disclosure of quality preference-revealing information in a supply chain with competitive products. Annals of Operations Research. https://doi.org/10.1007/s10479-021-03945-0
Taheri, S. M., & Arefi, M. (2009). Testing fuzzy hypotheses based on fuzzy test statistic. Soft Computing, 13(6), 617–625.
Wang, K. J., Chang, T. C., & Chen, K. S. (2015). Determining critical service quality from the view of performance influence. Total Quality Management & Business Excellence, 26(3–4), 368–384.
Wang, T. C., Wu, C. W., & Shu, M. H. (2022). A variables-type multiple-dependent-state sampling plan based on the lifetime performance index under a Weibull distribution. Annals of Operations Research, 311(1), 381–399.
Wheel Wright, S. C. (1984). Manufacturing strategy: Defining the missing link. Strategic Management Journal, 5(1), 77–91.
Wu, S. F., & Lin, Y. P. (2016). Computational testing algorithmic procedure of assessment for lifetime performance index of products with one-parameter exponential distribution under progressive type I interval censoring. Mathematics and Computers in Simulation, 120, 79–90.
Yang, J., Meng, F., Huang, S., & Cui, Y. (2020). Process capability analysis for manufacturing processes based on the truncated data from supplier products. International Journal of Production Research, 58(20), 6235–6251.
Acknowledgements
The authors would like to thank the Editor and three anonymous referees for their constructive comments and careful reading, which significantly improved the presentation of this paper. The earlier version of this paper was presented at the 26th ISSAT International Conference on Reliability and Quality in Design (RQD 2021), August 5-7, 2021.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, KS., Yu, CM., Chang, TC. et al. Fuzzy evaluation of product reliability based on ratio-based lifetime performance index. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-04988-7
Accepted:
Published:
DOI: https://doi.org/10.1007/s10479-022-04988-7