Learning to improve reliability during system development |
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| Institution: | 1. Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 633 N. St. Clair, 19th Floor, Chicago, IL, USA;2. Department of Neurology, Health Science Center T12-045, Stony Brook University, Stony Brook, NY 11794-8121, USA;3. University of Southern California, USC Dornsife Center for Self-Report Science, 635 Downey Way, Los Angeles, CA 90089-3332, USA;4. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 1101D McGavran-Greenberg Hall, CB #7411, Chapel Hill, NC 27599-7411, USA;1. Xi''an Jiaotong-Liverpool University, Suzhou Industrial Park, Suzhou 215123, People''s Republic of China;2. National University of Singapore (Suzhou) Research Institute, Suzhou Industrial Park, Suzhou 215123, People''s Republic of China;3. Food Science and Technology Programme, c/o Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore;4. Keio-NUS CUTE Center, National University of Singapore, Singapore 119613, Singapore;1. Equipe de Recherche en Epidémiologie Nutritionnelle, Centre de Recherche en Epidémiologie et Statistiques, Université Paris 13, Inserm (U1153), Inra (U1125), Cnam, COMUE Sorbonne Paris Cité, Bobigny Cedex, France;2. Département de Santé Publique, Hôpital Avicenne, Bobigny Cedex, France |
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| Abstract: | Research, conducted in collaboration with a leading aerospace manufacturer, aimed to facilitate learning in order to improve the reliability of engineering systems during their development phase. In particular, the processes and mathematical models used during reliability growth testing were investigated to assess how they might be better used to support this improvement. This required both soft and hard OR approaches to be adopted. For example, information flows were mapped and reengineered in order to provide a basis for more effective data collection and feed-back to decision-makers. A new mathematical model that combines failure data with engineering judgement was developed to estimate reliability growth. The paper presents a case study describing the problem, the modelling conducted, the recommendations made and the actions implemented. The ways in which the researchers and the manufacturer learnt to improve both the modelling and the reliability growth testing process are reflected upon. |
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