Abstract
Warranty claims data alone are not adequate for purposes of statistical inference in many cases, including estimation of product reliability, prediction of future claims, costs, and so forth. To address many of these problems, additional data, called “supplementary warranty data,” are required. In this chapter, we focus on the various forms of supplementary warranty data that may provide the additional information needed for inference. The most important additional data needed for estimation and other inferences about product reliability are service times of item that did not fail. These data are censored data. For other purposes, a broader class of supplementary data is needed. This may include data from several different sourcesᾢsome internal (from different units or sections of the manufacturer) and others external. For maximum benefit, the collection and analysis of data must be done in the context of the product life cycle. The chapter discusses the various sources of data, the characterization of the data and the role of data in management of the warranty process.
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- 1.
If the interval over which data are collected is given by \( [\tau ,t),\) \( \tau > 0 ,\) then one can have left-censored data for items sold before \( \tau \) that fail in the interval \( [\tau ,t) \) and are still under warranty.
- 2.
One such sub-system is that responsible for collecting the data needed for decision making by top management in the front-end phase of the product life cycle.
- 3.
In Sect. 4.6, we proposed a scheme for the classification of data. A scheme used in the automotive industry involves four domainsᾢ(i) Computer Aided Design (CAD), (ii) Computer Aided Manufacturing (CAM), (iii) Computer Aided Engineering (CAE), and (iv) Service. For details, see [1], where the different data types in each domain are listed.
- 4.
In some organizations D-I-1 and/or D-I-2 are fed to the management system through the marketing system.
- 5.
This can also include sales at the retail level if the manufacturer sells directly to final end-customers.
- 6.
The overall reliability of the product must take into account the implications for warranty costs, customer satisfaction level, etc., all of which are determined during the feasibility phase, as well as the implications for development and production costs. This issue is discussed in detail in [2].
- 7.
In the automotive industry, the Automotive Industry Action Group (AIAG) determined that the four basic uses of (retained) data are (i) product design re-use (including machine and tooling re-use), (ii) service parts management, (iii) legal, and (iv) historical.
- 8.
Notes for Fig. 5.4
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(a)
Data and information from OEM management system
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Strategic decisions relating to the product
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(b)
Data and information from OEM engineering system
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Component design dataᾢreliability specifications, material selection, etc.
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Test data relating to the component during development
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(c)
Data and information from OEM production system
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Assembly of component into the product
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Linking failed components to batch numbers
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Other data relating to the batches under consideration (such as vendors if more than one vendor is used)
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(d)
Data and information from different service agent warranty systems
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D-I-1 of Fig. 5.3
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(e)
Data and information from different vendor production systems
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Linking failed components to batch numbers
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Other data relating to the batches under consideration (such as material used)
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Process control data
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QC dataᾢinspection, testing, etc., for the batch under consideration
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(f)
Data and information from other external sources
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Technical journals
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Reports from research laboratories
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Research reports from academic institutions
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Trade journals (industry specific)
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(a)
- 9.
This section is adapted from [1]
- 10.
Chapter 16 uses this structure in the context of improvements to product and operations.
- 11.
The period can be shorterᾢfor example, a week or a day. If there are two or more in a workday, it can be a shift within a day.
- 12.
[x] is the smallest integer equal to or greater than x.
- 13.
The different kinds of repair are discussed in Sect. 3.7.2. Optimal servicing strategy can involve both repair and replace actions [3, 4]. The analysis of warranty data for these cases follows along similar lines, but can be considerably more involved.
- 14.
We use the first sub-script to denote the structure and the second to denote the scenario so that the set I uv denotes data set for Scenario v (varying from 1 to 4) under Structure u (varying from 1 to 3).
- 15.
SeeChap. 11 and Appendix B.
- 16.
SeeChap. 11 and Appendix B.
References
Bsharah F, Less M (2000) Requirements and strategies for the retention of automotive product data. Comput Aided Des 32:145ᾢ158
Murthy DNP, Rausand M, Osteras T (2008) Product reliabilityᾢperformance and specifications. Springer, London
Murthy DNP, Jack N (2007) Warranty servicing. In: Kenett F, Faltin FW R (eds) Encyclopedia of statistics in quality and reliability. Ruggeri. Wiley, New York
Yun WY, Murthy DNP, Jack N (2008) Warranty servicing strategies with imperfect repair. Int J Prod Econ 111:159ᾢ169
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Blischke, W.R., Rezaul Karim, M., Prabhakar Murthy, D.N. (2011). Supplementary Warranty Data. In: Warranty Data Collection and Analysis. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-647-4_5
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DOI: https://doi.org/10.1007/978-0-85729-647-4_5
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