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Use of Warranty Data for Improving Current Products and Operations

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Warranty Data Collection and Analysis

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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Abstract

For existing products, the analysis of warranty data allows a manufacturer to evaluate various performance measures at the product and business levels, and to assess these relative to the targets defined during the front-end feasibility and design stages of the product life cycle. If the actual performance measures differ significantly from the target values, actions are needed to correct the underlying problems, which may be design, production, customer-related, and/or warranty servicing problems. Total Quality Management (TQM) provides an approach for addressing these through continuous improvement. In this chapter, we look at the use of warranty data for continuous improvement of the product and the manufacturing process by identifying the underlying causes and implementing effective strategies for solving the problems identified.

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Notes

  1. 1.

    The concept of the PDCA Cycle was originally developed by Walter Shewhart and is often referred to as the “Shewhart Cycle”. It was promoted very effectively by W Edwards Deming and is also referred to as the “Deming Wheel.” Discussion on PDCA can be found in many books on quality. See, for example, [8, 19].

  2. 2.

    There are a many books that discuss problem solving; see for example [1, 33].

  3. 3.

    There are many books on thinking. See, for example [7, 12].

  4. 4.

    There are many books that cover most of the techniques listed. See, for example [3, 29, 34, 41]. Books that deal primarily with one method are [35] dealing with the 5WHY’S, [20] with Fishbone diagrams, [39] with failure mode and effects analysis, and [5] with fault tree analysis.

  5. 5.

    See Sect. 9.4.4.

  6. 6.

    The different actions are also referred to as “labor codes”ᾢsee, for example [45], or as “defect codes”.

  7. 7.

    See Sect. 3.6 for various notions of usage.

  8. 8.

    Measuring satisfaction for consumer durables is different from that for industrial products. For more details regarding satisfaction with consumer durable products, see [36]. For industrial products, see [16].

  9. 9.

    In the IT-warranty literature, the term “Early Warning System” is used to denote software packages for carrying out this kind of analysis.

  10. 10.

    This is referred to as “No Fault Found”. For more on this, see [43].

  11. 11.

    MOP (month of production); MIS (month in service before failure).

  12. 12.

    There are many papers that deal with thisᾢfor example [45] deals with MIS-MOP plots and [25] looks at time-series plots.

  13. 13.

    See [6] for further discussion.

  14. 14.

    WCR can be defined in many different ways. One definition is the following: WCR = [Cumulative number of claims/Number of items sold] m 1000.

  15. 15.

    Sometimes the target value is determined by the average value of the WCRs, based on batches produced after to the process has reached its steady state.

  16. 16.

    This is discussed in detail in [42].

  17. 17.

    A case study involving Level 2 analysis for several electronic products produced by Philips can be found in [37].

  18. 18.

    For details of this case, see [21].

  19. 19.

    Reference

    [24] defines attribution theory as "a theory about how people make causal explanations for events of which they have knowledge, about how they answer questions beginning with ‘why'.".

  20. 20.

    For more details regarding SERVQUAL, see [46].

  21. 21.

    Several books on consumer behavior (e.g., Reference [32]) discuss consumer satisfaction in detail.

  22. 22.

    See http://www.usatoday.com/money/autos/2003-09-03-carbuyback_x.htm.

  23. 23.

    See http://www.usatoday.com/money/autos/2002-09-20-honda-warranty_x.htm.

  24. 24.

    See [31] for a review of the issues involved in warranty logistics.

  25. 25.

    For more on agency theory and incentive contracts, see [9, 44].

  26. 26.

    Issue of April 19, 2005.

  27. 27.

    Warranty Week, April 12, 2005.

  28. 28.

    Figure 15.10 is based on an approach proposed by professor suzuki. Reference [40] uses the same approach to solving design and service related problems with “process standard” replaced by “design standard” or “service standard”, respectively.

  29. 29.

    The TREAD Act was introduced into Congress as H.R. 5164, and signed into law by President Bill Clinton on Nov. 1, 2000 as Public Law 106ᾢ414.

  30. 30.

    This section is based on material from [15].

  31. 31.

    The design process also deals with the bill of materials (BOM) for production planning, all of the drawings (assembly and detail), and other production documents.

  32. 32.

    The new product development process is discussed in Chap. 16.

  33. 33.

    The most famous case of this is the well documented Ford-Firestone debacle. For an overview, see Wikipedia. Scores of articles can be found on Google.

  34. 34.

    This is called “metallurgical analysis” in the case of metallic components.

  35. 35.

    The material for this case is based on [2] and is used with their permission.

  36. 36.

    Factor analysis is a technique in multivariate statistical analysis that is an attempt to model the variability in a large number of observed variables in terms of a smaller number of unobservable variables called factors. The objection is to reduce the data set to a manageable number of variables. See [30] or books on multivariate analysis for details.

  37. 37.

    This case study is based on material from [27], where the approach is used to address the issues raised in complaints from customers regarding excessive noise from the bearings. The warranty data indicated that roughly 3% of the bearings suffered from this problem, and it was deemed to be a design problem. Ford reliability engineers initiated a reliability improvement process to fix this problem and reduce warranty cost and customer dissatisfaction.

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Authors and Affiliations

  1. 5401 Katherine Avenue, 91401-4922, Sherman Oaks, Los Angeles, CA, USA

    Wallace R. Blischke

  2. Department of Statistics, Rajshahi University, Rajshahi, Bangladesh

    M. Rezaul Karim

  3. School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    D. N. Prabhakar Murthy

Authors
  1. Wallace R. Blischke
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  2. M. Rezaul Karim
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  3. D. N. Prabhakar Murthy
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Correspondence to Wallace R. Blischke .

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Blischke, W.R., Rezaul Karim, M., Prabhakar Murthy, D.N. (2011). Use of Warranty Data for Improving Current Products and Operations. In: Warranty Data Collection and Analysis. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-647-4_15

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  • DOI: https://doi.org/10.1007/978-0-85729-647-4_15

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