Skip to main content

Warranty Claims Data

  • Chapter
  • First Online:
Warranty Data Collection and Analysis

Abstract

Data are collected to extract information, which in turn generates knowledge. An understanding of this is important in any data collection and analysis. Warranty claims data are the data that can be collected during the servicing of claims under warranty. In this chapter, we deal with the different types of data that can be collected during the warranty servicing process. We discuss the classification of warranty claims data. This involves four categoriesអproduct related, customer related, service agent related and cost related. Some details of the data for each category are given. Various uncertain factors that affect the collection of warranty claims data are discussed briefly. The chapter concludes with a discussion of the use of warranty claims data for effective management of warranty from a product life cycle perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A different perspective is the statistical perspective. To a statistician, structured data are data that are collected under controlled conditions (e.g., a designed experiment or sample survey), while unstructured data are those collected haphazardly under conditions not under the control of the experimenter. The latter case is often called an “observational study”. In this sense, claims data would almost always be unstructured.

  2. 2.

    Notes for Fig. 4.2

    Can be either a call center or receptionist at a warranty service centre (either retailer or independent agent).

    • The skills and competencies may vary.

    • The repair technician can either be trained by the manufacturer (for repairing specific products) or have general competency to carry out repairs. If the failed item is serviced on site, then a technician must be dispatched to the site.

    • The report for the service agent can differ from that for the vendor of the failed components and/or the manufacturer.

    • The ability to transfer data depends on the compatibilities of the different warranty systems. Some information can be lost in the transfer.

  3. 3.

    Fraudulent claims from customers (and service agents) that go undetected account for about 10ᾢ15% of manufacturers’ warranty costs [4].

  4. 4.

    In the automobile industry, Chrysler was the first to introduce the transfer of warranty with resale before the original warranty expired (Warranty Week February 3, 2003). As a result, the resale values of such cars increased significantly.

  5. 5.

    The conditions under which a warranty contract becomes null and void must be stated explicitly in the warranty document.

  6. 6.

    There can be several reasons for not being able to observe or reproduce the failure (for example, intermittent failures not observed at the service depot). The “trouble not identified” phenomenon in automotive electronics is another example of this [15].

  7. 7.

    In the case of automobiles, each vehicle has a unique identification number (referred to as VIN).

  8. 8.

    This information is needed for traceability of components to batch numbers in production and is discussed in Sect. 5.9.

  9. 9.

    The servicing agent can be a retailer servicing products of a single manufacturer, as is typically the case for products such as automobiles, or a retailer selling brands of various manufacturers (for example, departmental stores), or an authorized independent agent.

  10. 10.

    The material for this section is based on [12].

  11. 11.

    In general, customers may not collect this information. Even when it is collected, it may or may not be communicated to the service agent when a warranty claim is exercised.

  12. 12.

    Customers may or may not know the exact time of failure. Through a process of interaction, however, the service agent may, on occasion, obtain an estimate of this delay.

  13. 13.

    Another example is the modern microwave oven. A customer who uses it only for warming food or boiling water might not detect a failure in some other mode (such as thawing, sensor cooking, etc.).

  14. 14.

    An example of this is a communications company that bought a batch of microwave antennas (for ground-to-ground and ground-to-satellite transmission) for operation in a remote region. Some were put in use and others kept as spares. Failed units were replaced by the spares and claims under warranty would involve returning all failed units on a periodic basis [8].

  15. 15.

    Stages in the evolution of warranty management are discussed in Sect. 2.12.1.

  16. 16.

    This section is adapted from a report [11] involving interviews with representatives from three major automotive manufacturers, four automotive suppliers and one automobile dealer in the USA.

  17. 17.

    This requires supplementary warranty data, as discussed in Chap. 5.

  18. 18.

    Center for Automotive Research (CAR): For more details, visit www.cargroup.org

References

  1. Ackoff RL (1989) From data to wisdom. J Appl Sys Anal 16:3អ9

    Google Scholar 

  2. Bellinger G, Castro D, Mills A (1977) Data, information, knowledge, and wisdom. From http://www.outsights.com/systems/dikw/dikw.htm

  3. Benyon D (1990) Information and data modeling. Alfred Waller, Heneley-on-Thames

    Google Scholar 

  4. Byrne PM (2004) Making warranty management manageable. Logist Manag, August 1, 2004

    Google Scholar 

  5. Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA (2007) Data mining. a knowledge discovery approach. Springer Science, NY

    MATH  Google Scholar 

  6. Holstrom JE (1971) Personal filing and indexing of design data. Proc information systems for designers, University of Southampton, Paper No. 1

    Google Scholar 

  7. Jeske DR, Liu RY (2007) Mining and tracking massive text data: classification, construction of tracking statistics, and inference under misclassification. Technometrics 49:116អ128

    Article  MathSciNet  Google Scholar 

  8. Lyons KF, Murthy DNP (1996) Warranty data analysis: a case study. Proceedings of the 2nd Australia Japan workshop, Gold Coast, July 17អ19

    Google Scholar 

  9. McCallum A (2005) Information extraction: distilling structured data from unstructured text. Soc Comput 3(9):48អ57

    Google Scholar 

  10. Patankar JG, Mitra A (1996) Warranty and consumer behavior: warranty execution. In: Blischke WR, Murthy DNP (eds) Product warranty handbook. Marcel Dekker, New York

    Google Scholar 

  11. Smith BC, Miller RT (2005) The warranty process flow within the automotive industry: an investigation of automotive warranty processes and issues. Report prepared for the centre for automotive research. From www.cargroup.org

  12. Sparker G (2006) Warranty financial management: Part 1អDefining warranty management expenses. Warranty week, Sept 12

    Google Scholar 

  13. Sureka A, De S, Varma K (2008) Mining automotive warranty claims data for effective root cause analysis. Database systems for advanced applications (Lecture notes in computer science), vol 4947. pp 621អ626

    Google Scholar 

  14. Tan AH (1999) Text mining: the state of the art and the challenges. In: Zhong N, Zhou L (eds) PAKDD 1999. LNCS (LNAI), vol 1574. pp 65អ70

    Google Scholar 

  15. Thomas DA, Ayers K, Pecht M (2002) The “trouble not identified” phenomena in automotive electronics. Microelectron Reliab 42:641ᾢ651

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wallace R. Blischke .

Rights and permissions

Reprints and permissions

Copyright information

Š 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Blischke, W.R., Rezaul Karim, M., Prabhakar Murthy, D.N. (2011). Warranty Claims Data. In: Warranty Data Collection and Analysis. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-647-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-647-4_4

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-646-7

  • Online ISBN: 978-0-85729-647-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics