Quality & Quantity

, 42:795

Measuring the readability performance (RP) of aircraft maintenance technical orders by fuzzy MCDM method and RP index

OriginalPaper

Abstract

With the development of the global economy and the ease of air transportation, extra emphasis has been placed on flight safety. There are precise specifications and procedures in the operation and maintenance of aircraft. Human errors and mechanical disorders are two key factors of flight safety. The maintenance personnel need to follow an outlined procedure to avoid human errors and ensure flight safety. Readability of aircraft maintenance technical orders can affect the quality and reliability of aircraft maintenance. To ensure the editing quality of technical orders, controlling/monitoring the number of unreadable sentences is important and necessary. In this study, the number of unreadable sentences found in a technical order was used as the measure of readability performance (RP) as well as a readability performance index was provided to evaluate whether the RP of individual readability characteristics of technical orders was adequate. Different readability characteristics make different grade of RP loss. Based on fuzzy multiple criteria decision-making (Fuzzy MCDM) approaches, we investigated the expert opinions to rank and calculate the weights of all readability characteristics. At the same time, we proposed the upper limits of unreadable sentences according the weights of individual readability characteristics. In this paper, the technical orders issued by Taiwan Aerospace Industrial Development Corporation was used as an example to evaluate the readability of the technical orders and total RP losses for individual readability characteristics. Finally, an improved way of editing quality for technical orders was recommended.

Keywords

Technical order Readability performance index Readability characteristics Fuzzy multiple criteria decision-making (Fuzzy MCDM) 

References

  1. Broadbent D. (1977) Language and ergonomics. Appl. Ergon. 17, 266–77Google Scholar
  2. Chapanis A. (1965) Words Words Words. Hum. Factors 7, 1–7Google Scholar
  3. Chen S.H. (1985) Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets Syst. 17, 113–29CrossRefGoogle Scholar
  4. Clark H.H., Chase W.G. (1972) On the process of comparing sentences against pictures. Cognit. Psychol. 3, 472–17CrossRefGoogle Scholar
  5. Foster, S.T.: Managing Quality–An Integrative Approach. Prentice Hall (2001)Google Scholar
  6. International Air Transport Association (IATA) Safety Report (Jet): International Air Transport Association and Airclaims Ltd. Montreal, Canada (2001)Google Scholar
  7. Jones, J.V.: Logistic Support Analysis Handbook. TAB Books, Inc., Blue Ridge Summit, PA (1999)Google Scholar
  8. Kammann R. (1975) The comprehensibility of printed instructions and the flow chart alternative. Hum. Factors 17, 183–91Google Scholar
  9. Kaufmann A., Gupta M.M. (1991) Introduction to Fuzzy Arithmetic: Theory and Application. Van Nostrand Reinhold, New YorkGoogle Scholar
  10. Langari J.Y., Zadeh L.A. (1995) Industrial Applications of Fuzzy Logic and Intelligent Systems. IEEE Press, New YorkGoogle Scholar
  11. Lyonnet, P.: Maintenance Planning Paris. Chapman and Itall (1998)Google Scholar
  12. Mayer R. (1997) Multimedia instruction: are we asking the right question. Educ. Psychol. 32, 1–9CrossRefGoogle Scholar
  13. Mendel J.M. (1995) Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83, 345–77CrossRefGoogle Scholar
  14. Montgomery D.C. (2002) Introduction to Statistical Quality Control, 2nd edn. John Wiley & Sons, New YorkGoogle Scholar
  15. Norman D.A. (1988) The Psychology of Everyday Things. Book Inc, New YorkGoogle Scholar
  16. Ross S.M. (1985) Introduction to Probability Models. Academic Press, CaliforniaGoogle Scholar
  17. Sanders, M.S., Mc Cormick, E.J.: Human Factors in Engineering and Design 7th ed. The Mc Graw-Hill Companies, Inc. (1993)Google Scholar
  18. Tang M.T., Tzeng G.H. (1999) A hierarch fuzzy MCDM method for studying electronic marketing strategies in the information service industry. J. Int. Inf. Manage. 8(1): 1–2Google Scholar
  19. Teng J.Y., Tzeng G.H. (1996) Fuzzy multicriteria ranking of urban transportation investment alternative. Transport. Plann. Technol. 20(1): 15–1CrossRefGoogle Scholar
  20. Wu L.L., Sung W.P., Chu F.L., Feng C.J. (2004) Using theory of Six Sigma to improve quality of construction engineering. J. Civil Eng. Technol. (Taiwan) 8(1): 18–8Google Scholar
  21. Zadeh L.A. (1965) Fuzzy sets. Inf. Control 8(3): 338–53CrossRefGoogle Scholar
  22. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, part 1, 2 and 3. Inf. Sci., 8, 199–49, 301–57(1975); 9 43–8 (1976)Google Scholar
  23. Zhau R., Govind R. (1991) Algebraic characteristics of extend fuzzy numbers. Inf. Sci., 54(1): 103–30CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2008

Authors and Affiliations

  1. 1.Department of Industrial Engineering and ManagementChienkuo Technology UniversityChanghuaTaiwan, ROC
  2. 2.Department of Leisure Industry ManagementNational Chin-Yi University of TechnologyTaichungTaiwan, ROC
  3. 3.National Palace MuseumTaipeiTaiwan, ROC

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