Improving MTM-UAS to predetermine automotive maintenance times

Giuseppe Di Gironimo, Carmine Di Martino, Antonio Lanzotti, Adelaide Marzano, Gianluca Russo

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

The preventive knowledge of serviceability times is a critical factor for the quantification of after-sales services costs of a vehicle. Predetermined motion time system are frequently used to set labor rates in industry by quantifying the amount of time required to perform specific tasks. The first such system is known as Methods-time measurement (MTM). Several variants of MTM have been developed differing from each other on their level of focus. Among them MTM-UAS is suitable for processes that average around 1-3 min. However experimental tests carried out by the authors in Elasis (Research Center of FIAT Group) demonstrate that MTM-UAS is not the optimal approach to measure serviceability times. The reason is that it doesn't take into account ergonomic factors. In the present paper the authors propose to correct the MTM-UAS method including in the task analysis the study of human postures and efforts. The proposed approach allows to estimate with an "acceptable" error the time needed to perform maintenance tasks since the first phases of product design, by working on Digital Mock-up and human models in virtual environment. As a byproduct of that analysis, it is possible to obtain a list of maintenance times in order to preventively set after-sales service costs. © 2012 Springer-Verlag.
Original languageEnglish
Pages (from-to)265-273
Number of pages9
JournalInternational Journal on Interactive Design and Manufacturing
Volume6
Issue number4
DOIs
Publication statusPublished - 1 Nov 2012
Externally publishedYes

Keywords

  • work measurement
  • predetermined time analysis
  • MTM-UAS
  • virtual maintenance
  • ergonomics
  • digital humans

Fingerprint

Dive into the research topics of 'Improving MTM-UAS to predetermine automotive maintenance times'. Together they form a unique fingerprint.

Cite this