Modeling the Maintenance Time Considering the Experience of the Technicians

Hyunjong Shin, Kai wen Tien, Vittaldas Prabhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Typically, maintaining a machine requires two different distinct tasks: To select which components to focus their attention; and the subsequent task is to check, repair or replace the selected components. For both tasks, the experience of technicians plays a critical role. An experienced technician is likely to select fewer components and requires less time for the subsequent task compared to an inexperienced technician. As a result, the maintenance time will be varied depending on the experience of the technicians. Extant research for maintenance has predominantly used exponential distribution family for modeling primarily because of its analytical tractability but at the cost of fidelity and inability to capture important characteristics such as technicians’ experience. With the growing adoption of networked sensors based on Internet of Things (IoT), big data, and real-time machinery diagnostics using artificial intelligence it is imperative to develop models with better fidelity for maintenance operations. Therefore, in this paper, we explore a model based on using the negative-hyper geometric distribution for maintenance time that varies based on the technicians’ experience. Our proposed approach requires more inputs such as (1) number of components, (2) number of components not in working state (3) technician’s experience level, and (4) time to fix a component based on the technicians’ experience. For instance, input for (2) could be obtained from IoT sensors and diagnostics. We study the efficacy of the proposed model using computer simulations and statistically characterize the possible impact of technician experience on the parameters of the maintenance distribution.

Original languageEnglish (US)
Title of host publicationAdvances in Production Management Systems. Production Management for the Factory of the Future - IFIP WG 5.7 International Conference, APMS 2019, Proceedings
EditorsFarhad Ameri, Kathryn E. Stecke, Gregor von Cieminski, Dimitris Kiritsis
PublisherSpringer New York LLC
Pages716-721
Number of pages6
ISBN (Print)9783030299996
DOIs
StatePublished - Jan 1 2019
EventIFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2019 - Austin, United States
Duration: Sep 1 2019Sep 5 2019

Publication series

NameIFIP Advances in Information and Communication Technology
Volume566
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

ConferenceIFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2019
CountryUnited States
CityAustin
Period9/1/199/5/19

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

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