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Authors: Ralf Gitzel 1 ; Subanatarajan Subbiah 1 and Christopher Ganz 2

Affiliations: 1 Corporate Research Germany, Germany ; 2 Global Service R&D, Switzerland

Keyword(s): Data Quality, Reliability, CMMS, Dashboard, Case Study, Failure Data, Industrial Service.

Abstract: Reliability or survival data analysis is an important tool to estimate the life expectancy and failure behaviour of industrial assets such as motors or pumps. One common data source is the Computerized Maintenance Management System (CMMS) where all equipment failures are reported. However, the CMMS typically suffers from a series of data quality problems which can distort the calculation results if not properly addressed. In this paper, we describe the possible data quality problems in reliability data with a focus on CMMS data. This list of problems is based on the results of six case studies conducted at our company. The paper lists a set of metrics which can be used to judge the severity. We also show how the impact of data quality issues can be estimated. Based on this estimate, we can calibrate a series of metrics for detecting the problems shown.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Gitzel, R.; Subbiah, S. and Ganz, C. (2018). A Data Quality Dashboard for CMMS Data. In Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-285-1; ISSN 2184-4372, SciTePress, pages 170-177. DOI: 10.5220/0006552501700177

@conference{icores18,
author={Ralf Gitzel. and Subanatarajan Subbiah. and Christopher Ganz.},
title={A Data Quality Dashboard for CMMS Data},
booktitle={Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - ICORES},
year={2018},
pages={170-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006552501700177},
isbn={978-989-758-285-1},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Operations Research and Enterprise Systems - ICORES
TI - A Data Quality Dashboard for CMMS Data
SN - 978-989-758-285-1
IS - 2184-4372
AU - Gitzel, R.
AU - Subbiah, S.
AU - Ganz, C.
PY - 2018
SP - 170
EP - 177
DO - 10.5220/0006552501700177
PB - SciTePress