Key Data Quality Pitfalls for Condition Based Maintenance

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

In today's competitive and fluctuating market, original equipment manufacturers (OEMs) must be able to offer aftersales services along with their products, such as condition based maintenance, extended warranty services etc. Condition based maintenance requires detailed understanding about products' operational behaviour, to detect problems before they occur, and react accordingly. Typically, Condition based maintenance consists of data collection, data analysis, and maintenance decision stages. Within this context, data quality is one of the key drivers in the knowledge acquisition process since poor data quality impacts the downstream maintenance processes, and reciprocally, high data quality will foster good decision making. The prospect of new business opportunities and better services to customers encourages companies to collect large amounts of data that have been generated in different stages of product lifecycle. Despite of availability of data, as well as advanced statistical and analytical tools, companies are still struggling to provide effective service by reducing maintenance cost and improving uptime. This paper highlights data related pitfalls that hinder organisations to improve maintenance services. These pitfalls are based on case studies of two globally operating Finnish manufacturing companies where maintenance is one of the major streams of income.

Details

Original languageEnglish
Title of host publication2017 2nd International Conference on System Reliability and Safety, ICSRS 2017
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on System Reliability and Safety - Milan, Italy
Duration: 20 Dec 201722 Dec 2017
Conference number: 2

Conference

ConferenceInternational Conference on System Reliability and Safety
Abbreviated titleICSRS
CountryItaly
CityMilan
Period20/12/201722/12/2017

    Research areas

  • condition based maintenance, data analysis, data quality, data reliability, after-sales service, statistics

ID: 16075795