Maintenance method of logistics vehicle based on data science and quality

Yu Ping Peng, Shih Chieh Cheng, Yu Tsung Huang, Jun Der Leu

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

1 Scopus citations

Abstract

With the changes in consumption around the world, the global logistics and logistics management has been developed, which has derived the business opportunities of freight logistics and the demand for vehicles, which has led to the increase of vehicles service and service parts. Therefore, effective remaining vehicle readiness and reduction in maintain costs have become an urging subject to be solved in the industry of today. However, as in the era of big data, it is important that the enterprise makes good use of data and information to save costs, increase revenue, and ensure competitive advantages. But if we could not ensure the quality of the data, it would easily lead the analysis to the wrong decisions. Therefore, this study is based on the predictive maintenance, taking the condition of data quality considerations, and using algorithms to construct a decision support model, and proposing optimal replacement cycles and rules for vehicle components, and analyzing the impact on brands and maintenance amounts. Therefore, this study is based on maintenance history, through systematic and manual analysis, to obtain good quality data, and then use chi-square test and algorithm analysis to establish a classification model for decision support. The research department analyzes and analyzes the 3.5 ton freight vehicle maintenance and repair history of a case company from 2008–2016. After the data is cleaned and sorted, it obtains 173,693 work orders and good data quality data for 23 types of maintenance items. And the results show that: the costs contains significant divergence among brands; service parts damage is related to particular environment; we can obtain appropriate service period through proper classification rules. The decision support model constructed by this study will be improved and integrated with the actual needs of the industry on the premise of taking into account the quality of data.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications. DASFAA 2020 International Workshops - BDMS, SeCoP, BDQM, GDMA, and AIDE, Proceedings
EditorsYunmook Nah, Chulyun Kim, Seon Ho Kim, Yang-Sae Moon, Steven Euijong Whang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages131-145
Number of pages15
ISBN (Print)9783030594121
DOIs
StatePublished - 2020
Event7th International Workshop on Big Data Management and Service, BDMS 2020, 6th International Symposium on Semantic Computing and Personalization, SeCoP 2020, 5th Big Data Quality Management, BDQM 2020, 4th International Workshop on Graph Data Management and Analysis, GDMA 2020, 1st International Workshop on Artificial Intelligence for Data Engineering, AIDE 2020, held in conjunction with the 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 - Jeju, Korea, Republic of
Duration: 24 Sep 202027 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12115 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Workshop on Big Data Management and Service, BDMS 2020, 6th International Symposium on Semantic Computing and Personalization, SeCoP 2020, 5th Big Data Quality Management, BDQM 2020, 4th International Workshop on Graph Data Management and Analysis, GDMA 2020, 1st International Workshop on Artificial Intelligence for Data Engineering, AIDE 2020, held in conjunction with the 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Country/TerritoryKorea, Republic of
CityJeju
Period24/09/2027/09/20

Keywords

  • Big data
  • Data quality
  • Vehicle predictive maintenance

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