Mining the relationship between production and customer service data for failure analysis of industrial products. (April 2017)
- Record Type:
- Journal Article
- Title:
- Mining the relationship between production and customer service data for failure analysis of industrial products. (April 2017)
- Main Title:
- Mining the relationship between production and customer service data for failure analysis of industrial products
- Authors:
- Kang, Seokho
Kim, Eunji
Shim, Jaewoong
Cho, Sungzoon
Chang, Wonsang
Kim, Junhwan - Abstract:
- Highlights: We propose a data mining process for failure analysis of industrial products. Failures are examined by a mashup of the production and customer service data. Interpretable visualization based on relative failure density is implemented. A case study is conducted using the data of real-world products. Abstract: Analyzing the causal relationships for failures of industrial products is necessary for manufacturers to prevent the occurrence of failures and enhance customer satisfaction. The data collected from each of the production and customer divisions can be a fruitful source for failure analysis. In this paper, we present a data mining process for efficient failure analysis of industrial products by a mashup of data collected from both divisions. The process consists of four main steps: problem definition, preprocessing, modeling, and visualization. Each step is designed to satisfy two constraints in order to be practically applied to industrial products. First, it has to be quick and incremental because the life cycle of most industrial products is not sufficiently long. Second, the insight derived from the process has to be easy to understand for domain experts since they are generally not familiar with data mining methodologies. A case study is conducted to demonstrate the effectiveness of the data mining process by using real-world data collected from a manufacturer in Korea.
- Is Part Of:
- Computers & industrial engineering. Volume 106(2017)
- Journal:
- Computers & industrial engineering
- Issue:
- Volume 106(2017)
- Issue Display:
- Volume 106, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 106
- Issue:
- 2017
- Issue Sort Value:
- 2017-0106-2017-0000
- Page Start:
- 137
- Page End:
- 146
- Publication Date:
- 2017-04
- Subjects:
- Data mining -- Industrial product -- Failure analysis -- Product quality
Engineering -- Data processing -- Periodicals
Industrial engineering -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03608352 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cie.2017.01.028 ↗
- Languages:
- English
- ISSNs:
- 0360-8352
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.713000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 1247.xml