NDLI: Research on cleaning inaccurate data in production management module in ERP

Content Provider	IEEE Xplore Digital Library
Author	Zong Wei Wu Feng Li Peipei
Copyright Year	2012
Description	Author affiliation: The office of Talent CPA, Nan Jing, China (Li Peipei) \|\| School of Management, Department of Industrial and Manufacturing Systems Engineering, Xi'an Jiaotong University, XJTU, China (Zong Wei; Wu Feng)
Abstract	With the rapid development of information technology, data quality has become a key factor in successfully operating and implementing ERP system. The problem of how to improve and enhance data quality in ERP has become an important research direction. However, because of the hugeness and complexity of ERP, this paper focuses on production management module and mainly aims at inaccurate data in it. Inaccurate data includes continuous abnormal data, discrete abnormal data and approximately duplicate records. Moreover, this paper designs different processes for detecting and cleaning different types of inaccurate data and then applies these processes to production management module in ERP system. At last, this paper illustrates how to use SOM clustering method and BP neural network to detect inaccurate data in production management module. It has certain directive significance for improving data quality in actual ERP system.
Starting Page	580
Ending Page	582
File Size	293947
Page Count	3
File Format	PDF
ISBN	9781457720246
e-ISBN	9781457720253
DOI	10.1109/ICSSSM.2012.6252304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Production management Inaccurate Data ERP Clustering methods Neural networks Production Management Module Educational institutions Approximation algorithms Cleaning Marketing and sales Data Cleaning
Content Type	Text
Resource Type	Article

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