Data Mining in Manufacturing: A Review

Abstract

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

A process for predicting manhole events in Manhattan

“…An overview of this type of problem in manufacturing is provided by Harding et al (2006). In our case, we are using data that was not designed to be used for predictive analysis, but instead for record-keeping.…”

Generating rules from data mining for collaboration moderator services

“…More detailed reviews of data mining research are given by Choudhary et al (2009), Wang (2007 and Harding et al (2006). Choudhary et al (2009a) consider the use of text mining applications to extract knowledge from post-project reviews.…”

The needs and benefits of Text Mining applications on Post-Project Reviews

“…KDT and TM involve techniques like information retrieval, text analysis, information extraction, clustering categorization, visualization, database technology, machine learning, natural language processing, data mining [29,[42][43][44][45][46] and knowledge management [30]. KDT refers to the overall process of turning unstructured or semi structured textual data into high level information and knowledge.…”