Paper Info
Title
Big data process analytics for continuous process improvement in manufacturing
Abstract
One of the most important challenges in manufacturing is the continuous process improvement that requires new insights about the behavior/quality control of processes in order to understand the optimization/improvement potential. The paper elaborates on usage of big data-driven clustering for an efficient discovering of real-time unusualities in the process and their route-cause analysis. Our approach extends traditional clustering algorithms (like k-Means) with methods for better understanding the nature of clusters and provides a very efficient big data realization. We argue that this approach paves the way for a new generation of quality management tools based on big data analytics that will extend traditional statistical process control and empower Lean Six Sigma through big data processing. The proposed approach has been applied for improving process control in Whirlpool (washing machine tests, factory in Italy) and we present the most important finding from the evaluation study.
Year
DOI
Venue
2015
10.1109/BigData.2015.7363900
Big Data
Keywords
Field
DocType
Big data, manufactoring, quality control
Data science,Data mining,Six Sigma,Computer science,Statistical process control,Process control,Cluster analysis,Analytics,Big data,Lean Six Sigma,Quality management
Conference
Citations 
PageRank 
References 
1
0.39
2
Authors
3
Name
Order
Citations
PageRank
Nenad Stojanovic11605152.52
Marko Dinic210.39
Ljiljana Stojanovic31113119.17