Big data process analytics for continuous process improvement in manufacturing

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.