Anomaly detection is becoming widely used in Manufacturing Industry to enhance product quality. At the same time, it plays a great role in several other domains due to the fact that anomaly may reveal rare but represent an important phenomenon. The objective of this paper is to detect anomalies and identify the possible variables that caused these anomalies on historical assembly data for two series of products. Multiple anomaly detection techniques were performed; HBOS, IForest, KNN, CBLOF, OCSVM, LOF, and ABOD. Moreover, we used AUROC and Rank Power as performance metrics, followed by Boosting ensemble learning method to ensure the best anomaly detectors robustness. The techniques that gave the highest performance are KNN, ABOD for both product series datasets with 0.95 and 0.99 AUROC respectively. Finally, we applied a statistical root cause analysis on the detected anomalies with the use of Pareto chart to visualize the frequency of the possible causes and its cumulative occurrence. The results showed that there are seven rejection causes for both product series, whereas the first three causes are responsible for 85% of the rejection rates. Besides, assembly machines engineers reported a significant reduction in the rejection rates in both assembly machines after tuning the specification limits of the rejection causes identified by this research results.

The term "Big Data" is a buzzword which describes new technologies that manipulate very large data sets which are massively generated by heterogonous sources. This new term encourages data scientists to extend their work and modify their techniques to overcome the new challenges come with big data concepts. Granular computing has emerged as a new rapidly growing information processing paradigm inside the community of Computational Intelligence. Theories of Fuzzy sets and Rough sets theory are considered powerful examples of granular computing that can be applied to data mining techniques to extract nontrivial knowledge from huge data. The aim of this paper is to introduce a data mining approach for big data based on integrating fuzzy sets and rough sets theories. The proposed approach provides a novel granular data mining for big data that allow extracting useful knowledge and rules from huge data to enhance the decision making process. The proposed approach has been applied on different types of datasets. The experimental results show that our proposed approach is more efficient and robust when dealing with very big datasets and obtained consistent classification rules with classification accuracy 100%.