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Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart manufacturing. Although these driving technologies have the capacity to advance the state of the art in manufacturing, it is not trivial to do so. Current benchmarks of quality, conformance, productivity, and innovation in industrial manufacturing have set a very high bar for machine learning algorithms. A new concept has recently appeared to address this challenge: Quality 4.0. This name was derived from the pursuit of performance excellence during these times of potentially disruptive digital transformation. The hype surrounding artificial intelligence has influenced many quality leaders take an interest in deploying a Quality 4.0 initiative. According to recent surveys, however, 80–87% of the big data projects never generate a sustainable solution. Moreover, surveys have indicated that most quality leaders do not have a clear vision about how to create value of out these technologies. In this manuscript, the process monitoring for quality initiative, Quality 4.0, is reviewed. Then four relevant issues are identified (paradigm, project selection, process redesign and relearning problems) that must be understood and addressed for successful implementation. Based on this study, a novel 7-step problem solving strategy is introduced. The proposed strategy increases the likelihood of successfully deploying this Quality 4.0 initiative.

In big data-based analyses, because of hyper-dimensional feature spaces, there has been no previous distinction between machine learning algorithms (MLAs). Therefore, multiple diverse algorithms should be included in the analysis to develop an adequate model for detecting/recognizing patterns exhibited by classes. If multiple classifiers are developed, the next natural step is to determine whether the prediction benchmark set by the top performer can be improved by combining them. In this context, multiple classifier systems (MCSs) are powerful solutions for difficult pattern recognition problems because they usually outperform the best individual classifier, and their diversity tends to improve resilience and robustness to high-dimensional and noisy data. To design an MCS, an appropriate fusion method is required to optimally combine the individual classifiers and determine the final decision. Process monitoring for quality is a Quality 4.0 initiative aimed at defect detection via binary classification. Because most mature organizations have merged traditional quality philosophies, their processes generate only a few defects per million of opportunities. Therefore, manufacturing data sets for binary classification of quality tends to be highly/ ultra-unbalanced. Detecting these rare quality events is one of the most relevant intellectual challenges posed to the fourth industrial revolution, Industry 4.0 (I 4.0). A new MCS aimed at analyzing these data structures is presented. It is based on eight well-known MLAs, an ad hoc fitness function, and a novel meta-learning algorithm. For predicting the final quality class, this algorithm considers the prediction from a set of classifiers as input and determines which classifiers are reliable and which are not. Finally, to demonstrate the superiority of the MLAs over extensively used fusion rules, multiple publicly available data sets are analyzed.

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