Quality is a crucial dimension of products and processes. It is considered a competitive advantage for companies and organizations in the global market. Quality models and practices went through several evolutionary steps during modern history—from inspection to control, to quality assurance, to quality management and quality by design. These quality models follow the evolutions and revolutions in industry. It seems however, that in the last few years the quality discipline went into stagnation—very few innovative models for quality are being proposed and quality professionals in companies and organization have apparently lost their leadership positions. Also, the research for new and innovative quality models is scarce. The fourth industrial revolution is an opportunity for the quality movement to become a leading force. This poses significant challenges to the quality profession by emphasizing the need to adapt to technology innovations, to modern data analytics and to the entrepreneurships ecosystem that characterize an era of the fourth industrial revolution. In this paper, we present a framework for a quality discipline supporting the fourth industrial revolution. We propose to call it Quality 4.0. The paper also offers future directions for quality and reliability engineering that leverage opportunities derived from the fourth industrial revolution. Specifically, we discuss: (1) Quality as a data driven discipline, (2) the application of modeling and simulation for evidence-based quality engineering, (3) health monitoring and prognostics for quality, (4) integrated quality management, (5) maturity levels with respect to the fourth industrial revolution, (6) integrating innovation with quality and managing for innovation, (7) Quality 4.0 and data science, (8) integrating reliability engineering with quality engineering, and finally, (9) information quality. We are aware that these directions are still not a comprehensive picture of Quality 4.0. We claim however, that they constitute a substantial basis to update the body of knowledge and practices of the quality profession.

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A Bayesian network (BN) is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, BNs are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies among the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable, or a hypothesis. They are not restricted to represent random variables, which form the “Bayesian” aspect of a BN. Efficient algorithms exist that perform inference and learning in BNs. BNs that model sequences of variables are called dynamic BNs. In this context, [A. Harel, R. Kenett, and F. Ruggeri, Modeling web usability diagnostics on the basis of usage statistics, in Statistical Methods in eCommerce Research, W. Jank and G. Shmueli, eds., Wiley, 2008] provide a comparison between Markov Chains and BNs in the analysis of web usability from e-commerce data. A comparison of regression models, structural equation models, and BNs is presented in Anderson et al. [R.D. Anderson, R.D. Mackoy, V.B. Thompson, and G. Harrell, A bayesian network estimation of the service–profit Chain for transport service satisfaction, Decision Sciences 35(4), (2004), pp. 665–689]. In this article we apply BNs to the analysis of customer satisfaction surveys and demonstrate the potential of the approach. In particular, BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.