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Technology has drastically changed the way businesses function over the years. Advancements in technology, specifically in big data and analytics have made a significant impact on quality management practices, introducing a new approach known as Quality 4.0. This new era of quality management integrates digital technologies, resulting in improved quality management practices, increased customer satisfaction, revenue, and competitive advantage.

Quality 4.0 uses advanced technologies such as big data and analytics to predict process conditions and identify quality characteristics that are essential to customers and the enterprise. Implementing this approach has allowed for a more data-driven approach to quality management, providing businesses with critical insights into their processes and customers' needs. Furthermore, it has also led to the development of new frameworks and perspectives on quality management, leading to increased organizational performance.

Machine learning has become a powerful tool in detecting fraudulent behavior in various industries, including healthcare, insurance, and manufacturing. ML algorithms and techniques have shown promise in improving quality control and prediction of equipment failures, with the potential to reduce human workload and improve quality assurance practices across different fields.

The study of different ways to detect Medicaid fraud using supervised and unsupervised machine learning algorithms provides insight into the strengths and limitations of these approaches. While supervised methods require regularly updated training datasets to identify fraudulent behavior based on predefined class labels, unsupervised techniques analyze the entire dataset to find patterns and identify outliers without prior knowledge of fraud or legitimate behavior.

Quality management (QM) is an essential component of any organization that strives for success and growth. However, with the growing complexity of today's society and the rapid pace of change, it has become increasingly challenging to maintain and develop QM practices that are effective and ethical. To address this, a new paper has been published that provides a roadmap for researchers and practitioners to meet the challenges of the future successfully.

The fourth industrial revolution, better known as Industry 4.0 has brought a massive transformation to the manufacturing sector, with the adoption of advanced technologies such as big data analytics, autonomous robots, and the Internet of Things (IoT). The integration of these technologies holds the promise of increasing productivity, efficiency, and sustainability in the manufacturing sector. However, it requires a focus on human competencies and skill development alongside the implementation of these technologies. As such, companies that plan to embrace Industry 4.0 must improve their understanding of this digital transformation through research, innovation, and creativity.

In today's fast-paced business environment, companies must focus on improving the quality of their products and services to remain competitive. The integration of Total Quality Management (TQM) and Industry 4.0 (I4.0) has led to a new approach called Quality 4.0, which emphasizes the application of digital technologies and data-driven insights for continuous improvement and innovation.

A recent scientific paper presented the findings of a literature review on the integration of prescriptive analytics in Quality 4.0. Prescriptive analytics is a type of data analytics that uses algorithms and mathematical models to make recommendations for future actions. The study demonstrated that incorporating prescriptive analytics can help organizations achieve higher levels of quality management by providing real-time recommendations for process improvement and optimization.