Evolution of The Quality Management Philosophy and Practice
https://nraomtr.blogspot.com/2017/03/evolution-of-quality-management.html
QUALITY 4.0
"Quality 4.0" is a term that references the future of quality and organizational excellence within the context of Industry 4.0.
QUALITY 4.0 PRINCIPLES
People
Quality 4.0 is more than technology. It’s a new way for quality professionals to manage quality with the digital tools available today and understanding how to apply them and achieve excellence through quality. By speaking the digital language and making the case for quality in disruption, quality professionals can elevate their role from enforcers to navigators to successfully guide organizations through digital disruption and toward excellence.
Process
As more work is automated the need for flawless processes remains the same, if not more important. Existing processes will be broken and the need to educate the next generation of workers to implement new processes and strategies will be vital to not only the quality professional but also business operations. Quality is a vital link and should be included at the strategic level for sustainability during digital transformation.
Technology
Technology is growing 10 times faster than it used to, and organizations’ platforms, such as processes, systems, data, operations and governance, must keep pace. Technology also is a great leveler because it gives any individual with the right idea and intent the capability that previously was available only to large organizations. Quality professionals must move from data analyst roles to data wrangler roles by engaging with new technologies, understanding these technologic advancements and the potential outputs they create, and determining how and when to use them.
QUALITY 4.0 TOOLS
Artificial intelligence: computer vision, language processing, chatbots, personal assistants, navigation, robotics, making complex decisions.
Big data: infrastructure (such as MapReduce, Hadoop, Hive, and NoSQL databases), easier access to data sources, tools for managing and analyzing large data sets without having to use supercomputers.
Blockchain: increasing transparency and auditability of transactions (for assets and information), monitoring conditions so transactions don’t occur unless quality objectives are met.
Deep learning: image classification, complex pattern recognition, time series forecasting, text generation, creating sound and art, creating fictitious video from real video, adjusting images based on heuristics (make a frowning person in a photo appear to smile, for example).
Enabling technologies: affordable sensors and actuators, cloud computing, open-source software, augmented reality (AR), mixed reality, virtual reality (VR), data streaming (such as Kafka and Storm), 5G networks, IPv6, IoT.
Machine learning: text analysis, recommendation systems, email spam filters, fraud detection, classifying objects into groups, forecasting.
Data science: the practice of bringing together heterogeneous data sets for making predictions, performing classifications, finding patterns in large data sets, reducing large sets of observations to most significant predictors, applying sound traditional techniques (such as visualization, inference and simulation) to generate viable models and solutions.
