The history of quality management can be traced back to the middle-ages, where inspection was a widely used quality control measure. Over time, as businesses experienced growth and expansion, it became difficult to inspect all end products against specific requirements. 

It is during the 1940s that industrial quality management leaders such as Deming, Roming, and Juran introduced a new wave of changes that transformed the quality management sector. Their suggestions gave rise to the concept of ‘Total Quality Management.’ Where quality is ensured through specific production intervals. 

Today, the growth in technological innovations is not only introducing significant changes in quality management but also across other dockets. Cloud computing, better data analytics tools, the blockchain, and artificial intelligence are some of the top technologies businesses are continuing to leverage in their quality management processes. 

Unraveled below is how quality new technologies revolutionize quality management. 

Solidified quality control measures through cloud computing

Over recent years, cloud computing has become a household term for many enterprises. It is the delivery of computing services such as networking, software, and data storage via the internet. Some of the benefits brought about to quality management by the innovation that reinforces quality control procedures include: 

  • Flexibility
  • Scalability
  • Security
  • Cost-effectiveness

Flexibility means that organizations can adapt their quality control procedures to arising organizational and industrial demands. For example, with cloud computing, an enterprise can expand its quality management portfolio to include suppliers and customers. 

Concerning scalability, organizations tend to expand their quality management infrastructure as they grow over time. With cloud computing at hand, growing organizations do not need to develop their quality management resources, such as server rooms, to host data and analytical operations. Whatever implements required are found in the cloud and can be seamlessly acquired. 

On a different note, the cloud promises better security in terms of who, where, and when organizational data is accessed. The technology in tools such as Veeva quality management software systems proactively implements control measures to guard against security breaches on company data.   

New ways of utilizing big data to gain competitive edges-better data analytics tools

Companies can now capture a large pool of data about their customers, operations, and suppliers. To take advantage of the increasingly valuable asset in quality management, organizations need quality experts and tools, which know how to make meaning out of big data. 

Technologies are introducing various tools to aid with collecting, analyzing, and interpreting big data. These include statistical sampling programmes, control and Pareto charts, experimental models, and much more. 

Not to forget are the real-time data access tools. These tools make it possible for companies to proactively monitor and review issues that compromise the quality of goods and service delivery. 

Secure, decentralized, and tamper-proof quality assurance pieces of evidence

Blockchain technology was first introduced as a record-keeping interface for the pioneer cryptocurrency, Bitcoin. The technology facilitates a decentralized, secure, and tamper-proof way of verifying transactions. 

Though still at its infancy stages, especially in quality management, the blockchain can be utilized for registering processes involved in collecting, storing, and processing of quality management data. This will ensure truthfulness is sealed at all quality management levels used by an organization. 

Well monitored and controlled processes courtesy of AI technologies

Since time immemorial, processes such as monitoring the quality of goods and services delivered have been applying various statistical methods under the supervision of human beings. This is about to change as artificial intelligence technologies are currently experimenting and implementing complex processes in quality management through the use of artificial tools such as neural networks. 

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