Industry 1.0 is the starting point of the modern manufacturing industry. Within life sciences, movement from laboratory-scale to wholesale production fueled the establishment of the industry in the 19th century.

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The second industrial revolution was enabled by electricity and electronic machines and assembly lines. These had basic pre-set controls that provided basic automation and process controls. However, these process controls were limited to pre-determined and static settings which only allowed for the monitoring of process performance and passive control strategies. It can be argued that much of the current manufacturing industry still operates using the Industry 2.0 paradigm.

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The third industrial revolution was enabled by the emergence of computers and communication technologies. These technologies allowed for a greater level of automation and monitoring which enabled concepts such as continuous manufacturing and active control. Human-computer interfaces have aided in developing more sophisticated control strategies which in turn has resulted in higher product and process quality. With Industry 3.0 came advanced process analytical technology, or PAT, which aims to provide process and product quality data in near real time. It also advanced model-based or Quality by Design, QbD, which aims to control target product quality profiles within a defined set of quality parameters. However, to achieve the full potential of these, more technological advancements are required to attain deeper process knowledge and real-time analytics to better enable real-time release testing with high levels of quality assurance.

 

The fourth industrial revolution brings together the advanced manufacturing technologies that enable integrated, autonomous and self-organising manufacturing systems that can operate independent of human involvement. Whereas Industry 3.0 introduced an automated digital environment to the plant floor, Industry 4.0 promises advancements of the entire manufacturing process and infrastructure. Performance data can be analysed by machine learning models and used for critical real-time business and operational decisions. The journey from data collections to full digital maturity involves transformation of the raw data captured from a manufacturing process, to information gained from analysis of this data, to knowledge gained from the addition of contextual meaning, through the use of artificial intelligence, to information that allows for informed decision making to be performed.