Artificial Intelligence – The Future of Manufacturing

The future of manufacturing – indeed the future of most industries – is becoming increasingly automated. Many rote tasks are now being performed by machines and artificial intelligence (AI) with human oversight, and many of the applications that will be needed to manage production in the future have not yet been developed or even imagined.

Operations and plant managers, when thinking about making efficiencies and chasing profitability, would do well to consider the bigger picture and make strategic decisions that could future-proof the entire organisation instead of fixing short-term problems. Ultimately, this will lead to a streamlined, more profitable business.

This holistic approach can take many forms – it goes far beyond merely upgrading existing technology and instead identifies and implements new sources of automation-enabled, sustainable business value. It is essentially cost-effective modernisation – in addition to boosting revenue through improved execution of business strategy, it can reduce overall modernisation costs by up to 10 percent over haphazard piecemeal approaches.

Below are some examples of the types of applications that can make the process plant of the future available – and affordable – today:

Server virtualisation, which allows the user to consolidate many PCs and servers into a high‑availability virtual host server, reducing heat load, weight, and power consumption, as well as improving maintenance efficiency and hence reducing total cost of ownership associated with maintaining many computers.

Workflow automation software, which, for example, might store and enforce a sequence of proven procedures by which a plant worker might respond to an alarmed incident or event, notifying all affected parties of status and progress in real time.

Real‑time energy management systems, in which profitability based on consumption in energy-intensive operations is monitored in real time, in the context of dynamic energy markets.

Real-time online modelling, in which, for example, every bit of raw material that comes into a process is tracked, measured, and compared to output with analysis of variance pointing to process variances.

3D virtual reality simulation systems, in which, for example, workers can train on handling hazardous situations in a realistic virtual situation much like a pilot trains with a flight simulator.

Controlled combustion, where advanced process control software monitors the firing of boilers and other equipment, and adjusts in real time to minimise excess O2, CO, and NOx emissions.