What is the smart factory and its implications for manufacturing?

What is the smart factory and its implications for manufacturing?

The so-called ‘smart factory’ has gained plenty of traction in recent years. It’s been written about in tones of awe, as a ‘paradise of efficiency where defect and downtime, waste and waiting are long forgotten issues of a long-forgotten age’.

Sounds compelling enough, right? But what does a smart factory actually entail, and what are its practical implications for the manufacturing process?

Automation, self-optimisation, predictive maintenance, asset sweating – all these are processes that are associated with the smart factory. The exact combination of processes at play in a particular environment will, of course, depend on the demands and priorities ofthat environment.

However, at its core, every smart factory can be understood as one where previously unconnected devices and machinery – the hefty hardware that makes up a factory floor – are joined together via the Internet of Things (IoT), opening up a raft of possibilities for information-sharing, intelligent analytics, and process enhancement. Practically, this means embedding networks of sensors throughout the factory floor or integrating to existing control systems, collecting information ranging from temperature and vibration levels to production speed and levels of consumables such as oil or water. Then, this data is transmitted to a central repository where analytics engines can be used to generate both immediate and long term insight.

From insight to action

Crucially, those insights are then used to inform actions – anything from simply replenishing the lubricants to adjusting the entire staff schedule so as to operate as efficiently as possible. And it is this stage that has the most powerful implications for manufacturing.

Suddenly, factory managers are able to make decisions based on tangible data, from right inside their own walls and processes, rather than relying on experience and intuition. They can take short-term action to rectify small problems before they escalate in costly ways, and they can take longer-term action to optimise processes and try out new approaches.

The smart factory, then, can be understood as consisting of three discrete processes: data collection, data analysis, and subsequent process enhancement – and it is stage three that makes the smart factory truly transformative for manufacturing.

From action to enhancement

Such process enhancements might be to do with the hardware on the factory floor in the form of asset sweating. That is, increasing throughput and maximising the useful lifespan of machines by carrying out maintenance at the most efficient time, and rectifying small problems before they escalate in a damaging way. This is achieved because the smart factory can automatically measure and analyse key statistics, such as the number of revolutions a particular machine has made, or levels of internal wear and tear, which would otherwise have to be assessed manually.

Alternatively, process enhancements might be in the realm of staff efficiency, by automating previously manual checks and freeing up resource to carry out more strategic work. The smart factory can ensure that maintenance and engineering tasks happen only when necessary, and at the most appropriate time of the day or week.

Then there’s the manufacturing process itself. The smart factory can automatically identify bottlenecks and other process inefficiencies, and make calculated suggestions as to how production lines might best be arranged. The smart factory, too, can better integrate the production line with product design, engineering and planning technologies, generating new insights between previously disparate teams and processes, and powering innovation and new and exciting ways. 

The practical possibilities for how smart factories are implemented are nearly endless. But at their core, they are very simply about accessing the data that already exist within every manufacturing environment, consolidating and analysing those data sets, and using them to inform real business decisions.

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