The evolving vision of a smart factory

Our current perception of the “Smart Factory” is deeply intertwined with advanced technologies like deep learning and AI. This is a stark contrast to the concept as it was envisaged fifteen years ago when the idea of the Smart Factory first took shape. Mark Clemens, Connectivity Architect and Security Strategist at COPA-DATA, delves into the evolution of this vision.

The smart factory: an evolving definition

The term “Smart Factory” lacks a global consensus on its definition. Furthermore, what we refer to as “the factory of the future” today will likely differ from the concept in ten years. This fluid definition is only one aspect of the complexity. The term also implies a final state, suggesting no further improvement is possible. Yet, it is probable that a Smart Factory built a decade ago has advanced, thanks to additional investment and more effective use of available information. Therefore, the term “Smarter Factory” is a more fitting description.

Towards a smarter factory

A Smarter Factory is achievable through incremental steps, allowing for continuous learning and improvement. This approach can be applied to both existing and new factories, irrespective of the existing level of technology, automation, or data collection. Establishing a strategy for a Smarter Factory, which sets achievable small goals, is crucial. This strategy should be iterative and agile, involving people from various domains from the outset, particularly when some job roles might become redundant due to technological advancements.

Practical steps to achieving a smarter factory

For example, a company aiming for net zero emissions and transitioning from fossil fuels to electrification faces challenges such as grid capacity limitations. Local generation, combined with storage solutions, becomes a viable option if the company understands its energy consumption patterns and identifies opportunities for peak reduction. This effort aligns with a Smarter Factory strategy, beginning with harnessing information that may not be immediately available.

Making smarter use of existing information

Instead of installing new energy meters on every piece of equipment, which would interrupt production, it’s more efficient to use existing sensors for alternate purposes in PLC programs. Maintenance staff and system integrators, equipped with networking expertise, can assist in identifying the most efficient and feasible locations for new measuring devices. IT support is essential for managing data repositories and ensuring cybersecurity.

Data structuring and harmonisation

While avoiding buzzwords, the concept of edge computing is introduced. Data can be collected from numerous energy meters and devices at speeds as fast as 100 milliseconds, leading to large data volumes. However, the value of data, like gold, fluctuates. For instance, energy consumption data is more valuable during an energy crisis and less so when energy prices are low. Data consistency and standardisation are challenges, especially when energy meters need replacement, affecting downstream logic and reporting.

Deriving real value from data

Ideally, data should be refined for downstream processes such as visualisation, reporting, and decision-making. These tasks can be efficiently performed at ‘the edge’, near the production site. COPA-DATA’s software platform, zenon, facilitates the provision of qualified, timestamped, harmonised, aggregated, consistent, and context-related data, especially with its latest version compatible with Linux.

The future’s smarter factories

While a fully autonomous, self-optimising, self-maintaining, and self-healing factory might seem like science fiction today, the future demands even smarter factories. Envision facilities operating in extreme environments, such as the deep sea or the moon, producing essential resources like oxygen or hydrogen. These future Smart Factories present formidable challenges that go beyond our current understanding.

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