Editor’s choice
Big data is transforming a variety of sectors, ushering them into the era of Industry 4.0. However, having access to raw data and knowing
what to do with it are at completely different ends of the digitalisation spectrum. To help manufacturers understand, and overcome, some of the challenges associated with smart manufacturing, Martin Thunman, CEO and co-founder of Crosser shares his insight.
B
efore we examine the challenges of digital manufacturing, let us reflect on industry’s journey. If we consider Industry 1.0 and
2.0 - when mechanisation and steam power, followed by the mass use of electrical power came into play - we are looking at a totally different era of manufacturing. Then the previous revolution, Industry 3.0, began to introduce automated production, IT systems and robotics to the factory floor. All the previous revolutions have a common connection: their technologies all produced data, of some description. But now, as we enter the Fourth Industrial Revolution, autonomous systems, the IoT and machine learning are equipping manufacturers with the ability to take this data, and make plants more productive, leaner and more cost-efficient.
POssIBIlITIEs Having access to data, and more importantly to data analytics, creates a wealth of use cases that can help manufacturers drive value across their business. The starting point for many businesses is getting hold of this data and sending it to a cloud system or data centre for analytics. The second use case involves factory floor integration, or taking this data and putting it to use. Indicators such as machine health can help form a work order that can be integrated into an enterprise resource planning (ERP) system,
equipment health and efficiency can be monitored within the DCS or SCADA system, or machine to machine integration can be used for production optimisation, as some examples. Industry 4.0 technologies are also a driver
for advanced automation. Moving beyond previous, more rigid systems of automation, new technologies allow machine to machine automation to take place, in a faster and much more data-driven manner. The fourth case involves understanding activities along a production line, and creating goals for a machine, process or a complete plant based on data insights. The final use case is the leveraging of machine data for processes beyond the factory floor. For example, supply chain, sales and finance could all benefit from data analytics - it is not all about machine health and operations on the shop floor.
THE CHallEngEs But there are challenges, and making these use cases a reality can seem difficult to realise - especially if an organisation does not have software developers onsite. According to
Actify.com, 33 per cent of all
data could be useful when analysed. However, companies only process 0.5 per cent of all data. By incorporating an enterprise data strategy, companies can ensure they are processing useful data and that time is not wasted on the rest.
However, knowing how to manage raw
machines can be difficult. It is important to think of a data plan as the foundation for success - hyped-up technologies like machine learning and artificial intelligence (AI) will come after. Manufacturers should follow three key principles when building a data strategy. Firstly, the strategy needs to be practical and easy to implement across the organisation. It also needs to be relevant and specifically tailored to the company’s goals as well as evolutionary and adaptable, to keep up with current trends. Finally, the strategy must be universally applied across the business and easy to update when necessary. The second challenge is complexity, which
comes in multiple layers. Legacy machines standing next to brand new robots, multiple generations of protocols and programmable logic controllers (PLCs), fragmented operational technology (OT) systems and segmented networks are just some of the drivers of complexity that create a large volume of varying data. How do you manage this data flood? Managing data is hard for many
manufacturers, but when combined with a lack of digital resources, there are further challenges to overcome. A report by the National Skills Coalition found that more than one-third of the manufacturers it surveyed have limited or no digital skills, while just 29
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August 2021 Instrumentation Monthly
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