What are the business and security impacts of Industry 4.0?


Industry 4.0 will make manufacturing more efficient and productive. By optimising factories, it will directly improve yield. On the product side, it will also extract greater value from data for usage-based design and mass customisation, which in turn will open the way to new markets. On many levels, it will completely change the business model to an outcome-based approach.

Accenture estimates that automation, connectivity and embedded software can increase production line productivity by up to 30%. The shift from selling products to selling measurable outcomes will redefine whole industry structures. This is the shift to servitisation, whereby companies are using the Internet of Things (IoT) to find new ways to grow revenue and increase profits.

Industrial equipment manufacturers sell outcomes, like machine hours or price-per-user, rather than just products. For the customer it means less disruption, increased uptime, incremented factory yield and ultimately higher satisfaction.

>See also: How Industry 4.0 is changing human-technology interaction

On the marketing level, mass customisation and the interconnectivity of the products will open up new markets. On the level of monitoring and operational efficiency, sensors can enable organisations to monitor assets, from elevators to shipping containers, in near real time – analysing data still in motion, not at rest – for predictive failure prevention.

Manufacturers can remotely monitor the condition of equipment and look for indicators of imminent failure – for example, vibration, temperature, or pressure outside normal limits. This means that the manufacturer can make fewer visits, reducing costs and freeing up employees.

What are the long-term impacts of Industry 4.0?

McKinsey Global Institute estimates that the value of the IoT will be $11.1 trillion per year in 2025, and the potential value of IoT applications in factory settings of between $1.2 trillion to $3.7 trillion per year in 2025.

This figure is computed as a function of operations optimisation, predictive maintenance, inventory optimisation, health and safety, human productivity (via augmented reality, monitoring and process redesign), usage-based design of factory machines and logistics optimisation, and more.

The extent of the long term impact is dependent on “enablers”. Certain conditions must pre-exist for the full economic benefits to be realised along the manufacturing landscape, including sensor-based monitoring environments, even when that means IoT-enabling legacy machinery. Short and long-range connectivity networks with sufficient reliability are the improvements in data analytics and the prescriptive actionability – not just the data itself.

Realising the benefits of IoT-based systems in factories must depend on improvements in the analytics. Real-time event processing systems exist today that monitor and work on the flow of real-time data points from many machines.

That being said, in today’s environment, little of the data generated by the machine park in the factory is actually used for decision making. Long-term impacts will not be realised if manufacturers cannot extract the value from the data.

If industrial IoT data points remain in siloed systems, manufacturing organisations, or if different departments do not have the incentive to collaborate, the long-term promise of value will not be realised.

Companies cannot improve the quality of their products if performance data collected by the field technicians department is not shared freely with the R&D or product group. Obstacles for data sharing need to be overcome if IoT impact is to be maximised.

Secondly, most of the value extractable from data will be automated – think algorithms, advanced analytics and some level of artificial decisioning. But machines won’t entirely be able to fill the gap. They may make the right decisioning with a confidence value of 80%, but that still leaves 20% of false positives. In data decisioning, human data assessment is still needed – human reasoning is still superior to machine decisioning.

As the volume, variety and velocity of data increases as the IoT ecosystems expand, businesses need to re-examine the way they visualise data, make it more humanly intuitive and less like the same hard-coded, legacy PC era charts and graphs appearing again and again.

Analysts advocate natural interfaces as one of the innovation accelerators in third platform theory. Manufacturers need to consider a more natural way of visualising data for more humanly intuitive data decisioning if they are to advance further into Industry 4.0.

Is Industry 4.0 likely to be replaced by other movements?

In terms of this trend affecting the manufacturing landscape, then yes maybe in time Industry 4.0 may be replaced. But, it won’t bee easily replaced. The analogy of the Fourth Industrial Revolution is an amalgamism of the collection of technical advances that are driving industrial innovation such as big data and advanced analytics, the IoT, servitisation, digital modelling, additive manufacturing and computer integrated manufacturing.

It is not inconceivable that Industry 4.0 as a definition becomes fused with third platform terminology, which has identified big data, social, mobile and cloud as the foundational innovation drivers for about the past decade.

Evidently, there is a good deal of overlap. Analysts such as Gartner and IDC predict that virtually all of enterprises’ new strategic IT investments through 2020 will be built on third platform technologies and solutions. So it is conceivable that the term Industry 4.0 may be usurped by more frequent references to the third platform.

>See also: Why cloud technology is central to Industry 4.0

What security challenges still face Industry 4.0?

There are barriers and enablers on the road to realising the benefits of Industry 4.0. If that value is to be realised, security and privacy issues will have to be addressed.

Companies need data about how factory-made goods are used by customers – to correct design flaws or usage-based design, for example – and consumers will have to trust that the manufacturer is maintaining strict data security.

Data confidentiality and security will affect IoT adoption. To gather data for usage-based design improvements, manufacturers need access to data about how their customers are using products.

Confidentiality will arise since a manufacturer is likely to consider specific details about factory performance as confidential. This concern needs to be overcome if the IoT impact is to be materialised.


Sourced from Luke Allen, business development and sales manager, dizmo.com

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...