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Business Technology Optimization

Demystifying Industry 4.0

This is a contributed article by Vincent Champain, General Manager GE Digital EU Foundry

 

Industry 4.0 is sometimes seen as a catch-all term used in the media to describe industrial productivity improvements, prompted by technological advancements. The news agenda is currently full of buzz around AI, IoT or data analytics – essential parts of creating the Industry 4.0. Questions are being asked about the use of artificial intelligence and its impact on businesses all over the world. The lack of answers, in turn, is often fuelling misconceptions about the nature of the new era of industries, its capabilities and challenges. In order to fully embrace the wave of changes, it is important to explain and further understand some of these myths that are currently prevailing.

The first myth is the idea of “artificial intelligence in a box” which would simply require one to supply data without knowing what it means. In practice, AI needs to be combined with appropriate industrial expertise and a physical model of the machines. While achieving 90% of suitable purchasing recommendations is a good performance for a bookstore, one mistake in analysing aircraft data would have catastrophic consequences. The truth is that artificial intelligence still takes a great deal of human intelligence and a lot of “exact science” to be effective. Regardless of how much data insight one has, one should not underestimate the value of traditional skills in material science, chemistry or process optimization. In fact, those traditional skills will probably continue to account for 90% of the added value: to be competitive, industrial companies will need to be digital AND industrial.

Another misconception is that you just need to accumulate data to obtain profitable insights. In fact, industrial margins are thin, and the volume of industrial data is much higher than in the consumer domain. For example, one plane flight generates as much data as Twitter generates globally in one week. To get a guaranteed and reasonable return on investment, one should instead start with identifying the tangible improvements and then extract the relevant information that helps them achieve these goals. Edge computing is a good example of how analytics could be used to process sensor data in the asset itself and extract real-time insights which are not managed from a central cloud storage facility before transmitting a reduced volume of data to the cloud

And last but not least the importance of the human factor and the appropriation of technology is too often underestimated. Many air crashes happen with correct software and data, but under conditions (weather, fatigue or stress) where the pilot was having trouble absorbing and prioritizing all of the information coming at him to perform the right action.

Instead of being a substitute for human work, technology can help employees focus on value-added tasks while freeing them from the need to perform routine and tedious activities.

For instance, General Electric is using AI to inspect 1M kilometres of Oil & Gas pipelines for safety and defects every year. Over 15,000 reports detecting 100 million defects have been generated in the last 15 years. We are now using a machine learning application to analyse the new inspections and use the learnings that were gleaned from years of historical data to ensure the pipes are safe and prevent damages.

Another interesting example is how lean IT has evolved. For many of the “lean purists”, the focus has been on pushing back on digital tools and relying on pen and paper to “free” operators from the constrains of rigid manufacturing execution software. However, recently lean experts have started moving towards “digital lean” using more flexible and adaptable solutions that could give employees the flexibility that’s missing in older MES.

Many solutions fail to deliver the impact expected because of a poor design making them too slow or too complex to use for real-life operators under real-life conditions.

Only by combining digital innovation, with human skill and industry knowledge organisations will be able to fully take advantage of the 4th Industrial Revolution. This will require moving beyond Industry 4.0 to a wider vision of digital transformation where the Industrial Internet connects humans, data, assets and machines to enable a truly connected digital world.

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