The factory of the future is not a low-cost proof-of-concept — but proof-of concept is possible

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Manufacturers are not like Steve Jobs. He famously didn’t worry about market research when developing the iPod, merely assuming — some would say arrogantly — that the product was so good that everyone would buy it. He was right, but few would have his confidence.

Manufacturers, by contrast, tend to be somewhat unenthusiastic about improvement ideas that are not rooted in reality. They are often designed to solve already-identified problems facing them or their customers. They want proofs-of-concept, with practical applications, and evidence that the solution will work. All of this makes developing the factory of the future somewhat tricky. If there is one thing that everyone agrees on, it is that the future looks quite different from the past.

Moving on from incremental improvements

Manufacturing has, in the past at least, tended to advance by incremental improvements. A tweak here, an amendment there, all quietly building together to make a better whole. This approach has served the sector well, but it has tended to result in a particular mindset developing. Managers in manufacturing tend to be logical, good at routine and getting things done, but perhaps not radical thinkers.

How, then, can radical thinkers prove that Industry 4.0 — the factory of the future — will work? How can proof-of-concept be demonstrated clearly in this kind of environment, when the concept is not really suitable for small use cases or incremental improvements? The answer, in my opinion, lies with information already present in many factories: The knowledge of the people employed in the organisation, the processes it uses, and data from the rapidly-expanding range of equipment connected to the Internet of Things (IoT).

Prediction and optimisation

The most important elements lie in two main areas. First, the nature of the data that is available. Once upon a time, manufacturers had to use historical data. Now, however, data are available in real time, and can even be used in predictive models to identify problems that are likely to arise in the future. This, for example, means that maintenance can be planned more effectively based on more than knowledge about the lifetime of parts. Instead, scheduling draws on data about the wear and tear on actual components currently in the machinery. This means it is possible, in principle, to avoid unscheduled down-time and stoppages.

The second area is the tools that are available to manage and analyse the data that are emerging. It’s particularly the new generation of advanced analytics tools that enable the processing of streaming data. For example, edge computing technology ensures that data processing happens as close as possible to the entity that has generated the data. This is the reverse of the push towards storing everything in the cloud. The (millions and millions of) events can be analysed directly at the coalface, as it were. It becomes possible to detect, for instance, extreme values in a system that may affect its operations, and make decisions about what to do very rapidly. The data can then be filtered and made available to central systems for aggregation and subsequent activity if necessary.

Measure concepts

Analytics can also make it easier to measure concepts of quality such as overall equipment effectiveness (OEE). This is a combination of availability of the machinery, its performance, and the quality of its production. A reduction in any of these can result in lower OEE. Analytic techniques enable deeper digging into the data from the machine to identify the reasons for the level of OEE. This, in turn, enables factories to maximise production more easily, by targeting the areas that are causing the problem. With many people arguing that quality is the only answer to manufacturing challenges, this is an important development.

A proof-of-concept for Industry 4.0

Commentators have suggested — and I would agree — that being able to use digital tools effectively to manage production machinery, processes and systems is, in fact, a very good proof-of-concept for Industry 4.0. Advanced analytics can enable manufacturing managers to work strategically to improve problem areas: a much more targeted approach that will inevitably pay off better than the previous, more scattergun techniques. To this, of course, is added the move from historical to real-time data that is an inevitable result of introducing more sensors connected to the IoT.

It should, therefore, be clear that the factory of the future may not actually need a proof-of-concept. It is, in fact, already here. All that is necessary is to put the jigsaw pieces together in the right pattern, and the proof is there.

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About Author

Samuel Blanquet

Pre Sales Senior Consultant – Manufacturing Leader Samuel joined SAS in June 2016 after a period working on product lifecycle management, customer relationship management and pre sales for other companies. His work has generally involved strategic development, and his roles have been largely customer-facing. Samuel’s focus is firmly on helping his customers to understand the business issues they face, which in the past have been particularly those connected to product lifecycle management, and enable them to introduce appropriate solutions to manage these issues.
Samuel’s current work builds on his previous experience in both pre sales and manufacturing. He now provides sales and pre sales support on targeted major manufacturing accounts for SAS. His strategic development work focuses on developing business value for his customers, including considering how analytics can be used to help manufacturers obtain value from data from connected equipment and sensors to enhance productivity and enrich their business model.

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