The most significant change since the Industrial Revolution

The future looks bright and exciting for the industries of tomorrow. But the future starts today since most of the technologies mentioned above are already available. So what are you waiting, jump on the bandwagon of change and prepare your organisation for the challenges of the future!

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The use of Artificial Intelligence (AI) in Industry 4.0 has been baking for quite a while, but technology is now ripe enough to turn it into reality. It involves the infusion of data and smart automation within the manufacturing process together with human input. To do so, it makes use of various technologies such as the Internet of Things, Cloud Computing, Blockchain and others. Let us try for a second to understand why the use of AI is important.

If you are the owner of a manufacturing plant, then you’re probably familiar with having various machines working in tandem and producing thousands of devices every day. Monitoring the different processes which go on simultaneously is already a huge headache. Most probably, you are at the mercy of the various operators in order to ensure that the level of production is at least maintained. How- ever, even though you are taking the best possible measures to mitigate any risks and ensure that everything runs smoothly, things still go wrong. When they do, havoc breaks loose until the main issues are sorted out. Things get further complicated when problems arise during a weekend or a public holiday since the availability of skilled professionals is very limited and costly.

When production is halted, it obviously results in financial losses which might also impact production deadlines. Large production processes would normally depend on warehouses of raw materials, parts or other items. All of these cost a lot of money and involve huge risks if they are mismanaged. The handling of these assets alone is already a huge headache for management. And we haven’t even started considering how to improve the plant; maybe through optimisation or changing the basic configuration. But sometimes, when dealing with legacy systems, any change can be catastrophic and as such, management might be slow to respond towards the changing needs of the market. Sometimes, it is simply too late and the lack of decisive action leads the plant towards certain doom.

Do these scenarios sound too familiar? Well, the good thing is that most of them can be handled quite easily with today’s technologies. To understand what we mean, let us give you some examples.

Micro-monitoring at a distance

Consider managing a plant with hundreds of machines, working like clockwork. To complicate matters, let’s imagine that there is more than one plant distributed around the world and located in different time-zones. How can you manage all of that?

The answer is that one can’t micromanage it! You’ll have people to do that, especially when the numbers start growing. But we all know that people are not infallible. The solution to that is to install an Intelligent Digital Twin (IDT) system capable of monitoring all of your machines with 24/7 constant coverage, irrespective of where they are physically located. You can forget about it and let the system take care of the issues which arise, whilst only alerting you when your attention is really needed. Furthermore, it will give you the peace-of-mind which you need since you can zoom into the inner workings of any machine when you want and in real-time.

Optimising your plant

Plant optimisation and improvement is a big game-changer. However, it is frequently ignored because as the saying goes, “if it ain’t broke, don’t fix it”.

But how true is that? Did anyone ever try calculating the opportunity loss of doing things in a different way? Most probably, it never happens because normally, you would have to rely on experts to drill down to that information.

With AI, this scenario changes because the system actually performs these calculations without needing to be prompted by anyone. It will constantly analyse the supply chain and quickly simulate the ideal and most efficient scenario for any particular job. It does so by looking at the current production, predicts potential downtime, slot in new tasks in the pipeline and make suggestions based upon deadlines such as delivery targets, the material available, etc.

Predicting the future

Predicting the future is not something easy but it is a very desirable feature especially when one considers that issues such as machine downtime, is one of the largest contributors to loss of production. In fact, 80% of companies are unable to calculate their true downtime costs. On average, companies record 4 hours of downtime daily!

Many have tried to tackle this feat but very few predictions can really survive the test of time. However, when dealing with a closed system, past events can give a very good indication of what will happen in the future. Machines in a manufacturing plant can be considered as a closed system which has been running for months and in some cases, for years. Being mechanical in nature, they constantly log heaps of precious diagnostic data which is probably lying in some database just in case the engineers need it. In reality, sifting through all that data is rather painful, precisely because of its volume, so people seldom refer to it.

However, AI systems excel at processing huge volumes of data and analysing it. Specifically, they find it rather easy to locate patterns and these patterns can give a good indication of what is going to happen. Hence, an AI system can easily make predictions on when the machine is going to fail and for what reason. This will allow the plant to organise prescriptive maintenance which shifts the management’s strict dependence on planned maintenance, to being able to take real-time action based upon actual events.

Furthermore, the system will not only predict a potential failure but it will also help management identify the root cause of the issue. Thus, some of the failures might be eliminated from the production cycle once and for all, whilst others will be handled in a timely manner. In so doing, unplanned downtime can be heavily reduced or even eliminate in some cases, thus maximising profitability and equipment reliability.

A factory made of Lego

In an ideal world, one would gather the machines and move them around until the right configuration is found. This would minimise costs and optimise the available space. Furthermore, when the plant starts growing and new machines are added, different configurations might be beneficial. In reality, this does not happen because it takes a lot of time and effort to do so, probably so much that it outweighs the benefits.

However, with an IDT system, this can be achieved rather easily. An IDT system already has a virtual replica of all the machines and their status is updated in realtime. Changing the configuration of a system thus becomes child’s play since moving virtual machines and changing configurations is similar like playing with Lego. Machines can be added or removed and their output can be easily simulated using the historical information which the system already possesses. Thus, different trials can be set up, either by the managers or by the AI system controlling the plant, in order to find out the best configuration possible. Once done, the value of such a configuration is assessed and if it outweighs the costs, it can be deployed in the physical system on the factory floor.

Asset-management made easy

Managing all the assets in an organisation is a big headache especially in plants having hundreds, if not thousands, of machines working simultaneously. Because of this, predictions become even more valuable, especially when managing the purchasing of spare parts in order to replace defective components or when planning for forthcoming machine reconfigurations.

All the components, both the purchased ones and those produced through the manufacturing process can be tracked through their unique identifier. Some of them will be tagged using technologies such as Radio Frequency IDs (RFIDs) which seamlessly provide information to the central system with regards to the physical movement of components. Thus, with the click-of-a- button, the person managing the system will have an up-to-date overview of all the physical stock available in the plant at any one point in time. Long gone are the days of never-ending stocktaking. The system will provide additional information such as products on order or those in transit. Furthermore, if the tracking module is built upon a blockchain system, one can ensure full traceability for any product; starting from the raw materials which make up the product, up to the end of the product’s lifecycle in the recycling stage.

Better decision making

IDT systems will be there to assist management in the provision of timely information, in the sifting through the sea of data and in the creation of summarised viewpoints over the operations of the various plants. Without doubt, they will become the personal assistants of management capable of not only giving accurate snapshots but of also highlighting areas of concern in real-time.

Furthermore, the time will come when the management of the organisation will be entrusted in the hands of the AI system thus automating the entire decision-making process. Decisions will be taken at the speed of light and corrective actions will be dispatched as soon as something happens. Managers will be relieved from the day-to-day micromanagement, they can look at the plant from the macro perspective and spend more time on planning future improvements in line with the direction imparted by the board of directors.

Conclusion

The future looks bright and exciting for the industries of tomorrow. But the future starts today since most of the technologies mentioned above are already available. So what are you waiting, jump on the bandwagon of change and prepare your organisation for the challenges of the future!

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