Industry 4.0 looks like a political campaign: lots of promises, scaremongering and people claiming everything wrong will be solved in the next four years.
I recently saw a diagram that tried to explain all the trends in Industry 4.0, which is commonly referred to as the fourth industrial revolution. The diagram’s sole purpose, it seemed, was to confuse and scare people into believing the hype.
Now, don’t get me wrong. Industry 4.0 is about to bring significant change. It’s 100 per cent coming. But it’s 100 per cent not complicated. I don’t remember, nor does my research show, that before any of the earlier industrial revolutions, we had people running around warning us. In fact, I believe the last time this happened was when the millennium bug was predicted, and look what happened then: nothing.
It’s the quiet people, who can see through the noise, that we should be seeking out and listening to – like the characters from The Big Short or the stats men starring in Moneyball. They all had the foresight to see through the complex to the simple. To them, it was never that complicated, with the overruling message being: stick to facts and don’t waver in your simple beliefs.
A lot of the hype is old news. Nobody can tell me automation is industry 4.0: 3D printers are just a smarter manufacturing methodology, cobots (collaborative robots) are part of an evolution in progress for the past 30 years, and talking computers are no more than talking computers.
Stand back for a minute from the ‘internet of everything’ and you’ll realise this could also be known by another term: planet Earth. All that’s different is accessibility to information.
When we realise this, we should also understand that when historians look back on this time of change, only data, algorithms and quants (quantitative analysts) will be regarded as important.
The challenge is no longer about being able to measure and access data. It’s about how to identify the information required to support optimal decision making.
A digital twin is a virtual representation of a product, part, system, process or network that allows you to see how it will perform, sometimes even before it exists. It can allow businesses to peer into the future without consequence, using decision analytics from the now to drive decisions.
With enhanced computing power, the best digital twin models use stochastic modelling, which essentially allows performance of all elements of the model to perform based on probability, accurately predicting scenarios where specific events will vary in performance. These digital twins will not only model scenarios but also determine the risk and probability of success based on replicating the future 100,000 times over (or however many you need to feel confident).
A tool this powerful can unlock significant benefits within your supply chain and be used in many different forms, depending on the level of adoption.
In its most basic form, a digital-twin model can be used to improve decision-making capability within a business to test scenarios. For example, instantly identifying the optimum production sequence following a critical failure to protect customer service or cost.
It can also be used for:
- Virtual commissioning: model new capital investments, diagnose financial performance and map detailed financials ahead of money being spent.
- Planning complexity: instantaneously adapt plans to variables that exist in the real world, driving optimised performance against key metrics. The ability to plan disparate but dependent resources using stochastic performance.
- Online diagnostics: running the digital twin in parallel with the real machine can provide valuable insight into where a problem might arise as the machine’s response drifts from the model as it ages.
- Production sensitivity analysis: scenario modelling allows the ability to model unforeseeable impacts on complex production and understand the true cost of failure (processing versus pack).
- Network optimisation: complex models become simple objects allowing full end-to-end modelling, optimisation and simulation of an entire network using stochastic probability both inside and outside the factory
Airbnb, Uber and Amazon are all platforms that enable collaboration. They shortcut markets and utilise underutilised assets. In essence they’re not revolutionary, just really smart. In industry, if you can run your capital assets for longer, they gain more value and you become more profitable, like using your car for Uber, or home for Airbnb. If you procure from a broker, and with scale you can go direct to the manufacturer, that incremental margin becomes your own. Amazon facilitated this.
Several other companies in small niches are providing the same service. Airtasker connects directly with resources and Expert360 with consultants. A little favourite of mine is Snooper, a business that completes store audits in the retail world by getting shoppers who are already heading to the store to collect the data required, as opposed to an expensive network that includes transport and travel time.
But collaboration is yet to peak, especially in the industrial world, both for data and physical sharing of assets.
So, get yourself some quants, a really powerful computer, a digital twin, and don’t be afraid to collaborate. It might be you the historians refer to in future.
Paul Eastwood, Director; Pollen Consulting Group
First published in Retail World: