Your business doesn’t just ‘make things’ anymore.
Today, industrial companies like yours also have a substantial design phase and service phase, which provides opportunities to innovate and generate recurring revenue. And across this extended value chain, digital technologies are playing centre stage.
But how can you test the potential impact of new technologies, and then scale them? To help answer that question, and more, we spoke with Thomas Benoist, Expleo’s Head of Digital Sales.
In this wide-ranging discussion, Thomas also explains how Expleo has helped industrialise technology for some of its global clients — and the significant benefits they’ve experienced as a result.
Do your customers view industrialising technology with confidence, or as something risky?
It really depends on the customer. Some larger companies like to evaluate new technologies in quite an experimental way. They’ll carry out Proof of Value and apply a win or fail fast approach to quickly identify if a new technology is worth further investment.
But some customers have a more risk-averse mindset, and they might need us to offer them some guidance and support. That could involve explaining why a solution won’t work 100% on day one, such as a hyperautomation system that will speed things up long-term, but needs some initial trial and error.
That can feel frustrating because the customer wants to get going. And they might not really understand how a particular technology works. So in these situations, we need to really support them through the implementation process.
Right now, it feels like everyone is fixated with the potential of AI. Can you give an example of how Expleo has industrialised AI to help a customer solve a very specific problem?
A good example is a project we worked on with a global ophthalmic lens company. This company produces over 400 million lenses a year. Their lenses are expensive and delicate, and they needed a way to improve their quality control process.
We developed an automated system, based on deep learning, that analysed an example of a perfectly produced lens from their production line. So it knew how to identify what a defective product looked like.
The system has been a real success and can accurately identify 87% of defects. But more than that, it’s significantly reduced the workload of our customer’s quality control operators.
Industrial businesses are now heavily focused on design and service — not just manufacturing. How are their operating environments changing, and what does this mean when it comes to industrialising technology?
We’re moving towards the digitalisation of the factory floor. Companies are connecting IoT-enabled machines and production lines with AI and the cloud. And that means they can collect huge volumes of data to support new use cases, such as predictive maintenance and digital twin.
We’re also seeing the use of technologies, such as augmented reality, automation, and robotics — all of which means an even greater dependence on digital.
A big challenge with all of this is defining how these investments improve performance. When you industrialise a new technology, can you ensure it’ll deliver a good return on investment? You need to be clear about how a technology will work across your business and deliver real value with pragmatism.
How does Expleo’s combination of engineering and digital expertise help customers overcome industrialisation across the design/manufacture/service journey?
The way we recruit is key to this. We don’t just hire people from business schools; we also hire mathematicians, computer scientists, and other roles in academia. Once we equip them with engineering skills, they can really make digital work in the industrial space. Which is hugely beneficial to our customers.
As an example, we helped a global automaker develop a new predictive maintenance service, which they wanted to deploy for onboard chargers and electric vehicle batteries. They were sending a lot of vehicle-related data to the cloud and asked us to help them use it to predict breakdowns.
We created a solution using machine learning that could predict if (and when) a battery or on-board charger was going to fail — with 99% accuracy. In this example, we were able to help a company fast-track innovation and develop a solution using the data already in their business.
Are certain industries more advanced in their understanding of how to pilot a technology and then scale it? And are there best practices you’d recommend?
There are a few fundamentals for every industry. One of them is, when testing a new technology, we want to generally avoid Proof of Concept and focus more on Proof of Value using direct data. That way, we can demonstrate that a solution is going to have a measurable, practical impact.
When you’re implementing a solution, you also need to know where you want to go. How and where do you want to deploy it into your business? What improvements are you looking for? How does it fit into your global roadmap?
These are all important questions to ask early on. And you need to have a pragmatic approach. When you’re scaling a new technology, it’s important to take small steps and not move too far too fast.
Industrialise technology at the scale you need.
At Expleo, we’re not in the business of just providing blue-sky thinking that will never be implemented. We make sure we understand the practical, day-to-day realities of how your business operates.
Once we know what’s happening, we can start to build a roadmap that helps us solve a particular problem, right now.