November 2016 | Richard Young
John Richmond is chief engineer at Tata's group technology and innovation office. That means he spends his time looking for ways to help Tata create innovative products and services − and revolutionise its approach to manufacturing.
Are we seeing a transformation in manufacturing? What's been the impact of the surge in digital technologies?
The short answer is 'yes'. A lot of what we're seeing – in what is sometimes called 'Industrie 4.0' – is down to a combination of factors. These factors include: sensors and processors becoming cheaper, drawing less power, getting smaller, and delivering more processing and communications capabilities, dramatically reducing costs of data storage and processing; many forms of low-energy telemetry; and increasingly sophisticated machine-learning algorithms to understand and act on data that is being generated. Connecting up these sensors in manufacturing facilities is providing a new level of information about all aspects of the factory, and offers the ability to monitor and control them in real time; the whole factory becomes a connected entity.
What kind of developments have powered that breakthrough?
A really important factor has been deep learning and machine learning. These types of learning allow us to develop a better understanding of the complex interactions in a manufacturing process that wouldn't otherwise be obvious. Just as importantly, they're also helping us to control those processes more cleverly without constant human intervention. We can set parameters and allow the technology to optimise the process.
|'I can't think of a single innovation over the past 100 years that hasn't been the result of different technologies and disciplines cross-pollenating,' says John Richmond, chief engineer at the Group Technology and Innovation Office|
Can you give us an example?
Complex, multi-layered processes, such as steel manufacture, are an obvious application. There are so many interacting variables at each stage of manufacture, especially with speciality steels, so it can be hard to optimise and, where necessary, adapt the process parameters efficiently. Factors such as the temperature in a furnace, variation in the chemistry of the raw materials and the chemicals added to the melt, can be complicated and difficult to manage. Deep-learning systems enable us to manage these scenarios in an automated fashion and ensure that the plant runs as efficiently as possible, producing consistently high-quality products.
What about outside the factory?
Downstream of the manufacturing process, embedded sensors, telemetry and analytics provide real insights into the use of the product. It means we can get a new order of feedback. This helps to optimise product design and manufacture, and also changes the relationship with the end user.
For example, we can apply machine-learning technologies to identify failure modes for products, learning when and why things break down. This means we can tweak designs, or be proactive with users in the field, helping them to avoid experiencing failures, where previously it would not have been possible. Clearly this can make a big difference to brand perception, asset utilisation and cost.
So you get closer to the customer?
Absolutely. We're seeing manufacturers using the fact they can now understand the product and its use environment in real time, with this granular level of detail, to effectively offer a service, not just a product. This can result in changes to the business model – opening up avenues for different services, while at the same time lowering the total cost of ownership for the customer.
It sounds like you're blurring the lines of different technology applications.
I can't think of a single innovation over the past 100 years that hasn't been the result of different technologies and disciplines cross-pollenating. Today, we're seeing new materials, new processing technology and new communications protocols and systems – and the ability to link all these together in ways that we couldn't before.
What are the innovations that really excite you at the moment?
We're seeing some great developments in the wearable-computing field. Tata has a smartwatch that gives real-time data on elements of factory safety, such as a worker's body temperature and pulse rate. It is part of a system that includes aspects such as continuous monitoring of toxic gases in the air. That's just the first iteration: we can see huge applications in health and wellbeing – and we're having some interesting conversations via our relationships with Harvard University, Yale University and MIT on what is next.
The basic idea came from Tata Steel, whose workers will be using it soon. You can imagine how important it is to ensure a crane operator, working alone 30 metres up and moving giant crucibles of molten steel, is in good shape. The watch also had inputs from Tata Chemicals and Tata Motors; it is supported by Tata Consultancy Services' cloud-based analytics platform; it is designed by Tata Elxsi; and it will be manufactured by Titan.
Sounds like a real group-wide effort.
That's one of the great things about the Group Technology and Innovation Office (GTIO). We're always looking for opportunities across the different companies to build synergies and develop brand-new products and services.
How long does it take to realise the potential of these innovations?
It can take time. Take graphene: after its discovery, there was a huge amount of hype about its unique properties as an atom-thin material. We are now past the part of the cycle where, if you read the press, it seemed that graphene was the answer to everything. What is happening now is that the genuine opportunities are starting to be identified and developed – indeed some commercial opportunities are starting to appear. Tata companies were forward-looking and took the lead on some early applications. You might have seen Tata Steel has opened the Advanced Materials Research Center at the Indian Institute of Technology in Madras, and a significant part of the work there will be exploring applications of graphene in the Tata group. There is a realisation that developing real-world applications is much more difficult than developing samples of the material in the lab, but, with the right investment and patience, we can see a host of applications.
Gaze into your crystal ball – what's the next big thing?
The convergence of different disciplines is fascinating. Gopi [Dr Gopichand Katragadda, who leads the GTIO] set a challenge for next year's InnoVista competition, asking companies to look at the intersection of biology, computing and medicine. For example, new understanding of areas such as the gut microbiome (the community of microbes in our intestines thought to have a huge effect on health and even mental states) is opening up incredible new avenues for health interventions. We can get much smarter about maintaining health rather than just stepping in with drugs when it goes wrong.
I'm also excited by some of the new lightweight, super-strong materials in development, using graphene. Imagine a solar-powered, permanently airborne, unmanned aerial vehicle that manages data and voice traffic at a fraction of the cost of a satellite. There are some incredible innovations in the pipeline – and it's creative, multi-discipline thinking that will bring them to market.