Digital Acceleration and Future of Engineering

Key Takeaways

1. Selective Adoption of Robotics: While we are seeing many industries adopting robotics in different ways, the scale of that adoption varies widely. Factors such as labour costs, production volume, and task complexity all come into play when it comes time to decide where and how to implement robotic solutions.
2. IIoT and Predictive Maintenance: More and more, connected devices are being used for faster, more thorough data collection and analysis, enabling better predictive maintenance and improving efficiency.
3. Challenges in Technology Integration: The connectivity mentioned above, worries about software updates, data security and the integration of new technologies with legacy systems and traditional practices are all posing significant challenges in getting buy-in in some sectors. 
4. AI's Growing Impact: We discussed applications for AI in everything from object recognition in autonomous mowers to generative design in architecture. But adoption is gradual, seemingly focused on specific use cases, making its use feel more situational rather than transformative.
5. Human-AI Collaboration: As we have seen in previous sector discussions about the advancements in AI and robotics, human expertise remains crucial in engineering and construction, especially for complex decision-making and contextual understanding of data and processes.

For this latest issue of our Digital Products for Growth newsletter, we brought together a panel with diverse engineering backgrounds to discuss the impact of technologies like AI, robotics, and the Industrial Internet of Things (IIoT) on their particular businesses and how those technologies are transforming engineering disciplines generally. We found things that surprised us, intrigued us and sparked ideas for the future. We think you’ll find the same.

Selective Adoption of Robotics

From autonomous mowers in agricultural settings to robotic arms in auto manufacturing, the shift towards automation is palpable. But the implementation of robotics in engineering and manufacturing also varies significantly across industries and geographic regions. 

While some sectors, such as automotive manufacturing, have embraced robotics extensively, others are more selective in their adoption. Factors influencing this decision include:

• Labour costs and availability
• Production volume and variety
• Task complexity and repeatability

While no one was surprised to hear how ubiquitous robotics was in the Chinese consumer experience, it’s fair to say that a number of the panel were surprised to hear that there was little sign of robotics in the Chinese factory setting, outside its automotive sector. 

In China at the moment, there's not many really big factories, unless it's the China automotive companies, which are huge.And for smaller companies, robotics is too expensive. People are cheaper. People are more flexible. In the past five years, I recall seeing just one factory using a welding robot.— Mark Burnett , Industry Designer, Mabu Design

So, in countries with lower labour costs, the economic incentive for automation may be less pressing. Switch your focus to countries with higher labour costs and the robotic landscape changes. 

In China or India, jobs that could be done by a robot are done by people because they are cheaper and more flexible. It’s in countries with higher labour costs that you see more automation.— Tom Pascall , Process Engineer, Bettys & Taylors of Harrogate

The discussion also touched on industries requiring high precision and consistency, such as contact lens manufacturing where robotics play a crucial role and on how industries traditionally reliant on human labour are exploring robotic solutions for specific tasks, to free up human resources for more complex, value-added tasks. 

But for now widespread robotic adoption across the wider construction and manufacturing sectors would appear to remain slow and steady rather than high-speed and explosive..

Harnessing the Power of IIoT

These are machines of work, machines that need to do a job, and the uptime is absolutely massive. If you can get early warning signs that something's going - the motor’s got accelerated wear, it's running outside of a set of boundaries- if that can all be communicated remotely, then you can get ahead of the game when you're looking to do preventative maintenance and keep these machines running.— Lee Kristensen , Global Product Manager, Textron Specialized Vehicles

IIoT (the Industrial Internet of Things) has enabled better inventory management through real-time tracking and improved the overall manufacturing process by providing detailed analytics that help in decision-making. 

The Industrial Internet of Things (IIoT) is transforming how engineering firms approach maintenance and operational efficiency. Connected devices are increasingly being used to:

• Collect and analyse real-time data from machinery
• Enable predictive maintenance, reducing downtime
• Optimise operational processes

This shift from reactive to predictive not only improves efficiency - maintenance can be scheduled for and worked around whereas breakdowns are more disruptive and time-consuming - but also extends the lifespan of equipment.

But as was clear during our discussion, none of that is a simple plug in and go situation. There are lots of complexities involving, among other things, retrofitting older plants with new technologies, security concerns associated with IoT devices, and the need for robust cybersecurity measures to protect sensitive data.

AI's Growing Impact and Human-AI Collaboration

In our industry, we have to keep on top of this. I don't think that software developers are going to be replaced, but software developers who don't embrace AI will be replaced by software developers that do. ... Do we think that in engineering that trajectory is the same?— Giles Cambray , MD, Spork

While our discussion showed that artificial intelligence is certainly making inroads into engineering disciplines, it also revealed that AI’s adoption is often focused on specific use cases rather than full-blown, end-to-end transformation. We heard about examples of it being used in:

• Generative design in architecture, enabling rapid concept visualisation
• Object recognition in autonomous machines for improved safety and efficiency
• Data analysis for product planning and manufacturing optimisation

Mark Burnett described how, when collaborating with architects in Japan, he was able to use AI-driven design tools to address Japan's traditionally conservative construction landscape. By leveraging Midjourney, they developed innovative architectural visuals that vividly showcased potential designs, helping to bridge cultural hesitancy and open discussions around adopting new construction methods. However, Mark underscored the importance of human oversight in these AI-driven designs, especially in a culture, where decision-makers prioritise human involvement and a hands-on approach over full automation in the creative process.

Elsewhere and in other sectors, AI is being more readily leveraged to address specific issues. Lee Kristensen, for instance, spoke about the use of AI in object recognition for autonomous machines: 

How do you teach the mower to identify and securely say that this is this object and we can take the appropriate action to stop the vehicle or keep going? To carry on mowing through leaves, but stop when it's a golf ball or something like that. How does it know a light leaf from a golf ball? Yes, I believe AI will be the key to driving that sort of progress.— Lee Kristensen , Global Product Manager, Textron Specialized Vehicles

So while AI shows great promise in certain types of tasks, the panel felt that its current role is primarily supportive, enhancing human decision-making rather than replacing it entirely. Why? Because Complex decision-making, contextual understanding, judgement, and creative problem-solving continue to rely heavily on human input. 

What I bring to the role is the ability to understand data in context and assess whether both the data and context are accurate, and what other data we should consider. So I hope that I bring something else to the role that can't be replaced.—Tom Pascall, Process Engineer, Bettys & Taylors of Harrogate

But there was also a belief that by integrating that human experience and input with AI-driven object recognition and analysis capabilities, you can get even more robust solutions than from human input along. It's one of the things driving an evolution in remotely operated equipment in the construction sector.

We still have operators sitting inside the machines, but now they can put detailed drawings, surveys and other information into the machine in advance, allowing them to work faster and safer - like excavating a site with live gas or electrical services. It will only be a matter of time before it's fully robotic.— Andrew Murfin , Director, RDBL Group

Challenges in Technology Integration

From a software perspective, the minute you start connecting everything, that's actually when the complexity rises exponentially. Implementing that stuff is a lot of work and increasing the complexity, especially when you need to interact with other devices?... Very unpredictable things can start happening.— Gawain Hammond , Development Lead, Spork

The integration of new technologies with legacy systems and traditional practices poses significant challenges for the engineering sector. Key issues include:

• Ensuring seamless software updates without disrupting operations
• Maintaining data security in increasingly connected environments
• Overcoming resistance to change in established industries

Participants noted that while some embrace new technologies, there can be hesitancy among others - or institutionally among management - to adopt unfamiliar systems for some or all of those reasons. It all underscored the need for careful consideration when implementing AI-driven decision-making tools in engineering processes.

What Will Tomorrow Look Like?

The engineering sector is on the cusp of a technological revolution, with robotics, IIoT, and AI offering unprecedented opportunities for innovation and efficiency. As these technologies continue to evolve, their integration into engineering practices will likely accelerate, driving the industry towards more automated, data-driven, and efficient operations. That’s an exciting journey to take but one that requires careful navigation of technical, economic, and human factors. 

Spork wants to help engineering sector clients to find ways to harness these technological innovations for greater, more sustainable business growth. By finding that sweet spot between cutting-edge technology and practical implementation, we aim to help engineering firms navigate this exciting period of digital acceleration and transformation.

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