Smart Manufacturing: The Hottest Topic

Let’s talk about Smart Manufacturing – everyone else in the industry is! As manufacturers worldwide seek to adapt to evolving market demands and competitive pressures, Smart Manufacturing is emerging as a crucial enabler of sustainable growth and innovation and is the sector’s hottest topic, opening up plenty of discourse on its challenges and opportunities.

What is Smart Manufacturing?

Smart Manufacturing refers to the use of advanced information and manufacturing technologies to create more efficient, flexible and responsive production processes. At its core, it integrates technologies such as Artificial Intelligence (AI), robotics, cloud computing and big data analytics. This integration allows manufacturers to gain real-time insights, automate decision-making processes and optimise operations across entire production cycles.

Smart Manufacturing reduces waste, minimises errors and enhances the use of resources, leading to significant improvements in operational efficiency and cost savings, as well as environmental sustainability.

Smart Manufacturing systems can quickly adapt to changes in production requirements, allowing manufacturers to respond swiftly to market demands and customise products. Advanced monitoring and analytics also ensure consistent quality control, reducing defects and enhancing product reliability, whilst predictive maintenance minimises equipment failures and downtime.

Challenges and considerations

While the benefits of Smart Manufacturing are compelling, the transition to this new paradigm is not without challenges. What is being discussed when it comes to AI, by industry heads and decision makers in manufacturing? What are the challenges and opportunities being talked about? Let’s delve a little deeper.

AI

It’s undeniable that AI can allow manufacturers to make more sustainable choices. AI can effectively integrate supply chains with manufacturing processes and design departments, streamlining processes. Comprehensive data knowledge and predictions boost efficiency and allow simpler and more accessible views for business owners, leading to quick decision making.

Harnessed correctly, manufacturing industry leaders say AI can be made use of most efficiently if three things can be achieved in harmony: individual convenience, enterprise efficiency and societal impact. It’s believed if these three areas can go hand in hand, AI is doing its job.

AI is already doing its job in many spheres in the UK. In the NHS, AI is proving its mettle in disease detection. At Network Rail, AI high speed cameras on trains are effectively detecting maintenance needs and train obstructions. And the UK has a robust AI ecosystem, with Open AI, Microsoft and DeepMind all having hubs in the UK, and the government providing £7.4 million funding for AI training.

Industry leaders have highlighted, though, that for AI to be used most productively and mindfully, it should not be implemented as technology for its own sake but to solve specific business problems. It also needs to be tested in varied departments and environments, to both bolster its advantages and discover where it might be redundant.

Manufacturers have also noted that how AI operates within a business model is crucial. AI is certainly invaluable for technical risk assessments, but experts argue these programmes should be built into the original model to make the best use of time and money, going forward. Integrating new technologies into existing systems can be complex and challenging, so manufacturers need to develop a clear roadmap for seamless integration.

AI in the automative industry and the road to net zero

The conversations here surround alternative fuel sources to petrol – batteries not being the complete solution; hydrogen being undoubtedly expensive – and how AI can be used to provide a pathway to better predictions regarding the fuel of the future.

AI may prove invaluable for research and development into the design and efficiency of competing technologies, and will no doubt provide a more accurate and detailed market outlook when it comes to predictions and innovative discoveries. It can certainly combine data from multiple sources and save on human computation.

However, industry leaders have highlighted caution when it comes to identifying new materials and technologies, as AI’s predictions can be speculative. But most agree that AI-driven research has the potential to shift market trajectories, and that, in a supporting role to R&D and engineering, it has the potential to transform the landscape.

AI on production lines

AI technologies are undoubtedly transforming automotive manufacturing processes. AI-powered robotics can execute complex tasks in factories and manufacturing plants with exceptional precision and consistency. Machine learning algorithms can also analyse production data to predict potential failures in a busy production line, and optimise workflows, enhancing productivity and minimising downtime. AI can identify minute imperfections that human inspectors might miss, ensuring higher quality standards and reducing the risk of recalls.

Yet, industry heads express the need to consider the obvious downside to using AI on production lines - the potential for significant job displacement, as automated systems replace human workers. AI could also introduce vulnerabilities, such as system malfunctions or cybersecurity threats, which could disrupt production. Discussions need to be ongoing, for a clear and effective way forward in production.

Use of AI in health and safety

A burning discussion point is how AI can be used to monitor health and safety practices in manufacturing by analysing CCTV footage to detect staff members who may not be wearing PPE, especially in high-risk activities like chemical handling. Although this use may have obvious benefits, some manufacturers have raised privacy issues and how they may be viewed by unions, stressing that this kind of AI surveillance must be general and not specifically focused on individuals. An ethics question - with the notion of of worker consent at heart - it must be carefully considered,

Wearable AIs are also being talked about as another application of AI. These include Smart Glasses that can display real-time data, step-by-step instructions, and diagrams directly in the worker's field of vision, allowing hands-free operation. Smart Helmets equipped with sensors, cameras, and AR capabilities that provide real-time hazard alerts and communication tools for remote assistance. Wearable Health Monitors, smartwatches or fitness trackers, that monitor the health and wellbeing of workers by tracking vital signs like heart rate, body temperature and stress levels. Exoskeletons - wearable devices that support and enhance human movement and assist with lifting heavy objects. And Smart Gloves - equipped with sensors that can detect motion, pressure and temperature and processes data to enhance the accuracy of tasks.

The scope in uptake of all these innovations remains to be seen, but AI definitely has a very integrated future in health and safety practices.

Recruitment and training

With AI gradually establishing itself in all aspects of manufacturing, there’s a converging demand for both traditional engineers and masters of digital technology - with some industry leaders suggesting it’s only ‘unicorn’ staff members that can do both!

Individuals are needed that specialise in mechanical design, software editing and systems understanding, but missing skills can be taught. What’s required are employees who learn and grow within companies, to keep apace with AI developments and technologies. This can be achieved by graduate programmes, robust training, in-house learning academies and companies partnering with educational institutions and training centres.

Companies must invest in the education and adaptability of their staff.

The Future of Manufacturing

Smart Manufacturing represents a transformative shift in the way products are designed, produced and delivered. As technologies continue to evolve, the manufacturing landscape will become increasingly dynamic and interconnected, and manufacturers who embrace this change and invest in Smart Manufacturing will be well-positioned to lead in a competitive global market.

However, trends, opportunities and challenges will abound, and conversations must continue on how to integrate AI into a thriving business model to best serve the manufacturing industry, its leaders and its staff. Its complexities need to be carefully assessed and harnessed ethically and effectively, and every surrounding discourse will be vital as we step into the future of manufacturing together.