Artificial Intelligence (AI) continues to be a hot topic across almost every industry, with its almost countless applications introducing sweeping changes and improvements that drive better outcomes for businesses. However, for the manufacturing sector, which has been going through the Fourth Industrial Revolution (4IR) for some time now, this pace of rapid innovation is nothing new.
Modern manufacturing is extremely advanced, complex, and competitive. A myriad of processes, technologies, and people – all working together in harmony. There is a constant pressure to keep up with technological advancements to increase manufacturing efficiency and stay ahead of the curve. From robots on assembly lines to automated supply chain and warehouse management platforms – the factories of today are already utilising an array of cutting-edge tech.
In this article, we explore how AI and machine learning can enhance the technology available to manufacturers, and how it can translate into boosting business results.
Unlocking the True Value of Data through Deep Learning
There is a lot of value hidden in unstructured data, and as businesses generate more and more of it, it becomes more difficult to tap into that resource with traditional analytics and research methods. Deep learning (a subset of machine learning) works by feeding raw data through an algorithm that excels at looking for patterns, and turns big data that gets collected at all stages of the manufacturing process into actionable insights. Deep learning takes inspiration from the human brain, allowing the system to learn as it gets more data.
Simulation and Automation
Industrial Internet of Things (IIoT) applications are making a big impact across industries, from automotive, to energy, to aerospace. Data, collected from machinery, sensors, cameras, and various other devices holds immense potential for optimisation of manufacturing processes and workflows.
One way to leverage it, is to connect it to a Digital Twin, which is a virtual model that receives real-time data and acts as an accurate digital representation of a physical asset. It takes into account everything from performance metrics and energy output of individual machines to weather conditions and interactions of different assets. Implementing digital twins for specific production lines or for entire factories connects a variety of sensors to provide a bigger, more complete picture of what’s happening. Not only does it grant an increased degree of control over the manufacturing process – it opens up possibilities for digital experimentation.
A potential impact of different configurations and process changes can be analysed before committing budget and resources, while running what-if scenarios can help anticipate possible problems. Predictive maintenance can lower downtime by half and increase the lifespan of industrial machinery by as much as 40%1. After all, it’s cheaper and less disruptive to keep hardware operational with timely service than to repair it when it breaks down.
Coming back to the manufacturing process itself, artificial intelligence and robotics work hand-in-hand. AI-driven platforms can learn to perform traditionally manual tasks that previously required human interaction: from connecting cables, to assembly, picking parts, and more. Robotic process automation empowered by AI solutions can increase yields by as much as 30%2. Working in conjunction with automation, AI technology is a step towards fully digitised smart factories, leveraging data to maintain and improve performance, drive down production costs, and increase profit margins.
Supply Chain and Quality Control
There is more to a manufacturing business than production lines. It needs supply chain, logistics, and inventory management — all of which can be made considerably more efficient through AI and machine learning. Thanks to real-time smart tracking, warehouse and inventory management can be streamlined making sure every item and every vehicle is accounted for. Any bottlenecks in the process can be quickly detected and eliminated, leading to faster delivery times, and the highest possible efficiency.
Price forecasting and analysis of vendor information allows manufacturers to be more agile and respond quickly to changing market conditions. Visual inspection through computer vision is also a powerful tool for quality control. Deep learning algorithms can be trained to detect defects and faults which leads to mitigating risks, reducing costs, and increasing the overall quality of the output.
Data for Pivotal Business Decisions
AI can help analyse customer data and support senior leadership teams to swiftly react to market changes. AI-enabled product development can fastrack prototyping – saving time and money on testing thanks to advanced algorithms and simulation, and help engineers innovate and deliver better products.
AI can also help optimise buyer journeys and customer experience, the same way it helps optimise a production line. Everything from historic sales data to industry and geographical region is taken into account, while leveraging predictive analytics enables manufacturers to effectively engage target buyer personas. More accurate demand forecasting translates into better long-term sustainability and growth. By learning how customers buy and use products, manufacturers can use this rich data to build trust, remove friction and turn first-time buyers into repeat customers and loyal brand advocates.
Addressing Health and Safety
Factory employees work with dangerous machinery every day, and even when all safety regulations are followed, their jobs are not without risk. Health and Safety remains one of the higher priorities in manufacturing, and machine learning applications prevent accidents by analysing data from camera feeds and IoT devices, tracking workers’ location and vital signs – and even monitoring the proper use of personal protective equipment. If a dangerous situation occurs, AI can react much quicker than a human worker, turning off machinery and ensuring a rapid response that lessens the potential impact of the accident.
Besides detecting if people on site are wearing hard hats and reflective vests, and if the machines are in order, properly trained and fine-tuned machine learning algorithms can be used in predictive analytics and scenario modelling to determine the root causes of past accidents and prevent future ones.
AI allows manufacturers to proactively address safety concerns by addressing the underlying cause of the issues, instead of being purely reactive. The result: lower injury frequency rates, less lost productivity time, and faster incident resolution.
The Future of AI in Manufacturing
The full potential of AI is still an uncharted territory. The technology is developing faster than ever, bringing value to manufacturing on every level: cutting costs, enabling automation, boosting efficiency and safety.
Theoretically, in the future, factory automation guided by AI solutions can reach unprecedented levels of productivity, operating around the clock with minor human oversight, and there won’t even be a need to turn the lights on in a factory for example. According to an article published by MDPI, digitisation and artificial intelligence already makes a huge impact on energy efficiency3, helping enterprises lower consumption and intensity while maintaining quality and increasing volume of the output.
But for now, applications of AI are aimed at ensuring all levels of stakeholders – both decision makers and workers on the factory floor – have all they need to do their jobs efficiently, safely, and in perfect synergy. Data plays a key role in this, fueling enterprises on a journey of improvement.
The biggest benefit of AI lies in a deeper understanding of the business. It enables manufacturers to have unprecedented control over every process and in partnership with technology experts, can open new horizons for digital transformation and comprehensive modernisation. In a world of increasing competition and increasing demand for quality and safety, applications of AI and Machine Learning offer a way towards increased revenue and sustainability without sacrificing quality and customer satisfaction.
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References
[1] V. Dilda, L. Mori, O.Noterdaeme, and C. Schmitz, Manufacturing: Analytics unleashes productivity and profitability, McKinsey
[2] Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector?, McKinsey Digital
https://www.mckinsey.com/~/media/mckinsey/industries/semiconductors/our%20insights/smartening%20up%20with%20artificial%20intelligence/smartening-up-with-artificial-intelligence.ashx
[3] X. Zhang, P. Liu and H. Zhu, The Impact of Industrial Intelligence on Energy Intensity: Evidence from China
Stoye, G., Warner, M. and Zaranko, B., 2021, Could NHS Waiting Lists Really Reach 13 Million?, Institute for Fiscal Studies. Available at: <https://ifs.org.uk/publications/15557>.
