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In a world evolving at lightning speed, the once mechanical heartbeat of the manufacturing industry now pulsates with the energy of Artificial Intelligence.
Picture this: an industry where machines predict their problems before they occur, where the notion of creativity merges seamlessly with the practical design process. As the demands for innovation surge, smarter, more efficient production methods become imperative.
This blog aims to dig into the core of AI’s influence on manufacturing, uncovering its revolutionary impact on processes, product and service quality, and design.
The Role of AI in Manufacturing
To understand the role of AI in manufacturing, we must first understand what AI is and what capabilities it can bring to enhance systems and processes within the manufacturing industry.
The Collins dictionary recently named AI as the word of the year. They define artificial intelligence as “a type of computer technology which is concerned with making machines work in an intelligent way, similar to the way that the human mind works.”
Now, let’s look at some AI components relevant to manufacturers in 2023.
Predictive Maintenance
AI has revolutionised how machines are monitored and maintained in the manufacturing industry. AI can pull data from sensors and machinery on the factory floor to understand how and predict when failures and breakdowns will likely occur.
This proactive approach places manufacturers on the front foot. Instead of having to call out an engineer to rectify an issue and order replacement parts, the machines recognise problems and address them before they come to fruition.
Predictive maintenance can also estimate the amount of downtime that is to be expected in a particular process or operation and consider this when decisions around logistics and planning need to be made.
Generative Design
Recently, we’ve also seen AI become more involved in a creative capacity. Generative AI tools such as DALL-E (an OpenAI system that can create original, realistic images from text descriptions) and ChatGPT (AI-powered chatbot designed to generate human-like speech in response to prompts) have taken the world by storm.
In the manufacturing industry, generative design algorithms can be leveraged to create outcomes based on the inserted parameters: the materials, size and weight of the product, costings, and manufacturing methods used. The adoption of generative design means that the product development process can be accelerated and has also been utilised to procure new components that are cheaper, lighter, and sturdier than their counterparts.
Additive Manufacturing
Additive Manufacturing is more commonly referred to as 3D printing and is an excellent example of a form of AI technology that has revolutionised the manufacturing industry. AI plays an integral role in optimising the design of complex products – this involves maximising the potential of the materials used and applied within products.
Additionally, additive manufacturing can spot and correct errors made by 3D printing technology in real-time, decreasing the likelihood of waste and reducing cost.
Benefits of AI in Manufacturing
The Fourth Industrial Revolution (4IR) has brought about a new wave of tech in the form of smart factories, machine learning, and the Internet of Things (IoT). These technological breakthroughs have provided several benefits for manufacturing businesses. Here are some of the most prominent advantages that manufacturers can make the most of:
- Improved Efficiency: AI can be a real game-changer in improving manufacturing industry efficiencies. Outdated operations and systems can be streamlined, downtime can be reduced, and productivity can be improved drastically.
- Enhanced Quality Control: Manufacturers are held to a very high standard in quality control. One of their core objectives is ensuring end users are met with the best product. Due to the intricacies of AI technology with IoT (Internet of Things) and smart factories being rolled out across the industry, quality control can be kept to the highest possible standard.
- Supply Chain Optimisation: AI systems are automated and can be classed as ‘always on’, meaning they don’t skip a beat regarding supply chain management. Thanks to AI, future product demand can be forecasted, and an outcome can be generated. Similarly, continuous inventory management can predict when supply is running low. Therefore, specific actions can be automated when items fall under a designated figure.
Examples of AI in Manufacturing
Numerous companies in the manufacturing industry have opted for AI for various reasons. Here are just a few examples of how manufacturers are using artificial intelligence to their advantage:
Siemens: Teamcenter
Great strides have been made in the adoption of AI by manufacturers across the globe. In December 2022, Siemens partnered with Microsoft to create a cross-functional collaboration tool called Teamcenter software.
The product was set up to address and rectify common issues such as unreported issues, delayed product launches and reduced product quality. Teamcenter eradicates these issues by providing visibility and clarity throughout the design and manufacturing process.
The generative AI also provides several other benefits, such as allowing end users to record observations in their preferred language, problems can be re-routed and escalated to the relevant parties immediately, and all critical data can be extracted automatically with the help of Microsoft Azure AI.
IBM: watsonx
International Business Machines (IBM) is a leading American manufacturer in the technology sector. It has been intrinsically linked with AI technology since 1977 when they built a supercomputer to become a chess-playing expert. The system was named ‘Deep Blue’ and succeeded in its mission by reigning supreme over the world chess champion at the time, Gary Kasparov – a heavily publicised feat that proved to be the first encounter of a program beating a human champion.
Since then, IBM has continued in the same vein. In 2011, they launched Watson, a question-answering computer system that can understand and respond to queries and prompts. Over ten years later, they’ve continued to develop and advance their technological capabilities, culminating in their recent enterprise-ready AI and data platform: watsonx.
watsonx was set up with three core intentions in mind:
- To help businesses build and deploy AI apps with ease.
- Scale AI workloads from one data store.
- Monitor and govern the entire AI lifecycle.
This revolutionary technology also has a set of AI assistants integrated within to help manufacturers scale and accelerate the impact of AI across their business.
The Pitfalls of AI Adoption for Manufacturers
It must also be noted that whilst artificial intelligence can provide many benefits, several pitfalls must be navigated accordingly.
Shortage of AI Support
Experienced data scientists and AI professionals are currently in short supply. Therefore, should you run into any AI-related problems, sourcing somebody with the expertise to fix the issue may cost you significantly. AI may cut costs and improve efficiencies in the short term; however, encountering technological issues could be detrimental to your balance sheets in the long run.
Outdated IT Infrastructure
Technology is an ever-changing, ever-evolving constant; AI technologies are no different. To get the best out of the technology, you need all your IT systems to be able to integrate and communicate with each other effectively. This can make the integration quite complex and potentially costly.
If you’d like to find out whether your business is ready to start implementing and making the most of AI technology, check out our Readiness Audit – a process that identifies deficiencies, opportunities and vulnerabilities within existing IT systems.
Quality of Data
To thrive, artificial intelligence and machine learning technology need access to high-quality, clean data. With clean data, you can avoid exposing yourself to a whole host of issues that have the potential to impact product quality and production.
You can negate this by ensuring that a good level of data hygiene (maintaining clean and accurate data within a system) is always maintained. It’s also critical that companies who intend to implement AI use good data sets; this can be done by ensuring that your data is clean and well-documented.
The Future of AI within Manufacturing
Earlier this year (April 2023), Capgemini stated that more than half of European manufacturers (51%) are implementing AI Solutions, whilst 93% of manufacturing business leaders say that AI is at least moderately functional in their organisation. Therefore, it’s clear that AI will continue to impact the manufacturing industry going forward significantly.
Here are a few AI-related trends to look out for in the not-so-distant future.
Microsoft Copilot
Microsoft’s latest AI breakthrough, Microsoft Copilot, was created with the intention of transforming the workplace by unleashing the power of intelligence and empowering the largest employee contingency within the manufacturing industry: the frontline workers.
The solutions of Copilot include improving efficiencies and enabling employees to make faster decisions on the factory floor. Copilot integrates seamlessly with other Microsoft programs such as Outlook and Teams, ensuring that critical information can be accessed at the drop of a hat and frontline workers are provided with a much-improved experience while on the go.
Metaverse Technology
The Metaverse is an engaging and immersive virtual experience created by Meta to blur the lines between the real and digital worlds. Again, this could have positive outcomes for manufacturers in terms of enhancing operational efficiency and creating new market avenues.
Introducing the Metaverse within the manufacturing space would provide companies with a raft of benefits. Metaverse tools will enable users to monitor and track manufacturing data in real-time and can provide increased confidentiality by providing controlled access.
However, those benefits can only be claimed if they ensure operational interoperability (a computer system’s ability to exchange and use information), data standardisation, and collaboration across their business processes and workflows.
Overall, it’s a very exciting time to be a manufacturer. After the trials and tribulations of the COVID-19 pandemic, it seems that manufacturers have been dealt an excellent hand in the form of AI technology.
However, the countless benefits and positives can only be harnessed provided that the organisations take the necessary steps to negate the potential pitfalls that the technology could bring. Manufacturing businesses must also ensure that artificial intelligence can integrate effectively with their existing employees, systems, and business operations to get the best out of AI.