Real AI use cases for manufacturers that optimize operations, empower the workforce and improve quality and waste reduction.
New primary research across multiple global industries has found that the most common area respondents have or will prioritize AI are manufacturing and the value chain. The global IFS study, Industrial AI: the new frontier for productivity, innovation and competition comprised over 1700 senior decision makers.
The research found AI hype has become so high that 82 percent of senior decision-makers acknowledge that there is significant pressure to adopt quickly. However, this same group of respondents state they are concerned that a failure to plan, implement and communicate properly means AI projects will stall in pilot stage.
More importantly, we are not just talking ChatGPT here. We must look beyond the AI hype, and the growing trough of disillusionment that surrounds it, to understand the practical AI use cases in key industries, and manufacturing offers an ideal proving ground for AI-driven solutions.
There are three currently available AI developments that can provide real benefits to manufacturers – from factory floor operations to the workforce underpinning them, and even at the business-level to drive quality and sustainability.
1. Manufacturers turn to AI pattern recognition tools to avoid a data overload
The rise in AI focus will help manufacturers improve efficiency through data pattern recognition. By using historical data, AI swiftly analyzes real-time production data, identifying patterns and anomalies. The long-term value of AI and data pattern recognition will provide manufacturers with ongoing root cause analysis, streamlining work, and predicting potential product quality issues by comparing various data points.
As manufacturing systems become more complex, AI-driven data pattern recognition is crucial for sharpening quality control, predicting equipment issues, and optimizing production for fewer defects, higher OEE, and significant cost savings. With Industry 4.0 and the emergence of Industry 5.0, there will be too much data being generated every second for the human mind to cope with – AI will become an indispensable tool for manufacturers. But workers aren’t going anywhere. Despite the rise of automation and AI, humans will also remain indispensable in manufacturing due to their superior decision-making, creativity, and adaptability, something AI can’t do yet.
2. AI can further optimize worker performance
As roles evolve, workers will need new skills. Providing them with the necessary tools and training to work alongside, and be augmented by, AI will ensure a productive synergy between human ingenuity and machine efficiency. AI greatly enhances the value proposition of connected worker platforms by empowering the worker with capabilities and insights designed to further optimize their performance.
Take AI-powered search on the factory floor. An unproductive worker is an expensive worker. AI can empower workers to work smarter, not harder by providing them with intelligent search capability that not only grasps their inquiry’s intent, but also adapts to human nuances like typos or vague terms. This ensures quick and accurate responses to inquiries such as how to troubleshoot a broken mixing machine with a jammed mechanism. AI makes sure they are presented with the right information at the right time via their smart device.
Or consider the fact many manufacturing workforces are comprised of a diverse set of staff, many speaking potentially different native languages. Intelligent multilingual transcriptions allow manufacturers to ensure critical operational content is available in all the languages of a diverse workforce. AI-powered transcription can break down global language barriers and encourage worker inclusion. This is an intelligent capability that automatically translates the audio from videos into relevant subtitles in the preferred language of the user. Not only does this reduce the effort to create and maintain content – it improves the comprehension and retention of information leading to better safety, quality and productivity.
3. Reducing waste and ensuring quality control
Minimizing industrial waste while still ensuring quality remains a persistent challenge. Industrial manufacturing waste accounts for at least 50 percent of the waste generated on a global scale. To tackle this issue, AI has emerged as a promising solution to strike a delicate balance between quality control and waste reduction by enhancing our ability to make crucial, intricate, high-volume decisions. With AI-powered systems, manufacturers can now optimize their operations and make more informed decisions, leading to reduced waste and improved efficiency. The IFS AI research found respondents think AI can have the biggest impact on sustainability through designing better flow in manufacturing processes to improve efficiency.
For example, a pivotal application is in quality control, where AI-driven computer vision systems take the lead. These systems consistently scrutinize products, upholding a standard of high-quality production while simultaneously reducing the need for labor-intensive manual inspections. Ultimately AI extends into Product Lifecycle Management (PLM), guiding the entire lifespan of products, informing product design and manufacturing processes, resulting in iterative enhancements and higher-quality outputs.
In the realm of anomaly detection, generative AI acts as a self-learning sentinel. It continuously analyzes data streams, identifies normal patterns, and evolves its understanding of what constitutes anomalies. This self-learning capability allows it to proactively detect and alert operators to emerging issues, empowering manufacturers to take corrective action before they impact production.
4. Architectural readiness for AI is key
A robust Industrial AI strategy requires a potent combination of cloud, good data, processes, and skills. But the IFS research found that manufacturing is the industry where executives (only 27 percent) are least likely to feel their level of architectural readiness for AI adoption is very high.
Achieving this at scale needs a clear-eyed strategic focus, including the high-impact use cases specific to the industry, having a cloud-based infrastructure in place which has industrial AI embedded, and investing early in developing the skills needed. Adopting this approach will turn the tide of disillusionment on AI – and deliver the benefits that boards and the C-suite are demanding.
For a list of the sources used in this article, please contact the editor.
By Maggie Slowik
Maggie Slowik is Global Industry Director for Manufacturing at IFS. IFS develops and delivers cloud enterprise software for companies around the world who manufacture and distribute goods, build and maintain assets, and manage service-focused operations. Within its single platform, IFS Cloud, its industry specific products are innately connected to a single data model and use embedded digital innovation so that clients can be their best when it really matters to their customers – at the Moment of Service™.