The advances in Generative AI in the past year represent the latest ‘shiny hammer’ – a powerful tool with specific, impactful applications. However, just as a hammer is not suitable for every repair task, Generative AI is not the one-size-fits-all solution for every challenge in the manufacturing industry.
According to Sikich’s recent Manufacturing Pulse survey, 19% of respondents have begun implementing AI into their business operations, leaving much space to further explore this tool. This article explores the diverse landscape of AI in manufacturing, highlighting where Generative AI shines and where other forms of AI are more apt.
Generative AI: Where It Excels
Generative AI’s ability to create new designs and custom solutions is unparalleled. It excels in generating innovative product designs and material compositions, optimizing for factors such as durability, cost and sustainability. It can also simulate and prototype new products and materials, speeding up the development process and reducing costs. This is particularly useful in material science, such as battery manufacturing. Recently, AI algorithms were used to propose new electrode materials, potentially leading to new batteries with higher capacity, longer life and faster charging times.
Other Forms of AI: Matching the Tool to the Task
While it may be tempting to use Generative AI across your company’s footprint, it’s only as useful as the situation allows for. It’s recommended to explore and leverage other forms of AI in your manufacturing business, including:
- Predictive Analytics: This subset of AI, while not generative, is best used in forecasting product demand, managing inventory and scheduling maintenance. Consider it the ‘screwdriver’ in our toolbox: perfect for fine-tuning operations based on data-driven insights you’ve collected and continue to compile. An example of this might be in the fashion retail industry: by utilizing predictive analytics to analyze data from past sales, current fashion trends and even weather forecasts, predictions of items that will be in demand in different regions can be made. This can help optimize inventory levels, reduce overstock and stockouts, and help ensure customer demands are met quickly.
- Automation and Robotics: Not new to the industry by any means, automating processes with robotics provides businesses the strength and consistency needed for repetitive, high-volume tasks, such as assembly, packaging and material handling. Think of AI-enhanced robotics as your ‘wrench.’ Examples of this can be seen at many large warehouses, where robotic systems automate the process of sorting, packing and shipping items. Robots can lift heavy loads and move products much faster and more consistently than humans, increasing the efficiency of warehouse operations and reducing the time it takes to process customer orders.
- Machine Vision for Quality Control: Here, AI acts as the ‘magnifying glass,’ as it closely inspects products with a level of detail and consistency impossible for human eyes. Automotive manufacturers use machine vision AI for quality control. It can inspect the assembly of vehicles and detect minute defects or inconsistencies in parts and assembly, far beyond what the human eye can discern. This ensures a higher level of quality and reliability in vehicle manufactured.
- Process Optimization: AI algorithms serve as your company’s ‘level,’ ensuring processes are streamlined and efficient. An algorithm can continually adjust and improve operations based on current and future needs. For example, in the chemical industry, AI algorithms can monitor and adjust various parameters, such as temperature, pressure, and chemical ratios in real-time, ensuring that the process is efficient, cost-effective, and produces the highest quality products.
- Human-Robot Collaboration: In this role, AI is a ‘tape measure’ that oversees the collaboration between robots and humans to improve efficiencies, as the two complement each other’s capabilities. In the automotive industry, collaborative robots (cobots) can work alongside humans to assist with tasks that are ergonomically challenging or repetitive, such as lifting or precision assembly, enhancing worker safety and efficiency.
- Supply Chain Management and Sustainability: AI in this area is the ‘calculator,’ which optimizes logistics and resource use for efficiency and sustainability. Examples in the logistics industry include AI analyzing shipping routes, weather patterns, and port conditions to determine the most efficient routes and schedules. This improves delivery times and reduces fuel consumption, contributing to sustainability efforts.
Things to Consider
The advent of Generative AI in manufacturing marks a pivotal moment, teeming with potential for groundbreaking innovation and enhanced efficiency. Its unique ability to generate novel designs and optimize processes sets it apart from traditional AI tools. However, the transformative power of AI in manufacturing lies not just in embracing new technologies but in astutely matching each AI solution – whether Generative AI or another form – to the specific challenges and tasks at hand. This precision in selection and application paves the way for unparalleled levels of efficiency and innovation.
To learn more about AI in manufacturing, be sure to check out the Sikich Industry Pulse results or get in touch with our team below.
