Manufacturing has been a longstanding pillar of progress for humankind. From the Industrial Revolution over 200 years ago to today, manufacturing has had a profound impact on our lives, made possible by its unrelenting innovation. Now, manufacturing is facing one of the most exciting, unmatched, and daunting transformations in its history due to artificial intelligence (AI) and generative AI (GenAI).
Manufacturers are attaining significant advancements in productivity, quality, and effectiveness with early use cases of AI and GenAI. The timing for these advancements is optimal as the industry grapples with skilled labor shortages, supply chain challenges, and a highly competitive global marketplace.
AI can help with all of these challenges via manufacturing-specific use cases that benefit manufacturers, their employees, and their customers. Here’s how.
How AI and generative AI can help
Innovative product design
Generative AI is augmenting human-based product design efforts and helping to accelerate innovation, enabling a virtuous cycle of market leadership through competitive advantage. AI and machine learning (ML) can do this by automating the design cycle to improve efficiency and output; AI can analyze previous designs, generate novel design ideas, and test prototypes, assisting engineers with rapid, agile design practices.
In manufacturing, process optimization that maximizes quality, efficiency, and cost-savings is an ever-present goal. AI and generative AI are ideally suited to optimize production processes and bridge the physical and digital worlds with real-time monitoring and control. That’s how automation via AI-powered systems helps manufacturers identify areas of improvement and proactively deliver process enhancements and better business outcomes.
Consider quality control. AI-powered systems can proactively identify product anomalies and defects so they can be corrected early and before waste increases. Similarly, AI can detect areas for improving and conserving energy usage and reducing waste, both of which can advance environmental programs. These improvements are ongoing and dynamic as AI continuously optimizes approaches based on real-time operational data.
Supply chain management
Manufacturing can benefit from more predictive supply chain management. AI can do this by analyzing data across production schedules, suppliers, customers, and logistics to facilitate smarter decision-making about sourcing, timing, and inventory, delivering improved efficiency and reduced costs.
Additionally, generative AI can help with demand forecasting by using historical data to predict demand fluctuations and improve inventory management. AI can also help find suppliers based on cost, quality, and reliability, to ensure a robust ecosystem for manufacturers.
Skill gap augmentation
While manufacturing runs on industrial assets, technology, and production processes, it is employees and their expertise that make it all possible. However, the “Great Retirement” has created an unprecedented skill shortage in the manufacturing industry. Experienced workers with deep expertise in processes and technologies are retiring in mass numbers as the workforce ages, leaving manufacturers scrambling to hire and upskill new talent. Generative AI can assist less experienced workers by providing training simulations and guidance to reduce the learning curve.
Manufacturing operations are inherently prone to risks and disruptions, such as cyber vulnerabilities, operational safety, and others. Generative AI can help mitigate these often serious risks. AI can use data to simulate potential scenarios and help manufacturers prepare contingency plans before a disruption occurs.
Transforming the future of manufacturing
AI and generative AI bring unparalleled innovative potential to the manufacturing industry, promising to reinvent its future. For manufacturers to harness AI and generative AI’s tremendous promise, the first and often overlooked step is to obtain the right kind of storage infrastructure. Doing so helps to ensure the final mile of AI deployment will run smoothly.
Today, many manufacturers have disparate IT systems and traditional data storage systems that were not designed to handle AI. To avoid halting AI processing mid-stream, modern storage solutions are needed prior to running AI. Modern storage characteristics include distributed storage, data compression, and efficient data indexing, all of which enable the speed and scale that AI requires.
Before the Industrial Revolution–about 200 years ago, life was very different than today. Many families lived on farms and worked together via manual labor to meet the needs of daily life. About 200 years from today, I believe life will be vastly different than today and much of it will be due to disruptive technologies like AI and generative AI. Yet, it’s unclear what the future will look like as the transformative potential of AI technologies in manufacturing is still emerging. One thing is certain: Newfound approaches and innovations will emerge and obsolete old ways of thinking and doing, creating a new pillar of progress for the future and all of humankind.
Learn more about unstructured data storage solutions and how they can enable AI technology.