AI is reshaping how manufacturing facilities operate daily, creating more efficient and safer working environments by managing environmental controls, worker scheduling, and equipment oversight. AI-powered computer vision systems can monitor production quality while ensuring workplace safety. The technology also helps optimize resource allocation, manage energy use, coordinate maintenance activities, and adjust staffing levels based on production demands.
These applications form the foundation of smart manufacturing, where AI, sensor technology, and advanced analytics work together to create more efficient, flexible, and productive environments. As these technologies evolve, they are expected to unlock even more possibilities for innovation in manufacturing.
Open vs. Closed-Source AI Environments
As manufacturers evaluate AI adoption, one of the most fundamental decisions is whether to leverage open-source or closed-source AI environments. Each approach offers distinct advantages and trade-offs that impact scalability, security, and innovation.
Open-Source AI
Open-source AI platforms offer transparency, flexibility, and cost savings. Because their source code is publicly available, manufacturers can customize models to fit their specific needs and benefit from a global community of developers contributing to continuous improvements. Open-source AI also fosters innovation by enabling cross-industry collaboration.
However, Dr. Michael Brodbeck, director of project management at FANUC, emphasized the importance of data integrity. “With open-source environments, when so many people input into the system, it runs the risk of people inputting bad data. With garbage in comes garbage out,” he said.
Mohsen Zayernouri, associate professor of mechanical engineering at MSU, added that open-source products are often used for creativity and general ideation, whereas closed-source products are preferred for proprietary information and targeted research and development.
Closed-Source AI
In contrast, closed-source AI solutions—such as those from Google Cloud AI, Microsoft Azure AI, and IBM Watson—offer built-in support, enterprise-grade security, and proprietary algorithms tailored for specific use cases. These platforms often integrate seamlessly with existing enterprise systems, making them attractive for manufacturers with limited AI expertise.
Ingrid Tighe President, of MMTC said “The information is trusted with good sources in a closed system. It is more transparent and traceable. However, it is more limited inside the sandbox. Also, you must have strong cybersecurity in a closed-source environment.”
Brodbeck likened open vs. closed AI environments to their discussion setting. “A closed-source approach is like keeping our roundtable conversation within this table, but an open-sourced one is sharing it with the world,” he said.
Making the Right Choice
For manufacturers, the decision between open- and closed-source AI depends on factors such as budget, security requirements, and technical capabilities. Many companies adopt a hybrid approach—leveraging open-source tools for experimentation and innovation while integrating closed-source AI for mission-critical applications that require reliability and support.
Read this article in our Integr8 Playbook, "Boosting Productivity in the AI Frontier," here.
