Roundtable 3: AI for Industry Wide Data Sharing

Roundtable 3 Banner

The manufacturing sector generates more measured, observational, operational, modeled and experience - based data than any other sector of the economy, even surpassing the financial sector. These data offer an industry base that could be contextualized and made available to enable radical innovations by AI in business practices, process engineering, product and system design, scalability, and sustainability, going far beyond improving the efficiency of manufacturing methods at individual sites.

On the other hand, few companies generate enough of the right data internally to apply AI, even for narrowly focused applications on process or machine units. This contrast of a data rich industry with data poor individual manufacturers drives a conclusion that the entire manufacturing industry can benefit from innovative AI tools and methods that aggregate data across manufacturers, while protecting critical intellectual property and preserving data privacy and provenance. Since the capabilities of and confidence in the anticipated data infrastructure can be expected to increase with additional users and contributors, the benefits of participation are expected to increase with time, fulfilling the fundamental requirement for a viable, self-sustaining, and self-financing network. In a virtuous cycle, the contributions of individual manufacturers enable broad, new industry-wide capabilities that provide productivity benefits to the contributing companies. Furthermore, the accompanying opportunities for researching new methods should provide opportunities for founding new businesses to deliver solutions to manufacturers.

A major discussion point concerns the tight grip manufacturing companies maintain on intellectual property, often extended to all production relevant data and information. This culture of secrecy emerged from a craft culture that placed high value on expertise and is as old as the industry itself. It has caused few problems to date because the culture is pervasive worldwide, and until now there has been little or no opportunity for firms to benefit financially from sharing manufacturing data. However, AI has the potential to radically increase the value of manufacturing operational and product data by harvesting the implicit knowledge incorporated in it and harnessing its predictive, reactive and discovery capacity, again through data centric modeling, machine learning, simulations, and digital twins. Since the value of this implicit knowledge almost certainly exceeds the value of explicit manufacturing knowledge, this information must be made accessible to unlock its value.