Unlocking Value in Manufacturing through Data Sharing

To realize the vision of the Factory of the Future, manufacturers are starting to implement advanced analytics and artificial intelligence in their operations. Leveraging these technologies end-to-end, from engineering to delivery, would unlock more value and innovation but remains challenging.

Advanced analytics and AI require large amounts of data from various sources to train decision-making and prediction algorithms. Not all manufacturers have sufficient data nor expertise to fuel these applications, so they need to obtain data from, and work with, other companies. This will help them improve their ability to anticipate problems and gain efficiency in their production processes.

Additionally, most manufacturers have low transparency into their value chains. Close collaboration is needed to understand product location, condition and authenticity. With more transparency into production processes, companies can not only provide better quality and new solutions but also prevent fraud, improve inventory management and respond to unexpected disruptions.

To date, we have established the foundations for data sharing in manufacturing in our White paper "Share to Gain: Unlocking Data Value in Manufacturing" by:


  • Identifying business needs driving data sharing and its value

  • Highlighting relevant use cases as well as suitable technologies and formats

  • Defining success factors and key enablers in preparing for and building data ecosystems in manufacturing

  • Developing a five-step framework that can help manufacturers get started on this journey

Moving forward, our Data Sharing Taskforce is working on demonstrating the concrete value of shared data around specific applications and building a framework for data sharing excellence in manufacturing.

Shedding light on these issues, this project can help manufacturers make the best of their collective data and build successful data ecosystems in manufacturing to address business needs they cannot address alone.

To learn more and engage in this project by joining , please contact memia.fendri@weforum.org

License and Republishing

World Economic Forum projects may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.