According to McKinsey, $11 trillion will be generated by 2025 through the digital transformation of production and the data it generates.
What are the drivers of this transformation?
- An increase in quality
- Improved operations through better monitoring and predictive maintenance
- Greater flexibility in the face of market and demand volatility and supply chain issues
However, a modification of the infrastructure is necessary to unlock these gains.
According to McKinsey, migrating to a hybrid Edge&Cloud platform offers more than significant gains:
- Inventory reduction that can yield 12-30% ROI
- Reduction of lead time which can yield between 10 to 90% ROI
- Reduced machine downtime that can yield 30-50% ROI
However, when it comes to industrial data management, many difficulties remain (Real Time, Big Data, etc..), for several reasons:
- Difficulty in assessing the volume and variety of IoT data in the face of multiple data sources
- Managing the complexity of real-time data management is a challenge for many companies
- Freeing data from independent silos is difficult with outdated tools
- Finding the right balance between the edge and the cloud requires a real internal reflection, which often remains at the embryonic stage because of a lack of knowledge about the specificities of these platforms and little time to train the teams
- Moving from reactive to predictive models requires a change in culture and practices.
- Scalability of IIoT solutions is at the heart of data-related issues
- Finally, there are still many questions about connectivity to the edge
The obstacles are therefore legion but :
- Real-time time series data generation increases 50% faster than static data generation.
- Continuous analysis is expected to grow at 28% per year.
It is crucial for manufacturers to think about a strategic plan and adopt these solutions in order to get the most out of their production data and benefit from this source of value that is just waiting to be exploited.
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