From push to pull
Predictive maintenance has become so trendy as a combination of several factors:
- Sensors are cheaper than ever, making them more widely available
- Sensors are becoming increasingly accurate and specific
- Low-bandwidth networks are now widespread
Greater visibility on the operating machinery means for companies:
- Better maintenance and repair operations planning
- Fewer business interruptions
- Optimization of infrastructure lifetime
- In fine, improve customer satisfaction and operational efficiency!
Figures only confirm these attractive promises. According to the well-known strategy consulting firm McKinsey, predictive maintenance in manufacturing could have a potential economic impact of nearly $630 billion per year in 2025*.
*The Internet of Things: mapping the value beyond the hype, McKinsey Global Institute – June 2015
Current limitations to predictive maintenance
If you’re reading this page today, it probably means that you’re already aware of the many challenges faced by organizations trying to set up a preventive environment.
- Feed your models with multiple types of data to enhance the dataset, from sensor data to images, video or even audio
- Collect data for an extended period to watch the system running throughout its degradation process
- Predict the most high-risk failures to be able to differentiate and take action on the most business-critical operations
Using ForePaaS for smart predictive maintenance
ForePaaS is a powerful AI platform that enables your organization to build and deploy production-ready data products and services. It includes all the components necessary to your project in a single, fully-fledged environment, from raw data collection to the deployment of an application or an API, through the critical steps of data preparation and storage.
Once your data sources have been connected to ForePaaS, create and orchestrate data workflows to make data ready for further analysis, perfectly fitted to your business. You can now build, train, score and run predictive maintenance models, with pipelines directly integrated into your data production line. Bring your algorithms to real life by embedding them into meaningful dashboards or feeding internal systems through APIs, in order to be proactively alerted when deficiencies or failures happen.