The easiest way to optimize production processes, reduce costs and improve product quality is to use complex solutions that include different technological tools on a single integrated base. It is easier to adjust them to the company’s processes and changing realities, choosing only the necessary tools.
IoT solutions in the cloud are tightly integrated with PaaS services for working with big data and machine learning, data warehouses of unlimited volume, and other tools for collecting, processing, and analyzing information.
You can select “bricks” and assemble the desired system from a set of tools for such platforms. Its construction from ready-made blocks speeds up implementation, helps to get results faster, and recoups integration costs in a short time.
Leverage IoT Cloud Platforms for Rapid IoT Adoption
So What Do Cloud-Based IoT Platforms Allow?
Build data pipelines: You can collect and consolidate data from various sources to then be used for forecasting or decision-making in real-time.
How does it work? For example, using the platform, data is collected from IoT devices, from enterprise SCADA systems and its databases, integrated with the MES level, then merged into Data Lake, aligned and enriched. They then use machine learning techniques to make real-time decisions or seek insights for long-term decision-making.
A separate data source is video analytics. You can collect data from any video and image sources, process it using recognition systems and computer vision, consolidate with other data – and get an additional source of reliable information for the work of analysts.
Such conveyors can be built for any production.
Implement predictive analytics: Predictive analytics allows you to make reliable predictions based on information from various sources, predict failures, detect anomalies, and change business processes in time.
Predictive analytics will help predict the maintenance time when equipment needs to be repaired, temporarily taking it out of service without downtime and in emergencies. In addition, you can predict the change in product quality when changing production processes or analyze supply and demand in the market.
Based on modern IoT platforms, you can create data sources that, using machine learning, allow you to build predictive models, test them and quickly implement the most effective ones.
Model assets and create digital twins: The digital twin of an IT asset, production process, or equipment is created based on data from different sources. The more data there is and the more reliable they are, the more detailed and accurate the digital copy of the production will be.
For example, you can create a digital twin of a workshop. In the production simulation program, change various parameters and see how they affect the entire process or its parts: the speed of work, product quality, the number of raw materials consumed, etc.
Create a unified digital environment and an internal corporate marketplace for IT solutions: Will combine the disparate applications and systems of the company into a single digital environment with uniform principles of data exchange. This will avoid duplication of data from different sources, their pollution. It will also help you process information faster and draw reliable conclusions.
In addition, a unified digital environment built on shared principles will help quickly implement and update IT solutions in company branches or new divisions. Typed internal processes and ready-made internal services for working with IoT and data, adjusted to the processes of the enterprise, can be immediately used in other workshops.