Industrial IoT ML/AI models for TCO improvement

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Industrial IoT ML/AI models for TCO improvement
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Moreover, a large volume of data is generated from the various IIoT (Industrial IoT) sensors in the field, factory, etc., and this data can be used to learn patterns using advanced Machine Learning (ML) and Artificial Intelligence (AI) algorithms.
Gyrus has developed several such ML/AI algorithms for Industrial IoT that directly improve productivity.
Gyrus models are adapts using the Customer data from such sensors and the custom model is integrated into Global Operations giving the below mentioned (Figure-1) top-line results.
IoT ML/AI Model Results
The Customer has existing systems in place at the factory floors, Global Operations center and at the warehouses.
The overarching goal of predicting failures, improving efficiency, and making an impact on the TCO from the top management helped to a good extent when certain existing processes had to change.
Time series data is collected from the sensors and the ML/AI algorithms are run on that data.
For Global operation software, make use of existing software to make API calls for all the models.
And also provided with custom dashboards based on the raw data and the analysis by the ML/AI engines.
IoT Sensors, ML/AI Algorithms and Flow
Implementation of ML/AI algorithms for improving overall TCO with the IoT hardware as follows.
Over of period of time, the development of data annotation set in place, as the base data set from gyrus does not cover all the specific cases for the customer.
Gyrus Inventory Management model is used for supply chain decisions.
The data from Asset Management, Weather, Seasonality, Demand Variability, Supplier Variability, Macro-economic Production, Macro-economic Consumption, Inventory levels, Sales Demand, Lead Times, even more, are in use as features to predict what to order and at the order levels.
Development of an output quality prediction model from the various inputs of the learning process of features affecting the output quality

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