Industrial IoT

Industrial IoT ML/AI models for TCO improvement


Industrial adoption of the Internet of Things (IoT) and digitization has provided great visibility into the several processes in a Factory or a Manufacturing Unit.  The Analytics on top of the monitored data helped managers and executives improve productivity by manually monitoring each of the metrics. 

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. The rest of the paper talks about the different models developed, sensors used and the specific target achieved. 

IoT ML/AI Model Results
IoT ML/AI Model Results

Company Profile

The customer is a large industrial conglomerate with over $4B+ in revenue, 10K+ employees. The Customer has several factories, warehouses, and logistics operations to produce, store and deliver to their customers worldwide. They operate on very tight margins and its protection is of paramount importance. In the first phase (this paper covers that phase), the IIoT project is implemented in a single department of the company. 


The Customer has existing systems in place at the factory floors, Global Operations center and at the warehouses. These Hardware and Software systems are a no-touch as they are operational and the current staff is well versed with them. Integrating new hardware is a big challenge as it involves installation and maintenance. For the Software systems as well, there are several databases and existing software modules that are customization and being in use case. Any new software has to integrate with those modules. 

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. 


To address the challenges mentioned above, the installation of new sensors set up without disturbing the existing flow. Certainly, Gyrus worked with a Systems Integrator who installs and maintains equipment for the Customer to install 

  • Wireless industrial IoT Modules
    • Sensors – Vibration, Humidity, Temperature, Gyro, Accelerometer
  • Wireless Controller with Connectivity to cloud

The usability of a single module with all the sensors populated with wireless connectivity helps to reduce the number of SKUs to install. Creating a parallel path for data collection and processing, without changing any of the existing hardware and machinery. It helps in the integration part to be completed fast and with the least friction.

Time series data is collected from the sensors and the ML/AI algorithms are run on that data. The data is collected from existing sources as well when required and available. For Global operation software, make use of existing software to make API calls for all the models. It also generates alerts with severity levels for predictive maintenance. 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
IoT Sensors, ML/AI Algorithms and Flow

Algorithms Implemented  

Implementation of ML/AI algorithms for improving overall TCO with the IoT hardware as follows.

Predictive Maintenance

With the various sensors including the Vibration sensors, providing continuous time-series data, the algorithm looks for conditions that could foretell a failure. At a base level, In order to predict any failure, anomalies located to detect in the sensor data. 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.

Remote Asset Management

The goal of asset tracking is to easily locate and monitor key assets and track their usage. It also helps in optimizing the supply chain logistics. Monitoring of active usage of the equipment is for the proper use also and reported usage is corroborated for billing. Machine Learning techniques are proven to be invaluable as an absolute match of values is not possible.

Supply Chain and Inventory Management

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.

Optimizing Manufacturing yield

This is a standard optimization model that adapts to the customer use case. Moreover, the model governs equipment usage for optimal output, Raw material input to yield optimal results. As a result, this model prescribes (Prescriptive) the usage times, and the raw material quality and quantity metrics.

Output Quality Management

Development of an output quality prediction model from the various inputs of the learning process of features affecting the output quality. The output quality prediction model predicts if the quality metrics fall below the threshold before the manufacturing and QA process. It also presents the causal features and the expected thresholds suggesting the specific areas to improve. 

Please refer to the complete IoT ML/AI models from Gyrus here.


Retooling parts of a Factory from both hardware and software perspective is a major challenge especially protecting the existing process in place. Setting up a parallel path for the new sensors helped the process. It’s challenging, but doable by working closely with the Customer Team in assessing the integration points and by implementing APIs for ML/AI models. 

Furthermore, The results exceeded the initially set goals and the customer plans to do the next phases of the project.

The models predicted any maintenance required for the machinery with more than 99% accuracy. At times when the time was not sufficient to call in maintenance before the breakdown, just switching off the system helped save repair costs. 

Moreover, the Inventory Management model saved costs by effectively placing orders and quantities. Certainly, this is a direct impact on the efficiency of the operation. Similarly, Yield and Quality improvement models had an uplift with the ML/AI models. 

Above all, the whole process set in place a framework for future extensions and to have the customer embrace Industry 4.0 methodology.


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