Purposeful Artificial Intelligence for Asset Efficiency

Leverage AI to better manage operations and asset maintenance.

Infosys Nia Asset Efficiency Solution ushers in a new approach to planning and management of assets by acquiring, interpreting, and applying contextual knowledge.

Thus simplifying asset operations, maximizing asset lifespan, and amplifying the capabilities of the entire organization.


Infosys Nia combines end-to-end capabilities required for the asset efficiency improvements of Industry 4.0. It extracts asset related data scattered across the enterprise to create a knowledge model to operationalize organizational information, uses Big Data Analytics and Machine Learning to identify issues, perform root-cause analysis, and predict maintenance requirements.

M2M Gateway

Allows acquisition and transfer of data from industrial systems operating on multiple industrial protocols
data platform

Data Ingestion

Multiple adapters to acquire data from various enterprise sources
machine learning

Big Data Analytics and Machine Learning

Enables model building using machine learning techniques

Knowledge Management

Connects a set of disparate, unrelated entities and captures the relationship between them


Eliminates inefficiencies by automating tasks that do not require human intervention

Insights, Correlations and Prognostics

Allows real-time KPI monitoring, correlation analysis and prognostics

Asset Efficiency with Infosys Nia

Infosys Nia Delivers Results

Decline in average time for issue resolution


Reduction in energy cost


Reduction in effort to detect the anomaly

Success Stories

Chiller Efficiency Management


  • A large chiller machine sought to improve operational efficiency and rationalize costs by harvesting inaccessible technical data and the knowledge of experienced maintenance professionals.


  • Infosys Nia ingested data from all sources of organizational truth to create knowledge models that captured all information related to the asset. Infosys Nia then monitored chiller sensors and instrumentation to identify patterns to prevent potential failures. Automated self-healing through creation of pre-emptive maintenance service requests.


Reduced cost

35% reduction in energy costs.

decrease energy consumption

50% Decrease in per capita energy consumption.

decline average time

75% Decline in average time for issue resolution.

improvement productivity

75% Improvement in enterprise productivity.

Spindle Performance Monitoring


  • A large automobile manufacturer was dealing with unpredictable downtime and unwarranted replacement of spindles based on OEM guidance in one of their manufacturing shop floors.


  • Infosys Nia ingested data from legacy machines, machines with proprietary protocols, and machines with modern controls through the Infosys Nia M2M gateway. Edge analytics monitored sensors and raised alerts when thresholds were exceeded. Trend analysis and frequency domain analysis were used to predict faults. Infosys Nia automated time consuming data science activities with a framework to run Exploratory analysis, feature extraction, build, test and deploy the machine learning models.


reduced downtime

30% reduction in effort to detect anomaly.

decline average time

Reduced downtime of the spindle machine by predicting the remaining useful life.

reduced cost maintenanc

Reduced cost of maintenance and production loss due to unwarranted replacement of the spindle.

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