Key highlights
Boosted efficiency and savings with
AI-driven automation
Improved decision-making
and compliance with real-time data
Challenges
1.
Accurate tracking of product and material history, location, and application was critical.
2.
Identifying and addressing fundamental issues in the process was challenging.
3
Difficulty in predicting machinery and component lifespan hindered timely maintenance and led to downtime.
4
Without analytics, factories struggled to uncover insights, identify patterns, and analyze trends.
Solution
1.
Zensar’s AI and Gen AI fabric drove the smart factory’s advanced functionality and incorporated several critical features, including:
Tracking product history, location, and application.
Collecting sensor data and performing analysis.
Identifying root causes of issues.
Predicting machinery and component maintenance needs.
Automating information retrieval and processes with AI bots.
Simulating processes using 3D tools for optimization.
Optimizing energy consumption.
Deriving insights from data analysis using visual tools.
Automating decision-making.
These capabilities were powered by a robust data lake, serving as the core repository.
Impact
Enhanced traceability Tracked product history, location, and application while ensuring quality control and compliance.
Data-driven insights Analyzed sensor data to identify root causes and predicted maintenance needs, which reduced costs.
Faster data access AI bots improved information retrieval, accelerating decision-making and boosting overall efficiency.
Operational efficiency Automated processes and decision-making, reducing manual tasks and errors.
Energy optimization AI fabric improved energy usage, lowered costs, and enhanced sustainability.