Products
1. Generative AI Application Suite
Overview
A cutting-edge suite of Generative AI tools designed for Product Complaints Management, this application incorporates reusable capabilities like Data Extraction, SOP Assistance, and Text Translation.
Features
Data extraction from complex text sources to simplify workflows.
Standard Operating Procedure (SOP) assistance powered by AI for seamless operations.
Real-time text translation for global collaboration.
Impact
$2M cost savings realized through process optimization.
Improved efficiency and accuracy in managing product complaints.
Significant reduction in manual intervention through automation.
2. Quality Control (QC) Lab Scheduling Algorithm
Overview
An innovative algorithm developed to optimize resource utilization in Quality Control Labs, ensuring smooth operations amidst rising testing demands.
Features
Scheduling based on staff availability, holidays, and time-offs.
Real-time updates powered by a Utility Function.
Comprehensive scheduling dashboards for QC managers.
Impact
Achieved 50% efficiency gains for QC managers.
Enhanced throughput in response to high testing demand.
Streamlined operations with data-driven scheduling.
3. Real-Time Sensor Health Monitoring Application
Overview
A proactive solution designed to monitor the health of critical sensors using a multi-model deployment framework.
Features
Real-time data monitoring and early failure detection.
On-demand deployment of predictive models.
User-friendly interface for operational teams.
Impact
Reduced potential operational disruptions through early detection.
Improved system reliability with real-time data insights.
4. Inventory Management Dashboard
Overview
A self-service dashboard enabling Supply Chain Leads to monitor inventory in real time and enhance operational efficiency.
Features
Automated inventory tracking with visualized accuracy metrics.
Real-time updates to reduce manual errors.
Historical trend analysis for improved decision-making.
Impact
Reduced manual inventory errors by 15%.
Improved budgeting and resource allocation through accurate forecasts.
5. Non-BOM Raw Material Forecasting Model
Overview
A predictive model using Vector Auto Regression (VAR) for forecasting non-Bill of Materials (non-BOM) raw material consumption.
Features
Accurate forecasting using historical data trends.
Integration with resource planning tools for budgeting.
Rigorous testing to ensure model reliability.
Impact
20% improvement in forecasting accuracy.
More effective budgeting and resource planning.
Reduced wastage and optimized inventory management.
6. Enterprise Data Lake for Real-Time Data Ingestion
Overview
A centralized Enterprise Data Lake enabling seamless data ingestion across Real-Time, Near Real-Time, and Batch pipelines.
Features
Scalable data architecture using Databricks and AWS Services.
Self-service reporting dashboards for operational managers.
Enhanced data quality and throughput metrics for consistent performance monitoring.
Impact
Improved cross-functional collaboration with centralized data.
Streamlined decision-making processes for multiple teams.
Enabled domain-specific application development.
7. Capital Project Performance Metrics
Overview
A project focused on defining and tracking Key Performance Indicators (KPIs) for capital projects through detailed statistical analysis and data wrangling.
Features
Correlation analysis to uncover meaningful patterns.
Comprehensive data exploration for actionable insights.
Real-time performance tracking dashboards.
Impact
Enhanced incident reporting and tracking accuracy.
Data-driven insights for improved safety measures.
Transparent progress monitoring for stakeholders.
8. Career Fair Event Analytics & Automation
Overview
A transformative initiative at NJIT, automating reports and enhancing efficiency for Career Development Services.
Features
Automated reporting of student placements and employer engagement trends.
Insights into multi-year historical hiring performance.
Scalable Tableau dashboards for ad-hoc reporting needs.
Impact
Saved valuable working hours through automation.
Improved understanding of placement dynamics for better strategies.
Enhanced collaboration between students, employers, and academic stakeholders.