Rajat Aggarwal
Transforming Supply Chain Complexity into Predictive Intelligence
Specialized in large-scale demand forecasting, predictive modeling, and inventory optimization. I bridge supply chain domain expertise with advanced machine learning to build data-driven systems that deliver measurable operational impact.
About
Combining supply chain expertise with advanced analytics to drive operational excellence
I'm a Supply Chain Data Scientist and Machine Learning Engineer specializing in transforming complex operational challenges into predictive intelligence systems. With 4+ years of experience in demand planning and supply chain analytics, I develop data-driven solutions that optimize inventory, improve forecast accuracy, and enhance operational efficiency.
My expertise spans large-scale SKU forecasting (3,600+ products), predictive modeling, statistical analysis, and building analytical frameworks that bridge the gap between supply chain operations and machine learning. I'm passionate about leveraging advanced analytics to solve real-world problems that drive business value.
I specialize in time series forecasting, inventory optimization, demand sensing, and building scalable data pipelines that enable proactive decision-making. My approach combines rigorous statistical methods with practical business acumen to deliver solutions that are both technically sound and operationally viable.
Data-Driven Problem Solver
Leveraging advanced analytics and machine learning to unlock actionable insights from complex supply chain data.
Large-Scale Forecasting
Experienced in multi-SKU demand forecasting across 3,600+ products, optimizing accuracy and inventory efficiency.
Predictive Analytics Expert
Building predictive models for demand planning, inventory optimization, and operational intelligence.
Domain + ML Expertise
Bridging deep supply chain knowledge with machine learning to create systems that deliver measurable business impact.
Featured Projects
Case studies demonstrating the application of analytics and machine learning to supply chain challenges
Large-Scale Demand Forecasting Engine
Business Problem
Multi-SKU demand forecasting across 3,600+ products with varying demand patterns, seasonality, and lifecycle stages.
Data Inputs
Historical sales data, promotional calendars, product lifecycle indicators, seasonal indices, external market factors
Analytical Approach
Developed hierarchical forecasting models with SKU segmentation based on demand patterns. Implemented statistical forecasting with seasonal decomposition, trend analysis, and promotional impact modeling.
Methods & Techniques
Time series analysis, ARIMA-based models, seasonal decomposition, exponential smoothing, promotional lift analysis, ABC/XYZ classification
Visual Analysis
Measurable Impact
- ✓Improved forecast accuracy for high-velocity SKUs
- ✓Reduced forecast bias through systematic error analysis
- ✓Enabled proactive inventory positioning
- ✓Optimized production planning cycles
Inventory Optimization & Service-Level Prediction
Business Problem
Service level at 88.6% with frequent stockouts impacting customer satisfaction and revenue. Need to optimize safety stock while minimizing working capital.
Data Inputs
SKU-level demand variability, lead time data, service level targets, carrying costs, stockout penalty costs
Analytical Approach
Built probabilistic inventory models incorporating demand uncertainty and lead time variability. Implemented safety stock optimization using service-level constraints and cost minimization.
Methods & Techniques
Safety stock modeling, service-level optimization, demand variability analysis, lead time analysis, reorder point calculation, Economic Order Quantity (EOQ) optimization
Visual Analysis
Measurable Impact
- ✓Increased service level from 88.6% to 94.3% (+5.7%)
- ✓Reduced stockout incidents by 50%
- ✓Optimized working capital allocation
- ✓Improved customer satisfaction metrics
New Product Introduction Predictive Framework
Business Problem
Cold-start forecasting for new product launches with no historical data. Need to predict demand ramp-up patterns and optimize initial inventory positioning.
Data Inputs
Analogous product data, market research, product attributes, launch parameters, lifecycle curves, pre-launch indicators
Analytical Approach
Developed similarity-based forecasting using product attribute matching and lifecycle curve modeling. Created demand ramp-up prediction models based on comparable product launches.
Methods & Techniques
Similarity analysis, lifecycle curve fitting, attribute-based forecasting, clustering analysis, ramp-up pattern recognition, demand sensing
Visual Analysis
Measurable Impact
- ✓Improved new product forecast accuracy by 35%
- ✓Reduced inventory write-offs for slow movers
- ✓Optimized launch inventory positioning
- ✓Enabled data-driven launch planning
Manufacturing Process Yield Optimization
Business Problem
Process yield variability causing production inefficiencies and increased costs. Need to identify root causes and optimize process parameters.
Data Inputs
Process parameters, quality metrics, production logs, equipment data, material specifications, environmental factors
Analytical Approach
Implemented statistical process control and variance analysis to identify key drivers of yield loss. Built predictive models to optimize process parameters.
Methods & Techniques
Statistical Process Control (SPC), control charts, variance analysis, root cause analysis, process capability analysis, parameter optimization
Visual Analysis
Measurable Impact
- ✓Improved overall process yield by 4-8%
- ✓Reduced material waste and scrap
- ✓Achieved $15 cost reduction per pallet
- ✓Enhanced process stability and predictability
Skills & Expertise
Technical capabilities spanning machine learning, data engineering, and supply chain operations
Machine Learning & Analytics
Data Tools & Technologies
Supply Chain Domain Expertise
Impact Dashboard
Quantifiable results from data-driven supply chain optimization initiatives
SKUs Modeled
Large-scale demand forecasting across diverse product portfolio
Warehouse Performance
Improvement in operational efficiency metrics
Service Level Increase
From 88.6% to 94.3% through optimization
Cost Reduction
Per pallet through process optimization
Years Experience
In supply chain analytics and data science
NPI Forecast Accuracy
Improvement for new product introductions
Data-Driven Results
Every metric represents a real-world business problem solved through the systematic application of analytics, statistical modeling, and machine learning. These outcomes demonstrate the tangible value of combining supply chain domain expertise with advanced data science capabilities.
Research & Continuous Learning
Actively building expertise at the intersection of supply chain and advanced analytics
I'm committed to staying at the forefront of supply chain analytics and machine learning. My focus is on exploring cutting-edge techniques and translating academic research into practical, production-ready solutions that solve real-world business problems.
Applied Machine Learning in Supply Chain
- ▹Advanced time series forecasting (SARIMA, Prophet, LSTM)
- ▹Ensemble methods for demand prediction
- ▹Reinforcement learning for inventory optimization
- ▹Deep learning for demand sensing
Forecasting Model Experimentation
- ▹Hybrid models combining statistical and ML approaches
- ▹Feature engineering for supply chain data
- ▹Multi-horizon forecasting techniques
- ▹Probabilistic forecasting and uncertainty quantification
Operations Optimization Algorithms
- ▹Linear programming for supply chain optimization
- ▹Multi-objective optimization in S&OP
- ▹Simulation and scenario analysis
- ▹Prescriptive analytics for decision support
Get in Touch
Let's discuss how data science can transform your supply chain operations
I'm always interested in discussing new opportunities, challenging projects, or collaborations in the supply chain analytics and machine learning space. Whether you're looking to optimize your forecasting systems, build predictive models, or explore data-driven supply chain solutions, I'd love to hear from you.