🚀 New Course Supply Chain Analytics — SQL · Python · Excel · Power BI, supercharged with AI View Program →
📍 Hyderabad, India  |  🎓 2025–26 Cohort Now Enrolling
✉️ krishnaidu@mathnal.tech
📱 +91 79936 51356
Professional Certification · 2025–26 Cohort

Supply Chain
Analysis, Machine Learning
& Agentic AI Program

India's most comprehensive supply chain intelligence program. Master data analytics, ML forecasting, discrete-event simulation, network optimisation and autonomous AI agents — all with hands-on Python, SQL, Power BI and cutting-edge LLM frameworks. Build 12+ production-grade projects over 12 months.

📩 Enrol Now – ₹3,50,000 View Full Curriculum ↓
1 Year
Duration
8 Hrs
Per Week
416+
Total Hours
12+
Projects
Total Program Investment
₹3,50,000
Inclusive of all materials, API credits & certification

🧠 Supply Chain Analysis

Advanced SC analytics, S&OP, KPIs, ABC-XYZ, statistical modelling — powered by Python, SQL & Power BI dashboards. Transform raw data into strategic decisions.

📊 Machine Learning & Forecasting

Classical → ML → Deep Learning → Foundation Models (TimeGPT, Chronos). Full MLOps lifecycle with MLflow, Prefect & Docker. Demand sensing at scale.

🤖 Agentic AI & AI Agents

Build multi-agent supply chain systems using LangGraph, AutoGen & CrewAI. Autonomous procurement bots, RAG copilots, and HITL decision workflows.

12
Months Duration
8
Hours per Week
416+
Total Hours
6
Program Phases
12+
Capstone Projects
30+
Tools & Frameworks
Why This Program

Designed for Next-Gen Supply Chain Leaders

This program bridges the gap between classical supply chain education and the AI-native future — giving you both the analytical rigour and the cutting-edge tooling to lead digital transformation.

🏗️
Full-Stack SC Intelligence
Go from raw data to autonomous decisions. Learn Python, SQL, Power BI, SimPy, OR-Tools, LangChain and LLM agents in one coherent, integrated curriculum — no fragmented courses.
End-to-End
🔬
Basic to Advanced Simulation
Start with Monte Carlo and random variable sampling, progress to SimPy discrete-event models, and culminate in LLM-driven agent-based simulations with digital twin concepts.
SimPy · Mesa · DES
📈
Forecasting from Classical to AI
ARIMA → XGBoost → LSTM/TFT → TimeGPT/Chronos. Learn statistical, ML and foundation model forecasting with uncertainty quantification, ensemble methods and auto-retraining.
TimeGPT · Chronos · NeuralForecast
⚙️
Optimisation at Scale
Network design, multi-echelon inventory, VRP routing and stochastic optimisation using Google OR-Tools, PuLP and Pyomo — deployed as microservices with FastAPI.
OR-Tools · PuLP · Pyomo
🤖
Agentic AI & Multi-Agent Systems
Build production-grade AI agent networks using LangGraph, AutoGen and CrewAI. Autonomous procurement bots, RAG copilots, and human-in-the-loop approval workflows.
LangGraph · AutoGen · CrewAI
🏅
Portfolio + Certification
Complete 12+ industry-grade capstone projects — demand sensors, optimisation services, autonomous agents — and earn the Mathnal Certified Supply Chain AI Practitioner credential.
Certified Practitioner
12-Month Curriculum

Six Phases. One Complete Journey.

8 hours every week, structured across six progressive phases that build on each other — from SC fundamentals to deploying autonomous AI agents in production.

01
Phase 01 · Months 1–3
SC Foundations, Python, SQL & Power BI Analytics
96 Hours
Months 1 – 3
Supply Chain Architecture & Digital Strategy
Core
SCOR framework, S&OP, DDMRP, digital twin concepts, supply chain KPIs, bullwhip effect, demand-supply dynamics, SC risk taxonomy, modern SC network design principles
SCORDDMRPS&OP
Python Core Stack for Supply Chain
Python
NumPy arrays, Pandas DataFrames & ETL, Matplotlib/Seaborn/Plotly visualisations, Jupyter + VS Code workflow, Git/GitHub version control, virtual environments, SC data schemas
NumPyPandasPlotlyGit
SQL & Data Engineering for SC
SQL
PostgreSQL & DuckDB; advanced window functions, CTEs, recursive queries; inventory and procurement analytics; star/snowflake schemas; dbt transformation pipelines; Parquet & Delta Lake intro
PostgreSQLDuckDBdbtSQLAlchemy
Power BI – SC Analytics Dashboards
Power BI
Data modelling (star schema), DAX measures & KPIs, Python visuals inside Power BI, real-time streaming datasets, procurement/inventory/logistics reports, Row-Level Security, Power Query M language
DAXPower QueryPython VisualsRLS
Statistical Analysis & ABC-XYZ Modelling
Python
Descriptive statistics, probability distributions (Normal, Poisson, Gamma), hypothesis testing, ABC-XYZ-VED segmentation, safety stock & reorder point calculations, SciPy & Statsmodels
SciPyStatsmodelsSeaborn
Capstone 1: SC Analytics Platform
Project
Build an end-to-end SC analytics platform: ingest CSV/ERP data → PostgreSQL DWH → dbt transformations → Python KPI engine → Power BI executive dashboard with drill-through & RLS
Full StackPythonSQLPower BI
02
Phase 02 · Months 3–5
Forecasting — Classical to Foundation LLM Models
80 Hours
Months 3 – 5
Classical Time-Series Forecasting
Python
ARIMA, SARIMA, ETS, Holt-Winters, Croston (intermittent demand), hierarchical reconciliation (bottom-up, MinT), backtesting framework, MAPE/RMSE/WAPE/sMAPE metrics, Statsmodels & StatsForecast
StatsmodelsStatsForecastProphet
ML-Based Demand Forecasting
Python
Lag features, rolling window statistics, calendar & external features; XGBoost, LightGBM, CatBoost pipelines; SHAP explainability; time-series cross-validation; Optuna hyperparameter tuning; feature stores
XGBoostLightGBMSHAPOptuna
Deep Learning Forecasting
Python
LSTM, Temporal Fusion Transformer (TFT), N-BEATS, N-HiTS with PyTorch & PyTorch Forecasting; attention mechanisms; probabilistic forecasting; multi-step-ahead prediction; uncertainty quantification (conformal)
PyTorchTFTN-HiTSNeuralForecast
Foundation Models & LLM Forecasting
LLM
TimeGPT (Nixtla) zero-shot forecasting; Amazon Chronos; Moirai (Salesforce); LLM-generated forecast narratives; combining statistical + ML + foundation model ensembles; exogenous variable handling
TimeGPTChronosMoiraiNixtla
Forecast Power BI + Python Integration
Power BI
Publish Python forecast models to Power BI; automated refresh with Azure ML / local gateway; scenario planning slicers; confidence interval bands; demand sensing live dashboard; S&OP reporting module
Power BIPythonDataflows
Capstone 2: Automated Demand Forecasting Engine
Project
Build a full forecasting engine: raw data ingestion → feature engineering → ensemble model (ARIMA + LightGBM + TimeGPT) → auto-retraining trigger → Power BI demand sensing dashboard
PrefectMLflowPower BI
03
Phase 03 · Months 5–7
Simulation — Monte Carlo to Agent-Based Digital Twins
80 Hours
Months 5 – 7
Basic Simulation: Monte Carlo & Stochastic Modelling
Python
Random variable sampling, demand variability simulation, inventory risk quantification, lead-time uncertainty modelling, what-if scenario analysis, variance reduction techniques, Latin Hypercube Sampling
NumPySciPyPlotly
Discrete Event Simulation with SimPy
Python
SimPy process modelling (warehouses, ports, factories), resource contention, priority queues, simulation clock management, multi-echelon inventory simulation, warm-up period analysis, output confidence intervals
SimPyPandasSeaborn
Advanced SC Simulation: System Dynamics & Agent-Based
Python
System dynamics modelling (feedback loops, stock-flow), agent-based simulation with Mesa framework (multi-agent SC), sensitivity analysis, experimental design (DoE), simulation output analysis & KPI reporting
MesaSimPyScipy.stats
LLM + Simulation: AI-Driven What-If Explorer
LLM
Natural language → simulation parameters via LLM; LangChain-powered scenario generator; interactive what-if explorer (Streamlit + SimPy + GPT-4o); automated narrative report generation from simulation results
LangChainStreamlitGPT-4o
Simulation Output Analytics in Power BI
Power BI
Pipeline: SimPy outputs → SQL → Power BI; animated Gantt & flow visuals; percentile bands; drill-through KPI boards; scenario comparison matrices; executive simulation summary reports
Power BIDAXPostgreSQL
Capstone 3: Digital Twin Warehouse Simulator
Project
Build a digital twin of a multi-echelon warehouse network: SimPy DES engine + agent-based worker model + LLM scenario generation interface + Power BI live KPI dashboard
SimPyMesaLangChainPower BI
04
Phase 04 · Months 7–9
Optimisation — Network, Inventory, Routing & Stochastic
80 Hours
Months 7 – 9
Linear & Integer Programming Foundations
Python
LP formulation, PuLP solver, SciPy.optimize, production planning, blending & transportation problems, sensitivity analysis (shadow prices, reduced costs), MIP models, branch & bound intuition, Gurobi intro
PuLPSciPyGurobiPyomo
Supply Network Design Optimisation
Python
Facility location (p-median, capacitated FLP), multi-echelon network flow, capacitated VRP with OR-Tools, scenario-based network analysis, cost-service trade-off curves, resilience modelling, NetworkX graphs
OR-ToolsNetworkXPuLP
Inventory Optimisation Models
Python
EOQ, (r,Q) and (s,S) policies, newsvendor model, multi-item joint replenishment, stochastic inventory optimisation with service-level constraints, DDMRP buffers, SKU rationalisation, substitution effects
NumPySciPyPyomo
Last-Mile & Vehicle Routing Optimisation
Python
VRP, VRPTW, CVRP, multi-depot VRP with OR-Tools; metaheuristics: Genetic Algorithm, Simulated Annealing, Tabu Search; OSRM & Google Maps API integration; real-world route optimisation case studies
OR-ToolsOSRM APIFolium
Stochastic & Robust Optimisation
Python
Two-stage stochastic programming, scenario generation (SAA), robust optimisation under uncertainty, chance-constrained programming, simulation-optimisation coupling (SimPy + OR-Tools feedback loop)
PyomoSimPyOR-Tools
Capstone 4: SC Network Design & Routing Service
Project
Production-ready optimisation microservice: FastAPI REST endpoint wrapping OR-Tools VRP + facility location solver; results stored in PostgreSQL; Power BI optimisation KPI dashboard; Streamlit UI
FastAPIOR-ToolsPower BIDocker
05
Phase 05 · Months 9–10
Supply Chain Machine Learning & MLOps
64 Hours
Months 9 – 10
Supplier Risk Scoring & Anomaly Detection
Python
Isolation Forest, One-Class SVM, LSTM Autoencoder for anomaly detection in procurement & logistics; supplier scoring models; credit risk ML; supply disruption early warning systems; SHAP explanations
Scikit-learnPyODSHAP
Lead Time & ETA Prediction
Python
Regression and classification for ETA prediction; survival analysis for time-to-delivery; gradient boosting pipelines with Scikit-learn; quantile regression for uncertainty; Shapley-based root cause attribution
LightGBMlifelinesScikit-learn
Dynamic Pricing & Revenue Optimisation
Python
Price elasticity models, reinforcement learning for dynamic pricing (Stable-Baselines3, PPO/DQN), markdown optimisation, demand-price ML, multi-armed bandit exploration, profit-maximisation constraints
Stable-Baselines3GymXGBoost
ML Operations (MLOps) for Supply Chain
Python
MLflow experiment tracking & model registry; DVC data versioning; Prefect & Airflow pipeline orchestration; Docker-based model serving (FastAPI + Uvicorn); concept drift detection; automated retraining triggers
MLflowDVCPrefectDocker
06
Phase 06 · Months 10–12
Agentic AI, LLMs & Autonomous Supply Chain
80 Hours
Months 10 – 12
LLMs & RAG for SC Knowledge Systems
LLM
OpenAI GPT-4o, Anthropic Claude 3.5, Gemini APIs; Retrieval-Augmented Generation (RAG) with LangChain & LlamaIndex; vector DBs (Chroma, Pinecone, Weaviate, Qdrant); SC policy Q&A bots; document intelligence for POs & invoices
LangChainLlamaIndexChromaPinecone
Tool-Calling & ReAct SC Agents
LLM
LangChain Agents (ReAct, Plan-and-Execute, OpenAI Functions), custom tool registry (SQL tool, simulation tool, optimisation tool, email tool, ERP API); SC copilot that queries DWH and interprets results in natural language
LangChainOpenAIFastAPI
Stateful Agent Graphs with LangGraph
LLM
LangGraph stateful agent graph architecture; conditional routing, branching & cycles; persistent memory (short-term & long-term); checkpointing; human-in-the-loop interrupts; parallel node execution; streaming responses
LangGraphLangSmithRedis
Multi-Agent Networks: AutoGen & CrewAI
LLM
AutoGen GroupChat with specialised agents (demand planner, procurement bot, logistics controller, risk monitor, expeditor); CrewAI crew & task hierarchies; agent communication protocols; agent memory & learning
AutoGenCrewAIGPT-4o
Autonomous Procurement & Replenishment
LLM
End-to-end agentic workflow: monitor inventory levels → run ML forecast → detect gap → generate PO draft → human approval via Slack/email → post to ERP mock API → audit trail; guardrails & safety layer with NeMo Guardrails
LangGraphFastAPISlack APIGuardrails
Capstone 6: Autonomous SC AI Command Centre
Capstone
Deploy a full autonomous SC command centre: multi-agent network (LangGraph + AutoGen) monitoring KPIs, triggering simulations, running optimisations, and generating natural language board reports — with full observability via LangSmith
LangGraphAutoGenPower BIDockerLangSmith
Tech Stack

30+ Production-Grade Tools & Frameworks

Every tool is used in hands-on projects — not just slide decks. You'll graduate with a real, working portfolio across the full stack.

🐍
Python Core
NumPy · Pandas · Matplotlib · Seaborn · Plotly · Streamlit · FastAPI · Pydantic
🗄️
SQL & Data Eng.
PostgreSQL · DuckDB · dbt · SQLAlchemy · Apache Parquet · Delta Lake · Airflow
📊
Power BI
DAX · Power Query M · Python Visuals · Dataflows · RLS · Report Server
📈
Forecasting
StatsForecast · NeuralForecast · TimeGPT · Chronos · Moirai · Prophet · Darts
🎲
Simulation
SimPy · Mesa · SciPy Monte Carlo · Latin Hypercube · Plotly Animations
⚙️
Optimisation
Google OR-Tools · PuLP · Gurobi · Pyomo · SciPy.optimize · NetworkX · OSRM
🤖
LLM & Agents
LangChain · LangGraph · AutoGen · CrewAI · OpenAI · Anthropic · Gemini · Groq
🗃️
RAG & Vectors
Chroma · Pinecone · Weaviate · Qdrant · LlamaIndex · FAISS · Sentence Transformers
🔬
ML & MLOps
Scikit-learn · PyTorch · MLflow · DVC · Prefect · Docker · LangSmith · HuggingFace
🛠️
Dev Environment
VS Code · Jupyter · Git · GitHub Actions · Poetry · Conda · Ruff · pytest
System Architecture

What You'll Build & Deploy

Every phase contributes a layer to a complete, production-ready supply chain intelligence platform with autonomous AI capabilities.

📥
Data Layer
Ingest ERP/CSV/API data into PostgreSQL + DuckDB warehouse with dbt transformation pipelines
PostgreSQL + DuckDB
dbt Transformations
Delta Lake / Parquet
SQLAlchemy ORM
📊
Analytics & BI Layer
Python KPI engines + Power BI DirectQuery dashboards with RLS and real-time streaming
Power BI + DAX
Python Visuals
Streamlit Apps
FastAPI REST
⚡
Intelligence Layer
Forecasting + Simulation + Optimisation microservices with MLflow tracking and Prefect orchestration
Forecast Engine
SimPy DES Engine
OR-Tools Solver
MLflow + DVC
🤖
Agentic AI Layer
LangGraph stateful agents + AutoGen multi-agent network with HITL approvals and LangSmith observability
LangGraph Agents
AutoGen Network
RAG Copilot
LangSmith Tracing
Target Audience

Who Should Join This Program?

🏭
SC Professionals
Planners, analysts, buyers and managers who want to upgrade from spreadsheets to Python-powered intelligence and AI-driven decision-making
💻
Data Scientists & Analysts
Data professionals looking to specialise in supply chain applications — forecasting, optimisation, simulation and agentic AI at domain depth
🎓
MBA / Engineering Graduates
Fresh graduates from supply chain, operations research, industrial engineering or business analytics backgrounds seeking a competitive edge
🚀
Consultants & Entrepreneurs
Consultants building SC practices and founders building SC-tech products who need deep technical fluency in AI, ML and optimisation
Learning Outcomes

Skills You'll Command

01
End-to-End SC Analytics Platform
Design and deploy a complete supply chain analytics platform: ETL pipelines in Python & SQL, dbt transformations, and Power BI executive dashboards with live KPI monitoring
02
Production Forecasting Systems
Build and deploy automated forecasting engines combining classical, ML and foundation model methods (TimeGPT, Chronos) with MLOps pipelines, auto-retraining and uncertainty quantification
03
Advanced SC Simulation
Model complex supply chains with Monte Carlo, SimPy discrete-event and Mesa agent-based simulation — integrating LLMs for natural language scenario generation and interpretation
04
Network & Inventory Optimisation
Formulate and solve facility location, multi-echelon inventory, VRP routing and stochastic optimisation problems using OR-Tools, PuLP and Pyomo, exposed as REST microservices
05
SC Machine Learning Pipelines
Deploy full ML pipelines for supplier risk, lead-time prediction, anomaly detection and dynamic pricing — with complete MLOps lifecycle: tracking, versioning, drift detection and retraining
06
Autonomous AI Agent Systems
Architect and deploy multi-agent AI networks using LangGraph, AutoGen and CrewAI for autonomous supply chain decision-making, with HITL guardrails, observability and production safety layers
Enrolment

Begin Your Transformation Today

Total Program Investment
₹3,50,000
Inclusive of all materials, projects, API credits & certification

Everything Included

✓ 416+ hours of live & recorded instruction across 12 months
✓ 12+ industry-grade capstone projects with real datasets
✓ Full Python, SQL & Power BI development environment setup support
✓ LLM API credits (OpenAI, Anthropic, Gemini) for agent labs
✓ Cloud compute credits for MLOps & deployment labs
✓ Mathnal Certified Supply Chain AI Practitioner certificate
✓ Lifetime access to course repository, code & future updates
✓ 1:1 career mentoring & portfolio review sessions
✓ Access to Mathnal alumni network & job referral program
✓ Weekly Q&A sessions with industry expert guest faculty

Ready to Enrol?
Let's Talk.

Reach out via email or WhatsApp to reserve your seat for the 2025–26 cohort. Limited seats available.

✉️
📱
WhatsApp / Mobile
+91 79936 51356
📅
Program Schedule
8 Hours/Week · 12 Months · 6 Phases
📍
Mode
Online (Live + Recorded) · Hyderabad & Pan-India
📩 Send Enrolment Enquiry

Frequently Asked Questions

Everything you need to know
What is the best supply chain AI certification program?
Mathnal Analytics offers a 12-month, 416-hour Supply Chain AI and Analytics Certification covering 6 phases: analytics fundamentals, Python and SQL, machine learning for supply chain, deep learning and NLP, agentic AI systems, and a capstone project. The program covers demand forecasting, inventory optimisation, transport routing, and S&OP transformation using real industry data. Based in Hyderabad, India.
Who should enrol in the Supply Chain AI & Analytics program?
Supply chain professionals, analysts, planners, engineers and managers who want to master machine-learning forecasting, optimisation, agentic AI and analytics. It suits both those moving into AI-driven roles and leaders who need to direct data science teams with credibility.
What will I be able to do after completing the program?
Build ML demand-forecasting models, formulate and solve optimisation problems (inventory, transport, network), develop Power BI dashboards, apply agentic AI to automate planning workflows, and lead end-to-end supply chain AI projects from data to deployment.
How long is the Supply Chain AI program and what does it cost?
The flagship program runs 12 months (416+ hours) at ₹3,50,000. It is the most comprehensive of Mathnal's offerings, spanning forecasting, optimisation, ML engineering and agentic AI with hands-on capstones.
Do I need a programming background to join?
Basic familiarity helps but is not mandatory — the program builds Python and analytics skills progressively from fundamentals. Motivation and supply chain domain interest matter more than prior coding experience.
How is this different from the CSCOP and FPPSP certifications?
The SC AI & Analytics program is the broadest (12 months, full AI stack). CSCOP (12 weeks) focuses on Python-powered optimisation. FPPSP (16-24 weeks) specialises in factory production planning and scheduling. Choose by depth and focus area.
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