Mathnal runs 16 supply chain training programs in Python, SQL, Power BI and Excel — covering demand forecasting, inventory optimisation, transport routing, procurement and production planning. Answer the four short questions below and we’ll recommend the course that best fits your current skills, your goals and the time you have.
Mathnal Analytics runs 16 specialised supply chain training programs across four tool tracks — Python, SQL, Power BI and Excel — applied to real supply chain problems rather than toy datasets. Programs span demand forecasting (ARIMA, exponential smoothing, XGBoost, LSTM, Prophet), inventory optimisation (safety stock, reorder point, ABC-XYZ, multi-echelon), transport optimisation (VRP, VRPTW, CVRP with OR-Tools), procurement analytics, production planning & scheduling (MPS, MRP, finite capacity) and end-to-end supply chain AI.
Whether you are an individual professional upgrading from Excel to Python, a university embedding analytics into an MBA or B.Tech curriculum, or a corporate L&D team upskilling planners, the navigator below recommends the right program for your starting point, focus area, role and time budget.
Forecasting, optimisation, automation and ML for supply chain.
Query ERP, POS and CRM data; build clean analytics pipelines.
DAX modelling and live KPI dashboards for planning teams.
Advanced forecasting and inventory models — no code required.
Wherever you land on the map, every program runs the same four-stage loop — concept before tooling, always ending in something you can show leadership.
The business logic and frameworks first. No tool opens until the "why" is clear.
Live coding on real supply chain datasets. Everyone builds a working solution in-session.
Apply skills to a case study or your own company data — a dashboard, model or optimisation.
30 days of post-training code review, feedback and deployment guidance.
Seats are limited per batch. Register early to secure your spot.
Everything you need to know about Mathnal's supply chain training programs.
Browser-based, no signup. Great warm-ups before a program — and useful on the job.
Tell us your team, your data and your goals. We'll respond within 24 hours with a tailored proposal, pricing and a recommended program path — for individuals, universities or corporate L&D.
Need help choosing a course, or want to discuss corporate / university batches? Tell us what you need.
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Every engagement follows our integrated DAPP → RESO methodology: from understanding your business problem through domain analysis, data engineering, algorithms and prediction — to risk-adjusted optimisation, simulation and executable decisions. A closed learning loop ensures continuous improvement.
Start from the supply chain business problem — not the technology. Understand the decision, its frequency, constraints. Engineer data into decision-ready features. Select algorithms because of the problem, not fashion. Generate predictions with uncertainty, then prescribe candidate recommendations.
Take DAPP's candidate recommendation and stress-test it. Is it the risk-adjusted optimum? How does it perform under realistic variability and disruption scenarios? Who decides, what threshold, what human oversight? Then execute, measure outcome, and feed learning back to domain.
Every execution outcome feeds back into the domain layer. Models retrain, risk scores recalibrate, decision thresholds adapt. This is not a one-time project — it's a continuously improving decision system.
How our delivery maps to the framework
Data engineering, algorithm selection, model training — the DAPP backbone.
DAPP PhaseForecasting engines, inventory optimisers, risk monitors — productised DAPP outputs.
DAPP → RESODomain analysis, risk assessment, decision design — the full DAPP → RESO arc.
Full LifecycleOptimisation, simulation, dashboards, training — enabling your team to operate the loop.
RESO + Learning