Mathnal is India's most specialised supply chain AI company. We build ML forecasting, optimisation engines, agentic AI systems, and real-time analytics for inventory, transport, production, warehouse, procurement and S&OP — turning raw data into decisions that save money.
Run a health check on your inventory, forecast accuracy, supply chain risk or cost structure. Each tool produces an actionable scorecard in under 2 minutes.
10-dimension score: safety stock, ABC-XYZ, turns, aging, service level. Upload or enter data — instant results.
Run free audit →MAPE, bias, tracking signal and model selection. Find out if your forecasts are hurting or helping your plan.
Run free audit →Bayesian risk engine with 45 disruption scenarios, Monte Carlo simulation and Value-at-Risk analysis.
New Run simulation →LP/NLP solver for cost, profit and resource allocation. Up to 50 SKUs — Excel-grade answers in seconds.
New Run solver →Click every problem you face. The more you select, the more precise our recommendation.
India's only company exclusively focused on the full supply chain intelligence stack — from descriptive analytics to autonomous AI agents.
Executive dashboards, KPI monitoring, ABC-XYZ segmentation, exception reporting. Python, SQL, Power BI.
Full ML forecasting: ARIMA through LSTM, TFT and foundation models. MLOps, bias correction, auto-retraining.
Multi-agent systems for procurement bots, replenishment agents, risk monitors and S&OP copilots. LangGraph, AutoGen, CrewAI.
Production ML: feature engineering, model deployment, monitoring, retraining. MLflow, Prefect, Docker, FastAPI.
Mathematical optimisation: inventory, transport, production, warehouse, procurement. OR-Tools, PuLP, Pyomo.
Consensus forecasting, scenario simulation, capacity planning, IBP dashboards connecting commercial, supply and finance.
We build forecasting systems at every maturity level — from Excel smoothing to autonomous deep learning pipelines. The right model depends on your data, SKU count and planning process.
Moving averages, Holt-Winters, ARIMA, seasonal decomposition. Works from day one on any demand history.
XGBoost, LightGBM with engineered features — lags, rolling stats, promotions, calendar effects.
LSTM, GRU, Temporal Fusion Transformer for multi-horizon, multi-variate demand at scale.
TimeGPT, Chronos and NeuralProphet — pre-trained models that forecast with minimal historical data.
Every company is at a different maturity level. We build solutions that match where you are today and grow with you.
Production-ready tools you can deploy. Each product includes dashboards, model pipelines and integration support.
Ensemble ML demand forecasting — XGBoost, LSTM, Prophet — with bias correction and demand sensing.
Dynamic safety stock via simulation, Bayesian methods and multi-echelon modelling. Any service level.
Real-time Power BI for 15+ KPIs: OTIF, fill rate, turns, forecast accuracy — with SKU-level drill-down.
Autonomous AI agent: exception monitoring, corrective actions, pre-approved response execution.
VRP, multi-stop routing with time windows, vehicle capacity, multi-depot scenarios for daily dispatch.
Continuous supplier risk scoring — geopolitical, financial, climate, quality. 14-day early warning.
Safety stock, ROP, ABC-XYZ, multi-echelon, SLOB management.
MPS, MRP, finite capacity scheduling, APS, Theory of Constraints.
VRP, CVRP, VRPTW, multi-depot routing, fleet sizing, last-mile.
Slotting, pick-path, wave planning, capacity and labour planning.
Spend analytics, should-cost, sourcing optimisation, supplier risk.
Facility location, DC-store network, centralisation vs local trade-offs.
Consensus forecasting, scenario simulation, IBP, demand-supply matching.
From discovery to continuous optimisation — every engagement follows a structured, sprint-based process with weekly demos and measurable outcomes.
We integrate with your existing ERP, databases and cloud infrastructure. No rip-and-replace — we build on what you have.
ML forecasting: MAPE from 28% to 12%. Safety stock freed $3.2M working capital. OTIF up 6 percentage points.
Read case study →Inventory optimisation across 50,000 SKUs. ABC-XYZ segmentation + dynamic ROP: excess stock reduced 22%.
Read case study →VRP route optimisation for 1,200 daily deliveries. Logistics cost down 12%, on-time delivery 98.5%.
Read case study →Professional training from India's specialist SC AI company. Live cohorts, corporate batches and university partnerships.
Supply chain AI analysis, case studies and Python tutorials. Free, monthly.
Whether you need a diagnostic, a product demo, a consulting engagement or a training proposal — we respond within 2 hours. Tell us what you are working on and we will suggest the right approach.