Six AI-powered supply chain products. Explore each — see the problems they solve, the dashboards they power, and the results they deliver.
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 + LearningEnsemble ML demand forecasting using XGBoost, LightGBM, LSTM, and Prophet — with automated bias correction, demand sensing, tracking signal alerts, and uncertainty quantification. Replaces Excel-based forecasting with a system that learns and improves every cycle.
Request a Demo → Talk to UsA national FMCG distributor was running monthly forecasts on Excel for 14,000 SKUs across 8 distribution centres. MAPE averaged 38%, with undetected over-forecast bias of 15% on seasonal products causing $2.4M in annual excess inventory.
Mathnal deployed the SC Forecasting Engine with XGBoost + Prophet ensemble, external signal integration (weather, festivals, POS), and automated tracking signal monitoring. Within 4 months, MAPE dropped to 19%, bias was corrected to within ±3%, and safety stock was reduced by 22% — freeing $2.4M in working capital.
Automatically tests 6+ models per SKU and selects the best performer based on out-of-sample RMSE.
Integrates weather, POS, Google Trends, and economic indicators for short-horizon correction.
Tracking signal, CFE, and MPE monitored weekly. Auto-alerts when |TS| > 4.
Measures whether each process step improves or degrades accuracy. Eliminates waste.
Prediction intervals at 80% and 95% confidence for risk-aware safety stock calculation.
Built on scikit-learn, statsmodels, PyTorch. Deployable on AWS, Azure, or on-premise.
Dynamic safety stock using simulation, Bayesian methods, and multi-echelon modelling. Calculates optimal buffers at any service level — and tells you exactly how much working capital you can free.
Request a Demo →Carrying $10.3M in inventory with a fill rate of 93%. Frequent stockouts on C-category parts despite excess stock in A-category. Mathnal's Inventory Optimisation Engine recalculated safety stock using demand CV, lead time variability, and Bayesian probability. Result: 31% SS reduction ($3.2M freed), fill rate improved to 98.5%, and 47 at-risk SKUs identified that traditional methods completely missed.
Real-time Power BI dashboards for 15+ KPIs — OTIF, fill rate, inventory turns, forecast accuracy, lead time, warehouse utilisation — with SKU-level drill-down and automated threshold alerts.
Request a Demo →Replaced 23 manual Excel reports with a single live dashboard. Decision latency dropped from 12 to 3 days. OTIF improved 6 percentage points within first quarter as teams could see and act on exceptions in real time.
Autonomous AI agent that monitors exceptions, generates corrective actions, and executes pre-approved responses — from reorder triggers to supplier escalations. Your planners focus on strategy; the Copilot handles the noise.
Request a Demo →Deployed SC Copilot to monitor supply exceptions across 6,200 SKUs. 55% of exceptions auto-resolved. Planner capacity freed 40%, redirected to strategic sourcing. Decision latency dropped from 12 days to under 4 hours for critical alerts.
VRP and multi-stop route optimisation with time windows, vehicle capacities, and multi-depot scenarios. Produces actionable daily dispatch plans that reduce cost and improve delivery performance.
Request a Demo →Optimised vehicle routing across 8 distribution centres serving 2,400 delivery points. Transport cost reduced 12% ($890K annually). Vehicle utilisation improved from 68% to 87%. On-time delivery rate improved from 89% to 96%.
Continuous supplier risk monitoring with geopolitical, financial, climate, and quality risk scoring. Early warning system that detects disruptions 14+ days before they hit your supply chain.
Request a Demo →Identified financial distress in a critical single-source supplier 14 days before delivery failure. Alternative supplier activated within 72 hours. Prevented $1.8M in production downtime. Now monitors 340 suppliers across 12 risk dimensions continuously.
12 weeks · 96 hours · Python-powered optimisation (LP, MIP, VRP) · 15+ case studies from Amazon, P&G, DHL, Maersk · Capstone + industry panel
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