Inventory, transport, warehouse, production, procurement, risk and S&OP — mathematical optimisation and ML, configured for how retail, e-commerce, manufacturing, agri, F&B and fashion supply chains actually behave.
Generic supply chain advice fails because retail demand doesn't behave like a harvest, and a garment size-curve is nothing like a production schedule. Pick your industry — see the exact pains we solve, what we deploy, and the numbers it moves.
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 + LearningStatic safety stock formulas assume normal distribution and constant parameters. Reality is different — demand is lumpy, lead times vary, risk profiles differ by SKU. Our simulation-based and Bayesian methods calculate the exact buffer needed at any service level, freeing trapped working capital while preventing stockouts.
Get Free Assessment →See ProductCarrying $10.3M inventory with 93% fill rate. Frequent stockouts on C-parts despite excess A-stock. Mathnal deployed simulation-based SS with Bayesian risk scoring. Safety stock reduced 31% ($3.2M freed), fill rate improved to 98.5%, and 47 hidden at-risk SKUs identified.
Free assessment — we'll quantify your savings potential to the dollar.
Request Free Assessment →MRP logic ignores real-time capacity constraints, changeover dependencies, and yield variability. Our constraint-based scheduling considers machine availability, changeover matrices, yield predictions, and demand priority simultaneously — producing executable plans, not theoretical ones.
Talk to Us →Plan adherence was 81%, changeover averaging 45 minutes, OEE at 72%. Mathnal deployed CP-SAT scheduling with changeover matrix and predictive maintenance. Adherence improved to 94%, changeover dropped to 35 minutes, OEE reached 85%. Overtime reduced 35%.
We'll assess your scheduling and identify the top 3 improvement levers.
Request Assessment →Fast-movers stored far from pick zones. Inefficient pick paths. Manual quality inspection. Reactive labour scheduling. We fix all of it — using ML-driven slotting, computer vision, and demand-based workforce planning that transforms warehouse throughput.
Talk to Us →Pick rate was 120 lines/hour, inventory accuracy 96.4%, and dock wait times averaging 90 minutes. Mathnal deployed slotting optimisation, CV quality checks, and demand-based labour scheduling. Pick rate improved to 150 lines/hour, accuracy reached 99.2%, dock wait dropped to 50 minutes, and labour costs reduced 30%.
Free slotting analysis — we'll show you exactly where the efficiency is hiding.
Request Warehouse Audit →Dispatchers plan routes by experience — resulting in suboptimal stop sequences, 60–70% vehicle utilisation, excess fuel, and missed delivery windows. Our VRP algorithms generate mathematically optimal routes in minutes, handling time windows, capacity constraints, and multi-depot scenarios.
Get Route Analysis →See ProductManual routing across 8 DCs serving 2,400 points. Freight cost $74K/month, vehicle utilisation 68%, on-time delivery 89%. Mathnal deployed VRPTW optimisation. Cost reduced 12% ($890K/year), utilisation hit 87%, on-time improved to 96%.
Upload your delivery data — free route optimisation analysis in 48 hours.
Get Free Route Analysis →Spend is fragmented across categories with no visibility. Supplier selection is relationship-driven, not data-driven. Contract compliance is tracked manually. We deploy NLP for spend classification, ML for supplier scoring, and LP for optimal allocation — revealing savings invisible to manual analysis.
Request Spend Analysis →Spend fragmented across 380 suppliers with no category taxonomy. Mathnal deployed NLP classification, supplier scoring, and TCO modelling. Identified 15% savings ($6.3M), consolidated 40% of supplier base, and reduced sourcing cycle from 12 weeks to 4 weeks.
Free spend cube analysis — we'll classify your PO data and show savings in 5 business days.
Request Spend Analysis →Supplier disruptions are discovered only after they hit. Single-source dependencies go unmonitored. Risk assessments happen annually, if at all. We deploy continuous ML-based risk scoring across financial, geopolitical, climate, and quality dimensions — detecting disruptions 14 days before impact.
Request Risk Assessment →See ProductSingle-source supplier for a key raw material showed no visible problems. Mathnal's risk monitor detected financial distress signals (payment delays to their sub-suppliers, credit downgrade) 14 days before a delivery failure. Alternative supplier activated in 72 hours. Prevented $1.8M in production downtime. Now monitors 340 suppliers across 12 dimensions.
Free risk assessment of your top 20 suppliers — scores delivered in 5 business days.
Request Risk Assessment →Most S&OP processes generate slides, not decisions. Sales says one thing, operations plans another, finance budgets a third. We redesign S&OP from scratch — structured monthly cadence, AI-driven scenario planning, consensus forecasting with FVA, and a single dashboard connecting demand, supply, and financial planning.
Request S&OP Assessment →S&OP was a monthly slide deck with no structured cadence. Sales, ops, and finance operated on different numbers. Mathnal implemented 4-week S&OP with FVA, scenario planning, and a consensus dashboard. Forecast accuracy improved 15%, OTIF rose from 88% to 94%, and ad-hoc decision-making dropped 70% within two quarters.
Free S&OP maturity assessment with a roadmap to structured planning.
Request S&OP Assessment →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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