Inventory, production, warehouse, transport, procurement, risk, and S&OP — each with dedicated ML models, problem-solution flows, case studies, and quantified ROI.
Static 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 →