Free Supply Chain Diagnostic Tools — How They Work
Mathnal's free diagnostic suite gives supply chain and finance teams instant, evidence-based answers — five browser-based tools that need no signup and keep your data on your device.
What supply chain tools are included?
The suite includes the Inventory Health Check (safety stock, reorder point, ABC-XYZ, dead-stock detection across 10 dimensions); the Forecast Accuracy Audit (MAPE, bias, model selection); the Risk & Resilience Simulator (Bayesian risk, Monte Carlo, VaR across 45 scenarios); the Optimization & Simulation solver (LP/NLP for production, transport and procurement); and the Family Expense Simulator (household budgeting with inflation and macro stress tests).
Are these tools really free?
Yes — all five are completely free, run entirely in your browser, and require no signup or data upload. They are built to demonstrate Mathnal's methodology in inventory, forecasting, risk and optimisation. For production-scale deployments on your own data, Mathnal builds custom engines.
Who uses Mathnal's diagnostic tools?
Supply chain planners, demand planners, inventory and procurement managers, operations analysts, students and small-business owners who want fast, defensible answers without expensive enterprise software.
Frequently Asked Questions
Everything you need to know
How do I check my supply chain inventory health for free?
Use the Mathnal Inventory Health Check at mathnal.tech/diagnose_supply_chain.html. Upload your CSV data and get a 10-dimension health score covering safety stock, ABC-XYZ classification, reorder points, dead stock detection, and service level analysis — all free, browser-based, no signup required.
What is a good MAPE for demand forecasting?
MAPE benchmarks: below 10% is excellent, 10-20% is good, 20-30% is fair, above 30% needs improvement. Mathnal's free Forecast Accuracy Audit auto-detects your data patterns and selects the best forecasting model from 7+ algorithms.
How can I simulate supply chain risk without expensive software?
The Mathnal SC Risk & Resilience Simulator (SCRRS) is a free browser-based tool with a Bayesian risk engine, 45+ disruption scenarios, Monte Carlo simulation, and Value-at-Risk analysis. No signup, no data leaves your browser.
What is Bayesian risk analysis in supply chain?
Bayesian risk analysis uses prior probabilities updated with new evidence (via Bayes' theorem) to compute posterior risk probabilities. In supply chain, it calculates P(Stockout), P(Delay), and P(Overstock) by combining historical performance with live disruption signals from geopolitical, climate, supplier, and logistics events.
What are the three free supply chain diagnostic tools from Mathnal?
Mathnal offers three free browser-based diagnostic tools: (1) Inventory Health Check — 10-dimension scoring with safety stock, ABC-XYZ, and reorder point analysis; (2) Forecast Accuracy Audit — pattern detection, model selection, MAPE and bias analysis; (3) SC Risk & Resilience Simulator — Bayesian risk engine with Monte Carlo simulation and VaR.
How can I optimize supply chain costs using linear programming for free?
Use the Mathnal SC Optimization & Simulation Tool at mathnal.tech/sc_optimization_tool.html. It offers a browser-based LP/NLP solver that optimizes cost, distance, volume, profit, or revenue across up to 50 SKUs. Supports production planning, transport, procurement, and full supply chain optimization with constraints like budget, warehouse capacity, and service level targets — free, no signup.
What is the difference between linear and non-linear optimization in supply chain?
Linear programming (LP) assumes proportional cost relationships — doubling quantity doubles cost. Non-linear programming (NLP) accounts for economies of scale, volume discounts, and diminishing returns where costs decrease per unit at higher volumes. Mathnal's free optimization tool supports both LP and NLP solvers for realistic supply chain planning.
How do I calculate safety stock for a target service level?
Safety stock is calculated using SS = Z × √(LT × σd² + d² × σLT²), where Z is the service factor (1.65 for 95%, 2.33 for 99%), LT is average lead time, σd is demand standard deviation, d is average demand, and σLT is lead time standard deviation. Mathnal's free Inventory Health Check calculates this automatically from your CSV data.
What is ABC-XYZ classification in inventory management?
ABC-XYZ is a dual classification matrix. ABC ranks items by value contribution (A=top 80%, B=next 15%, C=bottom 5%). XYZ ranks by demand variability (X=stable/predictable, Y=moderate variation, Z=highly erratic). Combined, the 9-cell matrix (AX, AY, AZ, BX... CZ) guides differentiated stocking policies. Mathnal's free tool generates this matrix automatically.
What are the best free supply chain diagnostic tools available online?
Mathnal Analytics offers four free, browser-based diagnostic tools requiring no signup: (1) Inventory Health Check — 10-dimension score with safety stock, ABC-XYZ, dead stock detection; (2) Forecast Accuracy Audit — 7+ model comparison with MAPE and bias analysis; (3) SC Risk & Resilience Simulator — Bayesian risk engine with Monte Carlo and VaR; (4) SC Optimization & Simulation — LP/NLP solver for cost, profit, volume optimization. All run 100% in your browser with zero data uploaded.
How does Monte Carlo simulation work in supply chain risk analysis?
Monte Carlo simulation generates thousands of random scenarios by sampling from probability distributions of uncertain variables (demand, lead time, supplier reliability, disruption probability). Each iteration produces a different outcome. The distribution of outcomes quantifies risk — for example, Value-at-Risk (VaR) at 95% confidence shows the maximum expected loss in 95% of scenarios. Mathnal's free SCRRS tool runs 10,000+ Monte Carlo iterations with 45+ disruption scenarios.
What is forecast bias and how do I detect it?
Forecast bias is systematic over-forecasting or under-forecasting. A positive bias means consistently forecasting too high (leading to excess inventory); negative bias means forecasting too low (causing stockouts). Tracking signal (cumulative forecast error ÷ MAD) detects persistent bias — values outside ±4 indicate significant drift. Mathnal's Forecast Accuracy Audit calculates bias and tracking signal automatically.