Supply Chain Optimization
& Simulation

Supply chain optimization uses mathematical programming (linear and non-linear) to find the best allocation of resources — minimizing costs, maximizing profits, or optimizing volumes — across production, transport, and procurement decisions. It considers constraints like capacity, demand, budget, and lead times to produce actionable plans.

Use this Mathnal Supply Chain Optimization Tool. It supports linear and non-linear optimization for cost, distance, volume, profit, and revenue across up to 50 SKUs. Upload your data or enter it manually. It runs 100% in your browser — no signup, no data leaves your machine.

Linear optimization assumes all relationships are proportional — doubling production doubles cost. It's fast and guarantees a global optimum. Non-linear optimization handles curved relationships like economies of scale, diminishing returns, and volume discounts. It's more realistic but may find local optima. This tool supports both approaches.

Five objectives: cost minimization, distance minimization, volume maximization, profit maximization, and revenue maximization. Four modes: production planning (volume/profit), transport optimization (cost/distance), procurement optimization (cost/volume), and full supply chain optimization (profit/revenue). All support up to 50 SKUs with configurable constraints.

Twelve columns: SKU_ID, SKU_Name, Unit_Cost, Selling_Price, Demand, Min_Order, Max_Capacity, Lead_Time_Days, Transport_Cost_Per_Unit, Distance_KM, Supplier, Weight_KG. Download the template from the Data Input step. A sample dataset with 15 SKUs is also built in for testing.

Absolutely. All calculations run in JavaScript in your browser. No data is transmitted to any server. Your supply chain data never leaves your device. You can verify this in your browser's Network tab — zero outbound requests.

The shadow price (dual value) tells you how much the objective function would improve if you relaxed a binding constraint by one unit. For example, if the budget shadow price is ₹2.5, adding ₹1 to your budget would improve profit by ₹2.5. It helps prioritize where to invest for maximum impact.

The Simplex method is an iterative algorithm that solves linear programs by traversing vertices of the feasible region. It's guaranteed to find the global optimum in polynomial time for practical problems. This tool implements a JavaScript-based Simplex solver inspired by PuLP's approach, running entirely in your browser.

Yes. Select "Production Planning" mode with volume or profit objective. Enter your SKU data with production costs, capacities, and demand. The solver finds the optimal production quantity for each SKU that maximizes throughput or profit while respecting capacity and budget constraints.

Transport optimization mode minimizes total transport cost or distance. It uses per-unit transport costs, distances, and weight data for each SKU to determine optimal shipment allocations. Constraints include maximum network distance, weight capacity, and per-SKU demand fulfillment requirements.