Understanding Economic Order Quantity (EOQ)
Every inventory manager faces a fundamental dilemma: order too much and you waste money on storage, insurance, and tied-up capital. Order too little and you waste money on frequent shipping, handling, and administrative costs. The Economic Order Quantity formula finds the mathematical sweet spot where your total inventory costs are minimized, balancing these competing forces to deliver the lowest possible annual cost.
Developed by Ford W. Harris in 1913 and later refined by R.H. Wilson, the EOQ model has been the foundation of inventory management for over a century. Despite its age, it remains remarkably relevant because it captures a fundamental truth about supply chain economics: there is always a trade-off between ordering costs and holding costs. Companies that master this trade-off gain a significant competitive advantage through reduced working capital requirements and lower operational expenses.
This calculator uses the Wilson EOQ formula to determine your optimal order quantity, then visualizes the cost trade-offs in the iconic U-shaped cost curve. You will see exactly where your ordering costs and holding costs intersect to form the valley of minimum total cost. The visual representation makes it easy to understand why the EOQ point is optimal and how far you can deviate before costs rise significantly.
How to Use This Tool
Enter Annual Demand
Input the total number of units you need per year. This should be your average annual demand based on historical data or sales forecasts, not peak demand. For seasonal products, use the average across all seasons.
Set Order Costs
Enter the cost per order including shipping, handling, admin time, receiving costs, and any fixed fees regardless of order size. This does not include the product cost itself—only the cost of placing and receiving an order.
Enter Unit Cost & Holding %
Set the purchase price per unit and your annual holding cost as a percentage. Holding cost typically ranges from 15-30% and includes storage, capital opportunity cost, insurance, taxes, and obsolescence risk.
Click Calculate EOQ
View your optimal order quantity, number of orders per year, reorder point based on lead time, and the cost curve visualization showing exactly where total cost is minimized.
Understanding Your Inputs
Annual Demand (D)
Total units you expect to need this year. Use historical data or forecasts. For new products, estimate conservatively. This is the primary driver of how many orders you need to place annually.
Order Cost (S)
Fixed cost per order: shipping fees, admin time, receiving costs, inspection, and processing. This does not include the product cost. Higher order costs push EOQ higher to reduce order frequency.
Holding Cost % (H)
Annual cost to hold one unit in inventory as a percentage of unit value. Includes storage rent, capital opportunity cost (typically 8-15%), insurance, taxes, and obsolescence risk (5-20% for tech products). Industry average is 20-25%.
Lead Time
Days from placing an order to receiving it. Used to calculate your reorder point—the inventory level at which you should place your next order to avoid stockouts during the lead time period.
Reading Your Results
Optimal Order Quantity
The number of units to order each time to minimize total annual inventory costs. This is where the U-curve reaches its lowest point.
Orders Per Year
How many times you need to place an order annually. This equals Annual Demand divided by EOQ. Fewer orders mean lower ordering costs but higher holding costs.
Reorder Point
The inventory level at which you should place a new order. This accounts for lead time so the new shipment arrives just as you run out.
Total Inventory Cost
The combined annual cost of ordering and holding inventory at the EOQ level. This is the minimum possible cost given your inputs.
Pro TipAdd Safety Stock for Real-World Variability
The EOQ formula assumes perfectly constant demand and 100% reliable lead times. In reality, both fluctuate. Add safety stock of 1-2 weeks of average demand above your reorder point to protect against stockouts during demand spikes or supplier delays. For high-variability items or critical products, consider using statistical methods like standard deviation analysis to calculate optimal safety stock levels.