Total units expected to be consumed or sold per year
Fixed cost each time you place an order (shipping, admin, etc.)
Annual cost to store one unit (storage, insurance, depreciation)
Days between placing and receiving an order (for reorder point)
Business operating days per year (default: 250)
EOQ balances two competing costs that move in opposite directions as order size changes:
Order More, Less Often:
Lower ordering costs, but higher holding/storage costs
Order Less, More Often:
Lower holding costs, but higher ordering/shipping costs
EOQ Sweet Spot:
Minimum total cost where ordering cost equals holding cost
EOQ = sqrt(2DS / H)
D = Annual demand (units)
S = Ordering cost per order ($)
H = Holding cost per unit per year ($)
Reorder Point = Daily Demand x Lead Time
Economic Order Quantity (EOQ) is a classic inventory management formula that determines the optimal number of units to order each time you place a purchase order. Developed by Ford W. Harris in 1913 and later refined by R.H. Wilson, the EOQ model finds the order quantity that minimizes the total annual cost of ordering and holding inventory. At the EOQ point, the annual ordering cost exactly equals the annual holding cost -- this is the mathematical sweet spot where total inventory costs are minimized.
The beauty of EOQ lies in its simplicity and practical applicability. While real-world conditions rarely match the model's assumptions perfectly, the formula provides an excellent starting point for inventory decisions. Many businesses use EOQ as a baseline and then adjust for factors like quantity discounts, storage capacity constraints, and demand variability. Even a rough EOQ calculation often reveals that companies are ordering far too much or too little, leading to significant cost savings.
Ordering Costs
Ordering costs are the fixed expenses incurred each time you place a purchase order, regardless of order size. These include purchase order processing, communication with suppliers, shipping and freight charges, receiving and inspection labor, invoice processing, and payment handling. For manufacturers, setup costs (machine changeover, calibration, test runs) replace ordering costs in the formula. Accurately capturing all ordering costs is crucial -- many businesses underestimate them by overlooking hidden administrative overhead.
Holding Costs
Holding costs (also called carrying costs) are the expenses of storing unsold inventory for a period. They typically include warehouse rent or depreciation, utilities, insurance, property taxes, security, opportunity cost of capital tied up in inventory, depreciation, obsolescence, spoilage, and shrinkage. As a rule of thumb, annual holding costs are approximately 20-30% of the inventory's value, though this varies significantly by industry and product type. Perishable goods have much higher holding costs due to spoilage risk.
Reorder Point
The reorder point tells you when to place the next order so that new stock arrives just as current stock runs out. It is calculated by multiplying daily demand by lead time (the number of days between placing and receiving an order). In practice, businesses add safety stock to the reorder point to buffer against demand variability and supplier delays, but the basic calculation provides a solid foundation for inventory planning.
EOQ is widely used across industries. Retailers use it to determine how many units of each SKU to reorder, balancing shelf freshness against ordering efficiency. Manufacturers apply it to raw material procurement, ensuring production lines have steady supply without over-investing in materials. Hospitals use EOQ for medical supplies, where stockouts can have serious consequences but excess storage is expensive and some items have limited shelf life.
The model is particularly valuable when combined with other inventory techniques. Many companies use EOQ alongside ABC analysis (prioritizing high-value items), safety stock calculations (buffering against uncertainty), and just-in-time principles (reducing waste). Modern ERP and inventory management systems often have EOQ calculations built in, automatically suggesting reorder quantities based on historical demand patterns and cost parameters that update in real time.
The classic EOQ model assumes constant and known demand, fixed ordering costs, constant unit purchase price (no quantity discounts), instantaneous replenishment (entire order arrives at once), and no stockouts allowed. In reality, demand fluctuates, suppliers offer volume discounts, orders may arrive in partial shipments, and stockouts do occur. These assumptions mean that EOQ should be treated as a starting point rather than a definitive answer.
Extended EOQ models address many of these limitations. The EOQ with quantity discounts evaluates whether ordering above EOQ to capture a price break reduces total costs. The production order quantity model accounts for gradual replenishment rather than instant delivery. Probabilistic models incorporate demand variability and lead time uncertainty. Despite its simplicity, the basic EOQ formula remains remarkably robust -- studies show that even if input estimates are off by 20-30%, the resulting cost increase is typically only 2-5% above optimal.
Gather accurate cost data before calculating EOQ. Track all costs associated with placing an order (not just shipping) and all costs of holding inventory (not just rent). Many businesses are surprised to find that their true ordering costs are much higher than expected once administrative overhead is included, which shifts the EOQ toward larger, less frequent orders.
Recalculate EOQ periodically as your business evolves. Changes in demand volume, supplier pricing, shipping costs, warehouse capacity, and interest rates all affect the optimal order quantity. Run sensitivity analyses by adjusting inputs by 10-20% to understand how robust your EOQ is to estimation errors. When the EOQ suggests a quantity that conflicts with practical constraints (minimum order quantities, shelf life, storage space), use the formula to evaluate the cost of deviating from optimal and make informed tradeoff decisions.