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Lot sizing is the process of determining the optimal quantity to order or produce in a single batch — balancing the fixed cost of placing an order (setup cost, purchasing admin, minimum shipping fees) against the variable cost of holding inventory over time. The Economic Order Quantity (EOQ) is the most famous lot sizing formula, first derived by Ford Whitman Harris in 1913, and it remains one of the most widely applied formulas in supply chain management. An EOQ/lot sizing calculator helps buyers, planners, and production schedulers find the order quantity that minimizes total annual inventory cost — the sum of ordering cost and holding cost. Total cost is U-shaped as a function of order quantity: very small orders create frequent, high ordering costs; very large orders minimize ordering frequency but drive up holding costs. EOQ is the mathematical minimum of this curve. Beyond EOQ, the calculator also implements periodic-review lot sizing methods (POQ, Lot-for-Lot, Silver-Meal, Part Period Balancing) used in MRP systems for lumpy, time-phased demand. Key inputs are annual demand, ordering cost (cost to place one purchase order or setup one production run), and holding cost rate (annual cost of carrying one unit of inventory, typically 20–30% of unit cost). The calculator outputs optimal order quantity, optimal number of orders per year, optimal order interval, and total annual inventory cost. Sensitivity analysis shows how total cost changes if EOQ assumptions change.
EOQ = √(2 × D × S / H) Where: D = Annual demand (units); S = Ordering/Setup cost per order; H = Holding cost per unit per year = Unit Cost × Holding Rate Total Annual Cost = (D/Q) × S + (Q/2) × H Optimal Orders per Year = D / EOQ Optimal Order Interval = 365 / (D/EOQ) days Reorder Point = (D/365) × Lead Time + Safety Stock
- 1Enter annual demand (D) in units — use 52-week trailing demand or forecast.
- 2Enter ordering cost (S) — cost to place one purchase order: purchasing staff time, system transaction cost, supplier minimum charges, receiving cost per order.
- 3Enter unit cost and holding cost rate (typically 20–30% annually including capital cost, storage, obsolescence, insurance).
- 4Calculate H = Unit Cost × Holding Rate — holding cost per unit per year.
- 5Apply EOQ formula: √(2DS/H) to get optimal order quantity.
- 6Divide annual demand by EOQ to get optimal orders per year; divide 365 by orders/year for order interval in days.
- 7Add reorder point = average demand during lead time + safety stock.
Optimal to order 303 units 8 times per year. Current practice of ordering 500 units 4.8×/year costs $118/year more in holding costs — switching to EOQ saves 7% on inventory costs.
Each production run should produce 6,742 units. With 7.4 runs per year, the production schedule spaces runs approximately every 7 weeks to minimize combined setup and inventory holding costs.
High unit cost (high H) drives EOQ down — it's cheaper to order frequently in small quantities than to carry expensive inventory. Order approximately monthly.
EOQ is the global minimum — but total cost is relatively flat near EOQ (the curve is shallow). Even 65% more than EOQ only costs 6% more — which is why EOQ is robust to small deviations.
Professionals in finance and investment use Lot Sizing Calc as part of their standard analytical workflow to verify calculations, reduce arithmetic errors, and produce consistent results that can be documented, audited, and shared with colleagues, clients, or regulatory bodies for compliance purposes.
University professors and instructors incorporate Lot Sizing Calc into course materials, homework assignments, and exam preparation resources, allowing students to check manual calculations, build intuition about input-output relationships, and focus on conceptual understanding rather than arithmetic.
Consultants and advisors use Lot Sizing Calc to quickly model different scenarios during client meetings, enabling real-time exploration of what-if questions that would otherwise require returning to the office for detailed spreadsheet-based analysis and reporting.
Individual users rely on Lot Sizing Calc for personal planning decisions — comparing options, verifying quotes received from service providers, checking third-party calculations, and building confidence that the numbers behind an important decision have been computed correctly and consistently.
Extreme input values
In practice, this edge case requires careful consideration because standard assumptions may not hold. When encountering this scenario in lot sizing calculator calculations, practitioners should verify boundary conditions, check for division-by-zero risks, and consider whether the model's assumptions remain valid under these extreme conditions.
Assumption violations
In practice, this edge case requires careful consideration because standard assumptions may not hold. When encountering this scenario in lot sizing calculator calculations, practitioners should verify boundary conditions, check for division-by-zero risks, and consider whether the model's assumptions remain valid under these extreme conditions.
Rounding and precision effects
In practice, this edge case requires careful consideration because standard assumptions may not hold. When encountering this scenario in lot sizing calculator calculations, practitioners should verify boundary conditions, check for division-by-zero risks, and consider whether the model's assumptions remain valid under these extreme conditions.
| Annual Demand | Unit Cost | Order Cost | Typical EOQ | Order Frequency |
|---|---|---|---|---|
| 1,000 units | $10 | $50 | 100 units | 10× / year |
| 5,000 units | $25 | $100 | 200 units | 25× / year |
| 10,000 units | $5 | $200 | 1,265 units | 8× / year |
| 50,000 units | $50 | $500 | 1,000 units | 50× / year |
| 100,000 units | $2 | $150 | 3,873 units | 26× / year |
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
In the context of Lot Sizing Calc, this depends on the specific inputs, assumptions, and goals of the user. The underlying formula provides a deterministic relationship between inputs and output, but real-world application requires interpreting the result within the broader context of finance and investment practice. Professionals typically cross-reference calculator output with industry benchmarks, historical data, and regulatory requirements. For the most reliable results, ensure inputs are sourced from verified data, understand which assumptions the formula makes, and consider running multiple scenarios to bracket the range of likely outcomes.
Pro Tips
The 'EOQ insensitivity property' means total cost is within 1% of optimal for order quantities between 0.75× and 1.33× EOQ. Don't obsess over getting the exact EOQ — a reasonable approximation within 25% of EOQ gives nearly the same cost outcome.
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The EOQ formula was independently derived at least 3 times before Harris published it in 1913 — by Andler in 1929 (called the 'Andler formula' in Germany), by Camp in 1922, and by Harris in 1913. It's often called the 'Wilson formula' in the UK after R.H. Wilson who popularized it in the 1920s — a historical attribution error that persists in British textbooks.