Inventory Optimization Model

Dashboard

A high-level overview of your entire inventory operation at a glance. The dashboard aggregates key metrics from all other sheets and presents them through charts and KPI cards. Covers total inventory value & turnover rate, stock status breakdown (healthy / warning / critical), top items by value and volume and monthly trend charts for key metrics.

Assumptions

Central configuration sheet where you define the core parameters that drive all calculations across the workbook. Covers holding cost percentage, ordering cost per purchase order, lead time defaults, service level targets and working days per year.

Inventory Data

The master data sheet where all your SKU information lives. This is the foundation that feeds every analysis in the workbook. Covers sku details (name, category, unit cost), annual demand figures, current stock levels, supplier lead times and demand variability metrics.

ABC Analysis

Automatically classifies your inventory into A, B, and C categories based on the Pareto principle (80/20 rule), helping you focus attention on the items that matter most. Covers automatic revenue-based classification, cumulative percentage calculations, visual pareto chart and category summary statistics.

EOQ Calculations

Implements the Economic Order Quantity formula to determine the optimal order size for each SKU, minimizing the total cost of ordering and holding inventory. Covers classic eoq formula implementation, optimal order quantity per sku, annual ordering cost calculations, annual holding cost calculations and total cost minimization.

Safety Stock & ROP

Calculates safety stock levels and reorder points for each item based on demand variability, lead time, and your desired service level. Covers statistical safety stock calculation, service level-based approach (z-score), reorder point for each sku, lead time demand analysis and buffer stock recommendations.

Cost Analysis

Provides a detailed breakdown of all inventory-related costs, helping you understand where your money is going and where savings opportunities exist. Covers holding cost breakdown, ordering cost analysis, stockout cost estimates, total cost of ownership per sku and cost comparison charts.

Scenario Analysis

Lets you model "what-if" scenarios to understand how changes in demand, lead time, or costs would impact your inventory strategy. Covers demand increase/decrease scenarios, lead time variability modeling, cost sensitivity analysis, side-by-side scenario comparison and risk assessment for each scenario.

Instructions

A built-in guide that walks you through the entire workbook — from entering your data to interpreting the results and making decisions. Covers step-by-step setup guide, formula explanations, best practices for data entry and interpretation guidelines.

Economic Order Quantity (EOQ)

EOQ = √(2DS / H)

Where D = annual demand, S = ordering cost per order, H = holding cost per unit per year. This classic formula finds the order quantity that minimizes total inventory costs.

Reorder Point (ROP)

ROP = (d × L) + SS

Where d = average daily demand, L = lead time in days, SS = safety stock. Tells you exactly when to place a new order.

Safety Stock

SS = z × σd × √L

Where z = service level factor, σd = standard deviation of daily demand, L = lead time. Provides a statistical buffer against demand uncertainty.

Inventory Turnover

Turnover = COGS / Average Inventory

Measures how many times your inventory cycles through per year. Higher turnover generally indicates more efficient inventory management.

1.Gather and Structure Your Inventory Master Data

The foundation of any inventory optimization effort is clean, comprehensive master data. For each SKU, you need unit cost, annual demand (or demand history from which to calculate it), supplier lead time, and demand variability. Many organizations discover that this data is scattered across ERP systems, spreadsheets, and tribal knowledge. Investing time upfront to centralize and validate this data pays enormous dividends — every downstream calculation depends on it. Pay particular attention to demand variability, which is often not tracked at all but is critical for safety stock calculations.

2.Classify Inventory Using ABC Analysis

Not all inventory items deserve equal attention. ABC analysis applies the Pareto principle to rank items by annual consumption value (unit cost × annual demand). Typically, "A" items represent 10-20% of SKUs but 70-80% of total value, "B" items are the next 20-30% of value, and "C" items are the long tail. This classification drives differentiated management strategies: A items warrant tight control, frequent review, and optimized ordering; C items can be managed with simpler rules and larger safety buffers. Without this prioritization, organizations waste analytical effort optimizing low-value items while high-value items run on autopilot.

3.Calculate Economic Order Quantities

The EOQ formula finds the order size that minimizes total annual inventory costs — the sum of ordering costs (which decrease as order size increases) and holding costs (which increase as order size increases). The classic formula is EOQ = √(2DS/H), where D is annual demand, S is the cost per order, and H is the annual holding cost per unit. While the formula assumes constant demand and lead time (rarely true in practice), it provides a powerful starting point. For most items, the total cost curve is relatively flat near the optimum, meaning that practical constraints (minimum order quantities, container sizes) can be accommodated without significant cost penalty.

4.Set Safety Stock and Reorder Points Statistically

Safety stock protects against two sources of uncertainty: demand variability and lead time variability. The standard formula is SS = z × σd × √L, where z is the service level factor (e.g., 1.65 for 95% service level), σd is the standard deviation of daily demand, and L is lead time in days. The reorder point equals average demand during lead time plus safety stock. The key insight is that safety stock is a deliberate trade-off between service level and carrying cost — higher service levels require disproportionately more safety stock. Setting this trade-off explicitly (rather than using gut feel) is what separates data-driven inventory management from reactive firefighting.

5.Model Scenarios and Continuously Improve

Inventory optimization is not a one-time calculation — it is a continuous process. Build scenario models to understand how changes in demand, lead time, or costs would impact your inventory strategy. What happens if a key supplier's lead time doubles? How would a 20% demand increase affect your stockout risk? These scenarios help you prepare contingency plans and stress-test your parameters. Review and recalibrate quarterly: update demand forecasts, refresh lead time data, recalculate EOQ and safety stock, and evaluate whether your ABC classifications still hold. The businesses that treat inventory optimization as an ongoing discipline consistently outperform those that set parameters and forget them.