This repository contains an Excel-based inventory planning model that demonstrates fundamental inventory control logic, including Economic Order Quantity (EOQ), safety stock, and reorder point calculations.
The purpose of this model is to make inventory planning logic transparent and inspectable. Many ERP and inventory management systems perform these calculations behind configuration screens. This workbook shows how demand assumptions, lead-time variability, service levels, and cost inputs translate into practical replenishment decisions.
This model is not intended to replace an ERP or production inventory system. It is designed as a practical learning and portfolio project for understanding how inventory policy decisions are calculated.
The model illustrates how common supply chain planning inputs are used to answer three basic inventory questions:
- How much should be ordered?
- When should inventory be reordered?
- How much safety stock is needed to reduce stockout risk?
It emphasizes transparency, interpretability, and traceability of calculations over automation or algorithmic complexity.
The workbook includes:
- Economic Order Quantity (EOQ) calculations
- Annual ordering cost and holding cost comparisons
- Safety stock estimation using demand and lead-time variability
- Service level assumptions by product
- Reorder point (ROP) logic
- Visual summaries of EOQ cost breakdown and safety stock requirements
- Notes explaining assumptions, formulas, and limitations
The EOQ section calculates an order quantity that balances ordering cost and holding cost. This helps demonstrate the tradeoff between ordering too often and carrying too much inventory.
The safety stock section estimates additional inventory needed to protect against demand and lead-time uncertainty. The model uses service level assumptions and variability measures to estimate stock buffers by product.
The reorder point section combines expected lead-time demand with safety stock to determine when inventory should be replenished.
This model is intended for:
- Educational purposes
- Scenario analysis
- Inventory planning demonstrations
- Supply chain and operations portfolio work
- Explaining replenishment logic in a transparent format
It is especially useful for showing how basic inventory planning formulas support operational decision-making.
This model is intentionally simplified. It does not include:
- ERP system integration
- Automated replenishment recommendations
- Supplier capacity constraints
- Dynamic pricing
- Seasonality
- Multi-location inventory planning
- Real-time demand updates
Results depend on the quality and representativeness of the demand, lead-time, cost, and service level assumptions used in the workbook.
This project complements the demand forecasting Excel model by using demand uncertainty and forecast error concepts as inputs for inventory policy decisions.
Together, the forecasting and inventory planning models demonstrate how demand planning can support replenishment, safety stock, and reorder point decisions.