Inventory management in the supply chain is the practice of deciding what to hold, how much, and where across suppliers, warehouses and stores, so that customers get what they need without tying up more cash and space than necessary. It combines classification, reorder rules, safety stock and regular measurement.
The task is a trade-off: too little stock causes stockouts, too much raises holding cost and obsolescence risk.
Types of inventory
- Raw materials and components: inputs waiting to be used in production.
- Work in progress: items partly through manufacturing.
- Finished goods: products ready to sell.
- Cycle stock: the portion needed to meet normal demand between replenishments.
- Safety stock: extra stock held to cover variation in demand or supplier lead time.
- Pipeline stock: goods in transit between locations.
Classifying items
ABC analysis ranks items by annual usage value. A items are the few that account for most of the value and get the closest control. C items are the many low-value items that can be managed with simple rules. XYZ analysis adds demand variability: X items are steady, Z items are erratic. Combining both helps decide which items deserve tighter forecasting and which can use simple min-max rules.
Reorder methods compared
| Method | How it works | Suits |
|---|---|---|
| Reorder point | Order when stock falls to a set level | Items with fairly steady demand |
| Periodic review | Check stock at fixed intervals and order up to a target | Many items ordered from the same supplier |
| Min-max | Order up to the max when stock falls to the min | Low-value, simple items |
| Kanban or pull | Replenish only what has been consumed | Repetitive production or stable flow |
| Forecast-driven (MRP/DRP) | Plan orders from forecast demand and lead times | Items with planned or lumpy demand |
Reorder point and safety stock
The basic reorder point formula is average demand during lead time plus safety stock. Safety stock grows with demand variability, lead-time variability and the service level the business wants to achieve. Higher service levels need disproportionately more stock, so targets should differ by item importance rather than being set at one level for everything.
The economic order quantity (EOQ) balances ordering cost against holding cost for a single item. It is a starting point, and practical constraints like supplier minimums, pallet quantities and shelf life often override it.
Metrics that show results
- Inventory turnover: cost of goods sold divided by average inventory.
- Days of supply: how long current stock would last at expected demand.
- Fill rate and stockout rate: how often demand is met from stock.
- Inventory accuracy: how closely system records match physical counts.
- Slow-moving and obsolete stock: the share of inventory with little or no recent demand.
Common problems
- Records that do not match physical stock, which undermines every other rule.
- One service target applied to all items.
- Parameters set once and never reviewed as demand or lead times change.
- Forecast bias that keeps pushing stock up or down.
- Inventory held in the wrong location while another site runs short.
Where AI and analytics help
Machine learning can improve demand forecasts, estimate lead-time variability from receipt history, and flag items whose parameters no longer fit. Optimization tools can set safety stock by item and location. They rely on clean transaction history and sensible service goals, and planners still decide how to handle new products, promotions and supply disruptions.
Common questions
What is a good inventory turnover?
It depends on the industry and product type. Compare with the same business over time and with similar companies rather than a general target.
How often should safety stock be reviewed?
Whenever demand patterns or supplier performance change, and at least on a regular schedule such as quarterly for important items.