What Is Inventory Management?

Just-In-Time is an inventory management philosophy that aims to minimise inventory levels by ordering and receiving goods only as they are needed for production or sale. Originally developed by Toyota in the 1970s as part of the Toyota Production System, JIT has become one of the most widely adopted inventory strategies worldwide. The core principle is simple: holding inventory is waste. Every unit sitting in your warehouse costs money in storage, handling, insurance, and tied-up capital. JIT aims to eliminate this waste by synchronising your purchasing with your actual demand. In practice, JIT requires extremely reliable suppliers, accurate demand forecasting, and efficient logistics. When it works well, JIT dramatically reduces carrying costs and warehouse space requirements. The risk is that any disruption in supply, whether a delayed shipment, a quality issue, or a natural disaster, can immediately halt your operation because there is no buffer stock to fall back on. Many businesses adopt a modified JIT approach, maintaining small safety stock buffers for critical items while applying JIT principles to the rest of their inventory.

ABC analysis is an inventory categorisation method based on the Pareto principle (the 80/20 rule). It divides your inventory into three categories based on their annual consumption value. A-items are the top 10 to 20 percent of products that account for 70 to 80 percent of your total inventory value. These are your most important products and deserve the most management attention: tight stock control, frequent reviews, accurate demand forecasting, and careful safety stock calculations. B-items are the middle 20 to 30 percent of products that account for 15 to 25 percent of your inventory value. These products get moderate management attention. C-items are the remaining 50 to 70 percent of products that account for just 5 to 10 percent of your total inventory value. These are typically managed with simpler controls: larger order quantities, less frequent reviews, and higher safety stock relative to demand because the cost of holding extra stock is low. ABC analysis is powerful because it focuses your limited management time and resources on the items that have the most financial impact. You can further enhance it by combining value-based ABC analysis with volume-based or criticality-based analysis to create a multi-dimensional classification.

Economic Order Quantity is a mathematical formula that calculates the optimal order quantity that minimises the total cost of ordering and holding inventory. The EOQ formula balances two competing costs: ordering costs (the administrative, shipping, and handling costs incurred each time you place an order) and holding costs (the warehousing, insurance, depreciation, and opportunity costs of carrying inventory). The classic EOQ formula is: EOQ = square root of (2 x Annual Demand x Order Cost / Holding Cost per Unit per Year). For example, if you sell 10,000 units per year, each order costs $50 to process, and holding one unit for a year costs $2, then EOQ = square root of (2 x 10,000 x 50 / 2) = 707 units per order. EOQ has limitations: it assumes constant demand, constant lead times, and does not account for volume discounts or storage constraints. However, it provides a useful starting point for order quantity decisions and can be adapted to incorporate these real-world factors. Many inventory management systems, including Frostbyte Pro, can calculate EOQ automatically based on your historical data.

First In, First Out is an inventory valuation and stock rotation method where the oldest inventory items are sold or used first. FIFO is based on the assumption that the first goods purchased or produced are the first goods to leave the warehouse. From a physical stock rotation perspective, FIFO prevents old inventory from sitting in your warehouse indefinitely, which reduces the risk of obsolescence and spoilage. From an accounting perspective, FIFO means that your cost of goods sold (COGS) is based on the cost of the oldest inventory, while the remaining inventory on your balance sheet is valued at the most recent purchase prices. During periods of rising prices, FIFO results in lower COGS and higher reported profits compared to LIFO. FIFO is the most commonly used inventory method worldwide and is required under International Financial Reporting Standards (IFRS). It is straightforward to implement and produces financial statements that accurately reflect the current value of your inventory. For businesses that deal with products that have a limited shelf life, FIFO is the minimum standard for stock rotation, though FEFO (discussed next) is often more appropriate.

First Expired, First Out is a stock allocation method where products with the nearest expiry date are shipped or used first, regardless of when they were received. FEFO is more sophisticated than FIFO because it considers the actual expiry date of each batch rather than just the order of receipt. Consider a scenario where you receive a batch of Product A on January 1 with an expiry date of March 30, and then receive another batch of the same product on January 15 with an expiry date of February 28. Under FIFO, you would ship the January 1 batch first. Under FEFO, you would ship the January 15 batch first because it expires sooner. FEFO is essential for food and beverage, pharmaceutical, and cosmetics businesses where product freshness and safety are critical. It minimises waste from expired products and ensures customers receive products with the maximum remaining shelf life. Implementing FEFO requires tracking expiry dates at the batch level and having an inventory system that can automatically allocate stock based on expiry dates. This is one of the core capabilities of modern inventory management software like Frostbyte Pro. If you are comparing FEFO and FIFO in more detail, see our guide on FEFO vs FIFO.

Last In, First Out is an inventory valuation method where the most recently purchased or produced items are assumed to be sold or used first. LIFO is primarily an accounting method rather than a physical stock management practice. Few businesses would deliberately sell their newest stock before their oldest stock, as this would lead to spoilage and obsolescence. From an accounting perspective, LIFO values COGS at the most recent purchase prices. During periods of rising prices, this results in higher COGS and lower reported profits, which reduces tax liability. This is why LIFO is popular in countries where it is permitted for tax purposes. However, LIFO is not allowed under International Financial Reporting Standards (IFRS) and is therefore not commonly used in New Zealand, Australia, the UK, or the EU. It is primarily used in the United States under US GAAP (Generally Accepted Accounting Principles). For most businesses reading this guide, FIFO or FEFO will be the more relevant and appropriate method.

Safety stock is the buffer inventory held to protect against stockouts caused by variability in demand and supply lead times. Calculating the right safety stock level for each product is critical. Too little and you risk stockouts; too much and you are tying up capital unnecessarily. The basic safety stock formula is: Safety Stock = Z-score x Standard Deviation of Lead Time Demand. The Z-score corresponds to your desired service level. For example, a 95 percent service level uses a Z-score of 1.65, while a 99 percent service level uses 2.33. A more practical formula that accounts for both demand variability and lead time variability is: Safety Stock = Z-score x square root of (Average Lead Time x Demand Variance + Average Demand squared x Lead Time Variance). For example, if your average lead time is 10 days, average daily demand is 20 units, demand variance is 25, lead time variance is 4, and your target service level is 95 percent (Z = 1.65), then Safety Stock = 1.65 x square root of (10 x 25 + 400 x 4) = 1.65 x square root of (1850) = 1.65 x 43 = approximately 71 units. Inventory management software can calculate and dynamically adjust safety stock levels based on your actual historical data, which is significantly more accurate than manual calculations using averages.

A reorder point is the inventory level at which you should place a new purchase order to replenish stock before it runs out. The reorder point formula is: Reorder Point = (Average Daily Usage x Average Lead Time in Days) + Safety Stock. For example, if you sell an average of 15 units per day, your supplier takes 7 days to deliver, and you maintain safety stock of 30 units, your reorder point is (15 x 7) + 30 = 135 units. When your stock of that product drops to 135 units, it is time to place a new order. Reorder point planning is one of the most fundamental inventory management techniques because it automates the timing of purchasing decisions. Instead of relying on someone to manually check stock levels and decide when to order, the system alerts you (or automatically generates a purchase order) when stock hits the reorder point. For businesses with hundreds or thousands of products, manual reorder management is impractical. Modern inventory management software continuously monitors stock levels against reorder points and can generate purchase suggestions or automatic purchase orders when thresholds are reached.

Demand forecasting uses historical sales data, market trends, seasonality patterns, and external factors to predict future demand for your products. Accurate demand forecasting is the foundation of effective inventory management. If you can predict what customers will buy, you can have the right stock in the right quantities at the right time. Basic demand forecasting uses historical averages and trend analysis. More sophisticated approaches use moving averages, exponential smoothing, seasonal decomposition, and regression analysis. The most advanced forecasting models use machine learning algorithms that can identify complex patterns in your data and incorporate external variables like economic indicators, weather patterns, and competitor activity. Common forecasting methods include simple moving average (averages a fixed number of recent periods), weighted moving average (gives more weight to recent periods), exponential smoothing (applies exponentially decreasing weights to older data), and seasonal decomposition (separates trends, seasonal patterns, and irregular variation). The accuracy of your demand forecasting directly impacts every other aspect of inventory management, from safety stock levels to purchasing decisions to warehouse capacity planning.

Batch tracking (also called lot tracking) is the practice of assigning a unique identifier to a group of products that were manufactured, received, or processed together. This identifier, the batch number or lot number, follows the products through every stage of the supply chain, from receipt or production through storage, allocation, and delivery to the end customer. Batch tracking enables full traceability: if a quality issue is discovered, you can trace every product in the affected batch, identify where those products are (in your warehouse, in transit, or already delivered to customers), and initiate targeted recalls rather than broad, costly recalls of all products. For food and beverage manufacturers in New Zealand, batch tracking is a regulatory requirement under MPI (Ministry for Primary Industries) food safety rules. For pharmaceutical and cosmetics businesses, similar traceability requirements apply. Even for businesses where batch tracking is not legally required, it provides significant quality management and customer service benefits. Modern inventory management software makes batch tracking practical by automatically recording batch information at every touchpoint. Learn more about implementing batch tracking for food manufacturers in our detailed guide.