Efficient inventory management is the backbone of any successful business, ensuring the right products are available at the right time while minimizing costs and waste. For inventory managers, tracking the right metrics is crucial to maintaining this delicate balance. With so much data at your fingertips, knowing which key performance indicators (KPIs) to focus on can make the difference between streamlined operations and costly inefficiencies.
In this blog post, we’ll explore three essential metrics that every inventory manager should be tracking (and two they should consider) to optimize stock levels, improve turnover, and drive profitability. Whether you’re looking to reduce stockouts, cut carrying costs, or enhance customer satisfaction, these metrics provide actionable insights to help you achieve your goals.
Inventory turnover is a vital statistic in calculating the health of your inventory operations. It shows the frequency in which inventory is sold and replenished over a period of time. High turnover can be both an indicator of good sales and good inventory management practices while a lower number may indicate a problem with overstocking or slow-moving items.
Calculating inventory turnover is a simple formula. For a period of time, divide the cost of goods sold (COGS) by the average inventory on hand during that time. This will result in the number of times the inventory turned over during that period. For example, a company had a cost of goods sold of $500,000 for a given product. During that time, the amount of inventory on hand averaged $100,000. So, dividing $500,000 by $100,000 gives us an inventory turnover ratio of five—meaning the inventory turned over five times during that timeframe. (For this example, we’ll assume 365 days.)
So, is five considered a good or bad turnover ratio? It depends. For example, for grocery or convenience store items, turning inventory five times a year would probably be considered a pretty low number; these types of products would ideally turn over each month (giving a turnover ratio of 12 or greater.) However, for manufacturing inventory like raw materials and production items, a turnover ratio of five might be considered better than average.
Understanding this number provides several advantages. First, cash flow and profitability are impacted as resources typically tied up with inventory are freed up for other business operations. From a financial planning perspective, knowing inventory turnover allows finance to accurately predict the speed at which a business can convert inventory into cash. Finally, demand forecasting is incredibly more precise as inventory turnover can reveal trends in buying patterns allowing for greater and easier replenishment strategies.
Being unable to fulfill orders due to missing inventory is a serious impediment to inventory operations. Stockouts can be financially expensive as well as hurt the reputation of the inventory operation. So, it stands to reason that understanding the rate at which certain items become unavailable could help in better planning and replenishment operations.
The stockout rate is just the percentage that results from dividing stockouts by the total number of orders. For example, if you have 130 stockout items and 1000 total orders, the stockout rate is 13%. In this way, stockout metrics can be calculated from a warehouse level down to a product level.
Like the inventory turnover ratio, this number is somewhat subjective depending on the item and industry. For example, for fast-moving consumer goods (FMCG) and retail and e-Commerce, a stockout rate of anything higher than 3% is considered a critical failure and leads to lost sales and high customer dissatisfaction. However, manufacturing and heavy equipment production accept a much higher stockout rate due to the complexity of supply chains and long production cycles.
According to a study by the IHL Group, inventory distortion (stockouts and overstocking) cost retailers nearly $1.8 trillion USD in 2023. While no single piece of software can solve the entire problem, implementing modern inventory management software will help identify and mitigate these types of inventory distortions. Reducing stockouts, for example, can increase overall customer satisfaction, optimize inventory levels, reduce expedited shipping costs, and increase demand planning efficiency.
Order accuracy is a simple metric. Many organizations look at accuracy to determine the performance of the inventory operation. However, within this metric lies a great deal of information that can be used to move the needle on several pieces of the overall business operations.
Order accuracy is just the ratio of successfully completed orders against total orders within the operation. This is typically expressed as a percentage (like, 95% accuracy.) It is determined by dividing the total number of accurate orders by the total number of orders and multiplying by 100. So, aside from how well the inventory operation is performing, what does this number tell us?
For starters, the number can reflect the overall operational discipline within the organization. Order filling mistakes can arise from a variety of things like mislabeled products, misplaced items, and poor stock location identification. These things can be mitigated with the use of modern inventory management software and mobile scanning. Knowing the state, location, and authenticity of the inventory increases the overall visibility of the stock and reduces the dependence on paper as a workflow driver tool within the operation.
Additionally, order accuracy rates can reveal how well the warehouse is designed and organized. Higher accuracy rates often indicate a well thought out, efficient warehouse design. Efficient warehouse designs often implement clear labeling and signage, compact aisles, and cross docking. A modern, cloud-based inventory management system provides the tools necessary to achieve a fully optimized warehouse design.
Two additional metrics that are important for inventory managers to consider are Demand Variability and Product Obsolescence Rate. Each of these are part of a broader product lifecycle analysis (PLA) process which is critical for inventory managers as they align their strategies with the different stages of a product's life. By understanding how a product evolves from introduction to obsolescence, organizations can optimize inventory levels, reduce waste, and maximize profitability.
Product lifecycle is typically broken down into four phases: Introduction, Growth, Maturity, and Decline. At each step, inventory managers are presented strategies on how to handle inventory levels and replenishment goals. For example, during the Growth phase, inventory managers would be looking to increase stock based on demand, would continue to work with suppliers to ensure consistent availability of products, and modify safety stock to avoid stockouts.
Demand variability is a critical metric tracked during these phases. It helps predict the consistency of customer demand for a product over a period of time and informs the planning of inventory levels, safety stock, and production schedules. Calculating the demand variability requires taking the sum of all demand and dividing by the period of time. This mean demand number is used in a standard deviation calculation to arrive at a demand variability percentage. A low variability percentage indicates consistent and predictable demand, which can result in lower inventory carrying costs. A higher variability percentage indicates volatility in demand and therefore may require higher safety stock to prevent stockouts and maintain order completion accuracy.
Modern, cloud-based inventory management systems have the tools necessary to calculate this number based on the history of order processing.
Products with very short lifecycles often become obsolete very quickly. Understanding the rate at which inventory becomes unsellable is a key metric indicating how well an organization is managing its inventory. The simple calculation is to take the value of obsolete inventory and divide by total inventory value. This percentage is the rate at which inventory becomes unsellable.
Naturally, a lower percentage is desirable, as an organization would like to maintain inventory that is ready to be sold and not being discarded. If an organization is struggling with higher obsolescence rates, there are several remedies available.
First, improve demand forecasting by using historical data and predictive analytics to align inventory with market demand. Secondly, implement a FIFO (first-in-first-out) strategy to prevent aging inventory. Modern inventory management systems can help route order pickers to the appropriate first-out stock to keep that process in compliance. Finally, another option is to provide the inventory aging data to the sales organization to develop discounts or campaigns to clear excess inventory.
All inventory managers want to be the most efficient, most optimized, and help drive the bottom line. Implementing software with interactive reporting to help accentuate these data points will help any organization looking to improve its processes. While each of these metrics are available for manual calculation, taking advantage of an inventory-focused enterprise platform with native machine learning to help predict and make sense of these metrics will certainly propel an inventory operation into a new level of discipline and efficiency.
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