A robust numbering system is the cornerstone of SKU analysis. Each SKU number should contain vital information, such as product type, model number, and unique identifiers. This systematic approach ensures clarity and consistency across your inventory.
Step 1: Data Collection
Begin by gathering comprehensive data on each SKU in your inventory. This includes dimensions, weight, cost, demand history, and lead times.
Step 2: Item Grouping
Categorize SKUs based on shared attributes. This could involve grouping by product type, size, or seasonal demand. This step lays the foundation for deeper analysis.
Step 3: Demand Patterns
Analyzing historical demand patterns for each SKU involves examining the past sales data for a particular product. This analysis helps to identify various patterns and trends in demand over time. By studying the historical data, businesses can gain insights into the seasonal trends, spikes, and lulls in demand for each SKU.
Seasonal trends refer to the recurring patterns in demand that occur during specific times of the year. Some products are more popular with festivals or events during holidays or specific months, leading to increased demand. Businesses can adjust their procurement and stocking strategies by identifying these seasonal trends. They can ensure that they have sufficient stock of the SKU during peak demand periods and avoid overstocking during slower periods.
Spikes in demand refer to sudden and significant increases in the demand for a particular SKU. These spikes can occur because of various factors such as promotions, marketing campaigns, or external events. By analyzing historical data, businesses can identify these spikes and plan their procurement and stocking strategies to meet the increased demand. This may involve increasing the order quantities or adjusting the production schedules to ensure the timely availability of the SKU.
On the other hand, lulls in demand refer to periods of low or decreased demand for a particular SKU. These lulls can occur during certain times of the year when there is typically lower consumer interest or during economic downturns. By analyzing historical data, businesses can identify these lulls and adjust their procurement and stocking strategies accordingly. They can reduce the order quantities or adjust the production schedules to avoid excess inventory during these periods.
Analyzing historical demand patterns for each SKU provides valuable insights that guide procurement and stocking strategies. Businesses can manage their inventory better by knowing when demand is high or low. This helps them avoid running out or having too much. This analysis helps businesses effectively meet customer demand and improve efficiency.
Step 4: Turnover Rates
To calculate turnover rates for each SKU, you need to determine the number of times a particular item is sold or replaced within a specific time period. This measure is important for knowing how well your stock sells and which items are popular or unpopular.
To calculate the turnover rate, you can use the formula:
Turnover Rate = Cost of Goods Sold (COGS) / Average Inventory Value
First, you need to calculate the COGS for each SKU. This involves determining the total cost of acquiring or producing the goods sold during a specific period. This includes the cost of raw materials, labor, and any other expenses directly associated with producing or acquiring the goods.
Next, you need to calculate the average inventory value for each SKU. This involves adding the beginning and ending inventory values for a specific period and dividing them by two. To find the inventory value, multiply the quantity of each SKU in stock by its unit cost.
After obtaining the COGS and average inventory value for each SKU, you can input these values into the turnover rate formula. The resulting turnover rate will indicate how well a particular SKU sells within the given period.
A high turnover rate suggests that an item is in high demand and sells quickly. This is beneficial as it indicates that the item is generating revenue and not tying up capital in inventory for an extended period.
A low turnover rate means the item is not selling fast. This can tie up money in inventory and make it more expensive to keep and, therefore, less profit.
By calculating turnover rates for each SKU, you can gain valuable insights into your inventory management. This information can help you decide which items to prioritize, whether to increase or decrease stock levels, and how to optimize your inventory to maximize profitability.
Step 5: Pareto Analysis
Pareto Analysis is a powerful tool that helps prioritize SKUs based on their impact on overall sales and margins. It follows the 80/20 rule, which states that 80% of the sales come from 20% of the SKUs. By identifying this vital 20%, you can focus your efforts on optimizing their performance.
To conduct a Pareto Analysis, start by sorting your SKUs in descending order based on their contribution to sales. Then, calculate the cumulative percentage of sales for each SKU as you move down the list. This will help you identify the point at which the cumulative percentage reaches 80%.
To prioritize, focus on the top 20% of products. Ensure their availability, set the right price, and improve their marketing. By doing so, you can maximize your sales and bottom line.
On the other hand, the remaining 80% of SKUs may require a different approach. These SKUs might have lower sales volumes or contribute less to your overall revenue. It is important to analyze the performance and determine if any adjustments will improve the profit of those items. This could involve reducing their inventory levels, conferring supplier contracts, or exploring alternative sourcing options.
By conducting a Pareto Analysis, you can make informed decisions about allocating resources and arranging your efforts. This analysis helps you understand your product range better so you can focus on important items and improve inventory management.
Step 6: Technology Integration
Leverage advanced inventory management software to streamline SKU analysis. Automation speeds up data processing and enhances accuracy.
