What is Inventory Forecasting in E-commerce?
Where 10-minute delivery is becoming the new norm, having the right stock present at the right time is more critical than ever. Having too much inventory takes up a lot of your working capital and risks obsolescence, while too little inventory can lead to lost sales and loss in frustrated customers. This trickery is what inventory forecasting for e-commerce aims to solve.
Inventory forecasting for e-commerce is the practice of forecasting future product demand to optimize inventory levels. It predicts how much of each product you'll need in the following weeks, months, or years based on past sales data, market trends, seasonality, promotions, and other pertinent criteria. Effective E-Commerce demand forecasting enables firms to make more educated decisions regarding purchasing, storage, and general inventory management solutions.
If you want to learn about all the benefits and challenges of inventory forecasting for e-commerce, take a look at Inventory Forecasting: Challenges, Benefits, and Best Practices.
Key Metrics for Ecommerce Inventory Forecasting
Inventory forecasting for e-commerce is highly dependent on tracking and analyzing metrics and KPIs. These metrics provide in-depth and valuable insights like the number of customers who land on your website, the number of products left in the cart, or the click-through rates you’re getting for your promotional emails! Monitoring these KPIs is essential for accurate e-commerce demand forecasting and optimizing your inventory management solutions. Here are some crucial metrics to consider:
Sell-Through Rate
The sell-through rate measures the % of inventory sold within a specific period. A high % sell-through rate indicates strong demand for your product, while a low rate suggests overstocking or slow-moving products in your inventory. This is crucial for Inventory Forecasting for E-commerce as it shows how well your inventory is performing.
Sell Through Rate = (Quantity of Stock on Hand/ Number of Units Sold) ×100
What is a good sell-through rate? You’d definitely ask this: a healthy sell-through rate typically falls between 40-80%, with rates above 80% considered excellent for most e-commerce businesses.
Lead Time Demand
This KPI measures the expected demand during lead time. What is lead time? It is the time between placing an order with your supplier and receiving the order. Understanding lead-time demand to prevent stockouts is of the utmost importance, especially when your supply chain is complex and lengthy. Lead-time demand is a key component of e-commerce demand forecasting.
Lead Time Demand = Average Lead Time in Days x Average Daily Sales
Reorder Point
The reorder point is the inventory level at which you need to place a new order to avoid stockouts. Reorder Point is designed to help businesses determine the optimal time to reorder products to maintain an uninterrupted supply chain. Here is the formula:
Reorder Point (ROP) = Lead Time Demand + Safety Stock (extra inventory held as a buffer against unexpected demand fluctuations)
Inventory Turnover Rate
The inventory turnover rate is the measure of how many times your total inventory is sold and replaced within a specific period. These turnover ratios usually indicate effective inventory management if they finish at a high number. Conversely, the same would imply overstocking or slow-moving items if the number is low. This is one of the most important metrics in inventory forecasting for e-commerce, and it describes the efficiency of inventory.
Inventory Turnover Rate: Cost of Goods Sold (COGS) / Average Inventory
Stockout Rate
This means how often a product runs out of stock. High stock-out rates can lead to lost sales and unsatisfied customers. Minimization of the stock-out rate is a key objective for successful Inventory Forecasting for E-commerce.
Stockout Rate = (Number of Stockout Occurrences / Total Number of Order Opportunities) * 100%
Carrying Costs
Carrying Costs are the cost of maintaining an inventory and include items such as storage and insurance, obsolescence, and capital costs. They must be understood because they directly affect inventory levels and expenses. Inventory management solutions such as Omniful aim to reduce carrying costs.
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Step 1: Calculate the Inventory Holding Sum
- Inventory Holding Sum = Capital Costs + Warehousing Costs + Inventory Service Costs + Inventory Risk Costs
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Step 2: Calculate the Carrying Cost Percentage
- Carrying Cost (%) = (Inventory Holding Sum / Total Value of Inventory) * 100
Demand Forecasting Accuracy
This is the most relevant indicator that quantifies the degree of accordance between your forecasts and real sales. Monitoring forecast accuracy lets you pinpoint areas for improvement in your Inventory Forecasting for E-commerce workflow and improve your predictive analytics in E-commerce planning.
Formulas
In the field of supply chain management, Demand Forecasting Accuracy is one of the key parameters that assesses the predictive capacity of the inventory forecasting techniques in use. In order to measure the level of accuracy, there are several measures; here are a few of the most common ones:
1. Mean Absolute Percentage Error (MAPE)
Formula: MAPE = (1/n)*Σ| (Actual Demand - Forecasted Demand)/Actual Demand|*100
Where:
n = number of periods
Σ = sum of
| | = absolute value
Explanation: MAPE is defined as the average of all absolute scaled percentage errors of forecasted values where the scales are the actual figures against which the forecasts are made. This ratio is used broadly, perhaps because it has an intuitive appeal and is easy to explain and understand. However, this measure can be affected by outliers; for example, actual demand values that are very small or very close to zero make it problematic.
2. Mean Absolute Deviation (MAD)
Formula: MAD = (1/n)*Σ|Actual Demand - Forecasted Demand|
Explanation: The absolute difference is taken to eliminate any possible negative values. In this case, MAD measures the average absolute difference between the forecasted value and the actual value. Compared with MAPE, MAD is more primitive and is not greatly influenced by extremes. However, it is less easy to understand visually because it may not be expressed as a percentage.
3. Weighted Mean Absolute Percentage Error (WMAPE)
Formula: WMAPE = Σ | Demand – Forecasted Demand | / Σ Demand * 100
Explanation: The WMAPE functions similarly to MAPE but incorporates total actual demand into its calculations. Consequently, this allows for reduced sensitivity towards individual periods characterized by low demand levels, granting a broader insight into overall accuracy amidst fluctuating demands.
Choosing the Right Formula
The optimal formula is specific to both the desired outcome and the properties of the data.
- If you need a simple and easily interpretable metric and your data doesn't have many extreme values or zero values, MAPE might be a good choice.
- In order to reduce the impact of outliers, MAD is a preferable choice.
- If your data's accuracy measurement is vulnerable to long periods of low demand, then WMAPE is ideal.
One more reminder that a single indicator of measure is far too imperfect to be used alone. Actually, it may be beneficial to use multiple metrics to obtain a more complete picture of your forecasting accuracy. Track these metrics on your inventory management solution, and you will gain insights that improve your e-commerce inventory forecasting and inventory decisions.
Time-Series Analysis for Ecommerce Demand Forecasting
Given the ever-changing nature of online retail, a precise E-Commerce demand forecasting system is paramount. Time series analysis is a major application of the various techniques currently adopted and relevant to inventory forecasting for E-commerce.
What is Time-Series Analysis?
Time-series analysis involves analyzing data points collected over time. In the case of Inventory Forecasting for E-commerce, this could be historical sales data, website visitors, and so on, all of which are acquired at regular intervals (e.g., daily, weekly, monthly). Based on these time-series data, businesses can uncover hidden patterns, including:
Trends: Long-term upward or downward movements in the data. E.g., a steady growth in sales of a given product for a number of years is evidence of an upward trend). This is vital for Inventory Forecasting for E-commerce.
Seasonality: Repetitive patterns occur within a set timeframe, like sales increases due to holidays or seasons. Seasonality detection is of great significance for the demand forecasting of e-commerce as well, especially for seasonality products.
Cyclical Patterns: Variations in the data that occur at generally longer intervals of time, usually related to economic cycles or market trends. They help formulate longer-term Inventory Forecasting for E-commerce.
Random Noise: Refers to changes in the data, often utterly random in occurrence, and are not explained by any pattern. While noise is something that cannot be avoided, the goal of predictive analytics in E-commerce is to minimize its impact.
How Time-Series Analysis Improves Ecommerce Demand Forecasting
With time-series analysis techniques, e-commerce businesses can increase the precision of their Inventory Forecasting for e-commerce. The main advantages are:
- Improved Accuracy: The time-series models might capture complex patterns in historical data, leading to more accurate E-commerce demand forecasting than simpler methods could provide.
- Enhanced Supply Chain Efficiency: By forecasting future demand, companies can optimize supply chain processes, moving from sourcing to delivery. This is a crucial issue in inventory forecasting for e-commerce.
- Data-Driven Decision Making: Time-series analysis generates data-driven insights to inform strategic decisions on purchasing, marketing, and the like. This is one of the essential capabilities of predictive analytics in E-commerce.
- Better Inventory Management Solutions: Accurate demands enable businesses to ensure optimal inventory levels, thus reducing stock-outs and overstocking. This has a direct bearing on improving the effectiveness of inventory management solutions.
How Inventory Forecasting Impacts Supply Chain and Order Fulfillment
Accurate inventory forecasting in e-commerce has a major impact on supply chain efficiency and order fulfillment processes. Accurate demand predictions ensure that each aspect of the supply chain, from sourcing to distribution, is appropriately managed. This improves customer satisfaction and stimulates business growth.
Effective demand forecasting in e-commerce enables the company to order the correct quantity of products at the right moment. This minimizes the risk of overstocking or stockout, diminishing waste and improving resource usage. This is one of the main benefits of implementing efficient inventory management software.
Accurate Inventory Forecasting for E-commerce is essential for minimizing lead times. Knowing the demand makes sure a business can work with its suppliers to ensure delivery takes place on time. This minimizes delays in order fulfillment and spares customers from delays in their expected delivery. One of the main functions of predictive analytics in E-commerce is streamlining supply chain flow.
In addition, when Inventory Forecasting for E-commerce is accurately predicted, customer satisfaction will be directly improved. When products are consistently stocked, companies can deliver orders quickly and prevent frustrating customers with "out of stock" messages or delayed deliveries. The excellent customer experience builds customer loyalty and repeats customer business.
Final Thoughts
Proactive inventory management practices should be followed to stay ahead in the constantly evolving e-commerce industry. Inventory Forecasting for e-commerce offers companies a wealth of advantages. Just picture a future where stockouts don't even exist, lead times are shortened, and customer happiness skyrockets—that's the potential of a good e-commerce demand prediction. With the right tools and methodologies, your supply chain can be transformed efficiently into a bustling factory, providing a seamless click-through-to-delivery service.
Omniful.ai is a top inventory management solution that uses AI to offer predictive analytics to the E-commerce community. Our platform integrates perfectly with existing systems, providing you with insights to assist in making informed decisions and optimizing inventory levels. Don't let guesswork dictate how your inventory strategy works. Take charge and fully take control of your e-commerce business. Get in touch with us today to see how Omniful.ai can help attain success in inventory forecasting for e-commerce.
