Inventory forecasting is the process of estimating future demand for a company’s products or services and determining the necessary level of inventory to meet that demand.

An accurate forecast enables businesses to make informed decisions about production, purchasing, and stock levels, which can help to improve overall efficiency and profitability.

There are a number of different methods that can be used to forecast inventory, and the most appropriate approach will often depend on the type of business and available data. In this post, we will cover some of the most common methods used for inventory forecasting, including:

- Trend analysis (moving averages and trend lines)
- Regression analysis
- Exponential smoothing

We’ll go over the pros and cons of each of these methods, the exact formulas you need to set them up, and which to choose for your particular business.

We’ll also leave you with some inventory forecasting best practices and common pitfalls to avoid.

With all that said, let’s brace ourselves for some math and jump right in!

**The Importance of Accurate Forecasting**

Inaccurate inventory forecasting can have a number of negative consequences for businesses, including:

Stock-outs: If demand is underestimated and inventory levels are too low, businesses may find themselves unable to meet customer demand, which can lead to lost sales and a subsequent dip in customer satisfaction.

Excess inventory: On the other hand, if demand is overestimated and inventory levels are too high, businesses may end up holding onto excess stock that ties up financial resources and takes up valuable storage space.

**Common Methods for Inventory Forecasting**

As we mentioned above, there are a number of different methods that can be used for inventory forecasting. In this section, we will take a closer look at some of the most common methods, including trend analysis, regression analysis, and exponential smoothing.

**Trend Analysis**

One of the simplest and most common methods for inventory forecasting is trend analysis. This approach involves using historical sales data to identify any underlying trends in demand, which can then be used to make predictions about future demand.

There are a number of different ways to carry out trend analysis, but one of the most common is to use a moving average. This approach involves calculating the average demand over a set period of time (e.g., 3 months, 6 months, etc.) and then using this figure to forecast demand for future periods.

Another popular method is to use trend lines. This involves plotting historical data points on a graph and then drawing a line of best fit through the data. The slope of the trend line can then be used to estimate future demand.

**How to calculate a moving average**

The formula for calculating a moving average is:

MA = (Sum of data points over the past n periods) / n

where MA is the moving average and n is the number of periods.

For example, if we wanted to calculate the 3-month moving average for January, February, and March, we would use the following formula:

MA = (January + February + March) / 3

Let’s say sales in Q1 were 100, 150, and 200 for each consecutive month.

This would give us an MA of (100 + 150 + 200) / 3, or 150.

We can then use this moving average to forecast demand for future periods. For example, if we wanted to forecast demand for April, we would simply use the moving average figure of 150.

**Trend analysis formula**

The formula for trend analysis is:

Trend = (Ending value – Starting value) / Number of periods

For example, if sales in Q1 were 100, 150, and 200 for each consecutive month, the trend would be calculated as follows:

Trend = (200 – 100) / 3

This would give us a trend of 100/3, or 33.33.

This trend can then be used to forecast demand for future periods. For example, if we wanted to forecast demand for April, we would simply use the trend figure of 33.33 and add it to the sales figure for March (200). This would give us a forecasted demand of 233.33 for April.

**When to use trend analysis vs. moving average for inventory forecasting**

There are a few things to consider when deciding whether to use trend analysis or a moving average for inventory forecasting.

The first is the length of time over which you want to forecast demand. If you only need to forecast demand for a short period of time (e.g., 1-2 months), then using a moving average is usually sufficient. However, if you need to forecast demand further into the future (e.g., 3-6 months), then using trend analysis will give you a more accurate picture of future demand.

The second thing to consider is the nature of the data. If the data points are evenly spaced out, and there is a clear trend, then trend analysis is the best approach. However, if the data points are more erratic or there is no clear trend, then using a moving average is usually a better option.

**Regression Analysis**

Regression analysis is a statistical method that can be used to identify relationships between different variables. This approach is often used in inventory forecasting to identify relationships between sales and other factors that may affect demand, such as seasonality, advertising, and price changes.

Once these relationships have been identified, they can be used to make predictions about future demand.

**Regression analysis formula**

The formula for regression analysis is:

Y = a + bX

where Y is the dependent variable (i.e., demand), X is the independent variable (i.e., advertising spend), a is the intercept, and b is the slope of the line.

To put this in context, let’s say we want to use regression analysis to predict demand for a product based on advertising spend.

If we have historical data on advertising spend and demand, we can use this data to calculate the values of a and b.

It’s possible to calculate the values of a and b manually, but if you want to save yourself some time, HubSpot has an in-depth tutorial on how you can use Excel or Google Sheets formulas to shortcut all the math.

Once you have the values of a and b, you can plug them into the formula to predict demand for any given level of advertising spend.

For example, let’s say we’ve calculated that a = 100 and b = 0.5. This would give us the following equation:

Y = 100 + 0.5X

If we wanted to predict demand for a month where we’re planning to spend $10,000 on advertising, we would simply plug this figure into the equation:

Y = 100 + 0.5(10,000)

This would give us a forecasted demand of 5,100 units.

**When to use regression analysis**

The main advantage of regression analysis is that it can help you identify relationships between different variables. This is useful if you want to forecast demand based on factors other than sales history, such as seasonality or price changes.

However, regression analysis does have a few disadvantages. First, it can be time-consuming to set up, especially if you’re not comfortable with statistical methods.

Second, regression analysis is only as accurate as the data that you use to calculate the values of a and b. If your data is inaccurate or incomplete, your predictions will also be inaccurate.

Finally, regression analysis only works if there is a linear relationship between the dependent and independent variables. If there is no clear relationship, or the relationship is non-linear, then regression analysis will not be accurate.

**Exponential Smoothing**

Exponential smoothing is a forecasting method that assigns exponentially decreasing weights to past observations. This approach is often used when there is no clear trend in the data or when there is a significant amount of random variation.

There are a number of different variants of exponential smoothing, but the most common is simple exponential smoothing. This approach involves using a weighted moving average to forecast demand, where the weights exponentially decrease as you move further back in time.

**Exponential smoothing formula**

The formula for simple exponential smoothing is:

Ft = αyt + (1-α)Ft-1

where Ft is the forecast for period t, yt is the actual demand for period t, and α is the smoothing constant.

The value of α can range from 0 to 1, and the larger the value, the more weight is given to recent observations.

**Choosing the right value for α**

The value of α will have a big impact on the accuracy of your predictions, so it’s important to choose a value that makes sense for your data.

If you choose a value that is too high, your forecasts will be very sensitive to recent observations and will not take into account long-term trends.

On the other hand, if you choose a value that is too low, your forecasts will be sluggish and will not reflect short-term changes in demand.

There is no right or wrong answer when it comes to choosing the value of α. The best approach is to experiment with different values and see which gives you the most accurate predictions.

You can also use statistical methods, such as the Mean Absolute Percentage Error (MAPE), to compare the accuracy of different values of α.

Once you’ve selected a value for α, the forecasting process is relatively simple.

You simply need to plug in the actual demand values for each period and solve the equation to get the forecasted demand.

Let’s say we want to use exponential smoothing to predict demand for a product over the next six months.

If our data shows that demand has been relatively stable over the past few months, we might choose a value of α = 0.5. This would give us the following equation:

F1 = 0.5y1 + (1-0.5)F0

F2 = 0.5y2 + (1-0.5)F1

…

F6 = 0.5y6 + (1-0.5)F5

To predict demand for the first month, we would simply plug in the value of y1 (i.e., actual demand for the first month).

To predict demand for the second month, we would plug in the value of y2 and the forecasted demand for the first month (i.e., F1).

And so on.

**How to Choose the Right Method for Your Business**

Choosing the right inventory forecasting method for your business will depend on a number of factors, including the type of business, the products you sell, and the data that is available.

If you are selling products that have a clear seasonal pattern, then trend analysis or regression analysis may be the best option. If you are selling products that are not affected by seasonality, then exponential smoothing may be a better choice.

It is also important to consider the data that is available when choosing a forecasting method. If you have only a few months of sales data, then a simple trend analysis may be the best option. If you have a long history of sales data, then more sophisticated methods, such as regression analysis, may be more appropriate.

Ultimately, the best way to choose a forecasting method is to experiment with different methods and see which one gives the most accurate predictions for your business.

No matter what method you choose, it is important to remember that inventory forecasting is an inexact science. There will always be some uncertainty when predicting future demand, so it is important to have some flexibility in your planning.

One way to do this is to create multiple forecast scenarios using different methods or assumptions. This will give you a better idea of the range of possible outcomes and help you to make more informed decisions about inventory levels.

**Consider inventory lead time in your forecasts**

Another important consideration is the lead time for your products. Lead time is the amount of time that it takes to receive an order from your supplier.

If you have a long lead time, then you will need to place orders further in advance, which means that you will need to be more accurate in your forecasting. If you have a short lead time, then you will have more flexibility in your ordering.

To account for lead time, you can either adjust your forecast to reflect the amount of time it will take to receive an order, or you can build up inventory levels to cover the lead time.

Building up inventory levels can be expensive, so it is often preferable to adjust your forecasts. This means that you will need to place your orders earlier, but it will save you the cost of holding excess inventory.

**Common mistakes to avoid in inventory forecasting**

There are a few common mistakes that can lead to inaccurate inventory forecasts. These include:

Not taking seasonality into account: Seasonal patterns can have a big impact on demand, so it’s important to consider them when forecasting inventory levels.

Relying too heavily on historical data: Historical data is a valuable tool, but it is important to remember that it only reflects past demand. Future demand may be different, so it is important to consider other factors, such as market trends and customer behavior, when forecasting inventory.

Failing to account for changes in the business: Businesses change over time, so it is important to keep this in mind when forecasting inventory. If you are introducing new products or making changes to your marketing strategy, this will impact demand and should be taken into account when forecasting inventory.

Making assumptions without data: It is important to base your forecasts on data rather than assumptions. If you don’t have enough data to support your forecast, it is better to wait until you do rather than guessing.

Failing to review and update your forecast: The demand for your products may change over time, so it is important to review and update your forecast on a regular basis. This will help you to stay accurate and responsive to changes in the market.

By avoiding these mistakes, you can improve the accuracy of your inventory forecasts and make better decisions about inventory levels.

**How does sales forecasting correlate with inventory forecasting?**

Sales forecasting is a key input into inventory forecasting. By predicting future sales, you can estimate the level of inventory you will need to meet demand.

Sales forecasting can be done using a variety of methods, such as trend analysis or regression analysis. It is important to choose a method that is appropriate for your business and data. Once you have a sales forecast, you can use it to estimate the level of inventory you will need to meet demand.

**Using software to manage your inventory forecasts**

SkuVault is a powerful inventory management software that can help you with forecasting inventory demand (in addition to many other facets of good inventory control).

SkuVault provides features such as sales history and trend analysis, which can be used to predict future sales.

SkuVault also allows you to set up reorder points so that you can automatically place orders when inventory levels reach a certain point. This takes the guesswork out of ordering and helps you to maintain optimal inventory levels.

In addition, SkuVault provides features such as real-time inventory tracking and reporting, which can help you to stay on top of your inventory and make informed decisions about future orders.

If you are looking for a way to improve your inventory management, we’d love to show you how SkuVault can help you stop working *in *your business and start working *on *your business.

For more information, click the button on this page to schedule a demo, or check out our features page here.