An accurate inventory forecast is invaluable, especially in times when supply chains and consumer demand are changing rapidly. Getting forecasts right requires a mix of data analysis, experience in the industry and customer insights to metaphorically peer into the crystal ball and predict future demand.

Market forces can change quickly, and something as seemingly insignificant as a product placement by a Tik-Tok influencer could clear out your stock in seconds.

Factors can be major or peripheral. Some factors could have a significant impact on revenue, while some have only a mild effect. Essential data elements required for accurate inventory forecasting include the following:

  • Current inventory levels
  • Outstanding purchase orders
  • Historical trendlines
  • Forecasting period requirements
  • Expected demand and seasonality
  • Maximum possible stock levels
  • Sales trends and velocity
  • Customer response to specific products

On a strategic level, forecasters need insights into organizational goals, local and global supply chain challenges, planned marketing pushes and campaigns, potential media influences and the competitive landscape.

What Is Inventory Forecasting?

Inventory forecasting — also known as demand planning — is the practice of using past data, trends and known upcoming events to predict needed inventory levels for a future period. Accurate forecasting ensures businesses have enough product to fulfill customer orders while not tying up cash in unnecessary inventory. Forecasting is more than just setting a reorder point — it’s using data analysis to identify patterns and trends to adapt to dynamic conditions and meet customer demand. Reorder points are one important piece, but there is much more to inventory forecasting.

Inventory Forecasting vs. Replenishment

With inventory forecasting, you calculate the amount of the different types of inventory necessary for future periods. Factors include replenishment data such as timing, availability and delivery speed — also known as lead time. Replenishment is the stock required to meet inventory forecasts based on inventory goals, supply and demand.

Key Takeaways

  • Forecasting demand helps you keep enough product on hand while not wasting valuable storage space on unnecessary products.
  • Various formulas can help you get started by identifying how long it takes for products or component parts to arrive to you after you order, at what point you should reorder stock, and how much stock you should have on hand to meet peaks in demand.
  • Many factors can affect demand for your product. Some of them are external, such as a storm affecting shipping times, and some are internal — such as a marketing campaign that drives up demand.
  • Sophisticated inventory management software can help you automate inventory forecasting, as well as other tasks, such as setting reorder points.

Inventory Forecasting Explained

Inventory forecasting uses data to drive decision making. It’s the application of information and logic to make sure you have enough product on hand to meet customer demand without overdoing it and ordering too much that you then must pay to warehouse. Forecasters are creating more complex tools like advanced computer-based simulations and futures markets to create demand forecasts.

Inventory forecasting starts with the simple step of looking at how much product your company has sold over time and making sure you have enough to continue meeting customer demand. Next, you start to add complexities, such as seasonality and trend forecasting. Is demand growing? Do you see a spike in demand in a specific season? Other factors are then added, such as planned marketing campaigns and other events that may affect sales. Sophisticated demand forecasting usually occurs with the aid of inventory management software.

What Are the Types of Inventory Forecasting?

Even though gut feelings and experience can play a role to some degree, the most efficient forecasting relies on data and formulas. There are different methods and approaches to these formulas.

The most common formulaic methods for successful inventory forecasting are trend, graphical, qualitative and quantitative. Choose the best method based on known stocking issues, personal insights, feedback from sales, customer input, mathematical analysis and market research.

Trend forecasting: Trends are changes in demand for a product over time. This method projects possible patterns and excludes seasonal effects and irregularities using past sales and growth data. More granular sales data helps this forecasting technique by showing how specific customers, as well as types of customers, will likely purchase in the future. Analysts can find new ways to market and offer sales from this data.

Graphical forecasting: The same data that a forecaster analyzes in trend forecasting can be graphed to show sales peaks and valleys. Some forecasters prefer the graphical method because of its visual nature and insights available. They can discern patterns from a series of data points and add sloped trend lines to graphs to examine possible directions that might otherwise be missed.

Qualitative forecasting: When they lack historical data, some companies go straight to the source: their customers. Qualitative forecasting often involves complex data collection, such as focus groups and market research. Forecasters then flesh out models from this type of data.

Quantitative forecasting: Considered more accurate than qualitative research alone, quantitative forecasting uses past numerical data. The more data a company has, the more precise the forecast usually is. One example of quantitative forecasting is time-series forecasting, which uses temporal quantitative data to make a model to predict future trends.

Four Types of Inventory Forecasting

There are four basic approaches you may consider for inventory forecasting.

  1. Trend forecasting: Project possible trends using changes in demand for your product over time. This doesn’t always account for seasonality or other irregularities in past sales data.
  2. Graphical forecasting: By graphing historic data, you can identify patterns and add slopped trend lines to identify possible insights that may have been missed without the visual representation.
  3. Qualitative forecasting: Qualitative forecasting usually involves focus groups and market research. Forecasters then flesh out models from this type of data.
  4. Quantitative forecasting: This uses past numerical data to predict future demand. The more data gathered, the more accurate the forecast usually is.

Award Winning
Cloud Inventory

Free Product Tour(opens in new tab)

How to Choose the Right Forecasting Method

First, consider what data you have available and what you can collect. The process won’t be the same for all organizations. Established companies should start with historic data, perform inventory analysis and use the quantitative approach. For newer companies, start by collecting qualitative market information.

The best forecasting uses a mix of methods and data types. Quantitative data gives modelers a place to start. Adding qualitative data fleshes out the model. Use industry-specific inputs to complete forecasting.

The trick is in making sure the model reflects wild cards — those ever-changing and often unpredictable trends and market upsets that can shift demand in an instant. Some of these market upsets are slow to occur, such as changes in styles or fads. When addressing more significant challenges, such as a global pandemic, statisticians should develop multiple models based on historical information and different plausible scenarios, possibly working with scenario planning teams.

Forecasters may also incorporate extreme-need scenarios to determine possible demand.

Inventory Forecasting Benefits

Inventory forecasting can mean the difference between profitability and piles of unsold goods. When used correctly, companies can better plan for potential trends, save money on storage and keep customers happy.

4 Top Inventory Forecasting Benefits

Creating and updating inventory forecasts can be a significant investment of your time and resources. But there are also many rewards to reap when demand forecasting is done well. Here are some of the benefits.

Cost savings
It all comes down to efficiency. By ordering the optimum amount of product you can take advantage of bulk ordering without tying up money in unnecessary inventory. Those unneeded products or parts also require warehousing space, which adds costs.

Customer & supplier satisfaction
Having stock on hand helps keep customers happy and improves the likelihood of repeat business. And understanding supplier processes and timelines helps you minimize stock-outs and keep healthy relationships with them with fewer emergency orders and better communication.

Back-end improvements
Inventory and supply chain are intrinsically connected. Improved demand forecasting improves your supply chain management by looking ahead to ensure the right amount of stock. Additionally, it can decrease the amount of manual labor that goes into inventory and supply chain management. Reorder points and other steps can be automated. Advanced inventory management software can keep forecasts up to date with new information as it’s fed into your platform.

Strategic insights
Improved communication across your enterprise can help you meet company goals — and inventory forecasting can play a key role in driving that communication. For example, by looking at past performance and expected outcomes of a marketing campaign, your inventory managers can ensure there is enough product on hand to meet customer demand, while also possibly cutting some costs with bulk buying. In this manner, your inventory management team can impact key performance indicators (KPIs) such as profit margins.

How Does Inventory Forecasting Work?

Inventory forecasting works by helping companies strike a balance between having too much cash tied up in inventory and having enough stock to meet demand. Three considerations in this calculation are forecast period, base demand and application of variables and trends.

For the forecast period, the further out a company goes, the less accurate the forecast becomes. This uncertainty is due to fluctuations in the market, current events and scientific risk. Standard intervals include 30 days, 90 days and one year. A one-year forecast interval accounts for seasonal fluctuations.

Base demand is the known customer need when beginning a forecast. Usually, it uses the prior 30 days of sales or any presales.

The application of variables and trends is the squishier part of forecasting. Some analysts have a recipe for this; they may look at global or even local influences and marketing pushes. These include sales velocity, supply chain challenges and seasonality. Each industry has unique variables that analysts must account for when demand planning.

16 Steps to Succeed at Inventory Forecasting

Follow these basic steps to perform an inventory forecast:

  1. Decide on a future forecast period, such as 30 days, 90 days or one year.

  2. Review the base demand for the period. For example, if the company sold 500 units in the last period, the starting data point will be 500 units for the forecasting model.

  3. Decide on trends and variables and their effect on an increase or decrease in sales, such as any promotions or outlying marketing activity that may have affected the baseline demand.

  4. Review the sales velocity. Sales velocity is how fast sales move through the company pipeline. It is based on the number of leads, average deal value, conversion rate and the sales cycle length.

    Calculate sales velocity with this formula:

    Sales velocity (SV) = [(# of leads) x (average deal value) x (% conversion rate)] / (sales cycle length)

    For example, imagine a scenario where your company has 20 opportunities to sell a product. The percent of these opportunities that usually get turned into a sale is about 50%. The average deal size is $5,000, with a sales cycle length of three months. Sales velocity is [(20 x .50 x $5,000)/90 days] = $555.56. This number means that the product is bringing in about $556 per day in revenue.

  5. Review upcoming marketing activity.
  6. Review any pertinent industry forces, such as new competitors in the marketplace, supplier issues, commercial buyer behavior, threats of less expensive product substitutes and other competitive rivalries.
  7. Review seasonality as it affects each product. There are many ways to determine seasonality, including calculating a seasonal index for each month, spreading the demand over 12 months or using more complex statistical methods.
  8. Consider the possibility of fads or unpaid publicity — including from social media. Since these can happen unexpectedly, many inventory supply experts can't predict these jumps in sales. Work with marketing staff who may have insight can help you plan for additional stock.
  9. Create models. Often, this science is more of an art that's based on all the collected trends and historic knowledge. You’ll use this information to build the models for forecasting. There are many statistical techniques. Which you choose depends on the stability of the product demand.
  10. Clean the data by removing unusual or outlier data points and looking for and filling in missing information.
  11. Identify either a parametric or nonparametric statistical approach. Nonparametric doesn't necessarily mean there are no parameters to the data, simply that the parameters are flexible. Nonparametric data includes things like histograms and ranked-choice surveys.
  12. Process the data by loading it or arranging it in a datasheet appropriate for the algorithm.
  13. Estimate the model parameters: which is the best and worst case for the data points?
  14. Validate the model(s). Use different data than what you used to calibrate the model.
  15. Adjust the model regularly or when events require that you do so. Remember, this is a forecast based on assumptions. Real-life can show it to be off base, so you may need to adjust the parameters.
  16. Reforecast.

Trends and Variables

Some trends and variables are common in forecasting. For example, when a celebrity wears a garment, the fashion industry expects jumps in demand for similar products. This event is not an outlier but anticipated, and it often comes with a plan. This coincides with product launches and restocking.

An outlier in the fashion industry was an unexpected extreme demand for casual wear during the 2020 quarantine, in response to the number of people working from home. Many companies had to react quickly to supply shirts suitable for video calls and pants with elastic waistbands. Meanwhile, demand for dress shoes plummeted.

Glossary: Inventory Forecasting Lingo

There are some terms you’ll need to know to make sense of inventory forecasting, including reorder point, stock outs, safety stock and lead time demands.

What is reorder point? The reorder point (ROP) is the stock level that triggers replenishment in an inventory management system. Accountants can manually plan reorder points. Demand planning software is particularly helpful for avoiding stock-outs by ordering the right items at the right time.

Reorder point formula: The reorder point formula is a trigger for companies to replenish a product. It is the daily usage in units, combined with the days of lead time necessary for replacement, combined with the units of safety stock.

Use this formula to calculate reorder point:

Reorder point = (# units used daily x # days lead time) + # units safety stock

How to determine optimal reorder point: Determine the optimal reorder point by calculating ROP. This ensures your business stays abreast of industry and world events. These situations can change how and when customers order products. Check and adjust forecasts and ROP at specified intervals.

Stock-outs: Stock-outs are when customer demand for a specific product exceeds the inventory a business has on hand. The danger of stock-outs is that companies can lose otherwise faithful long-term customers to the competition.

Safety stock: Safety stock is extra product inventory kept in storage as a buffer against stock-outs. Calculate safety stock by subtracting historical data on maximum usage and average daily usage. Some analysts also conservatively include the company's desired service level.

Lead-time demand: Lead-time demand is the time it takes for suppliers and manufacturers to deliver products compared to when they ordered it. Businesses account for the delay from lead time in their reorder point formulas.

Inventory Forecasting Formulas

Inventory forecasting formulas help your company have the right amount of stock. Some formulas are less complex than others. For example, understanding inventory turnover helps see how your company manages its stock. With the right inventory management software, you can also tap into more complex formulas and methods in forecasting, such as regression analyses and multifactorial models.

How do you calculate inventory forecasting

Formulas are an important part of forecasting. Examples of formulas include economic order quantity (EOQ), ROP, lead time, average inventory and safety stock. These are important concepts to understand to build out more complex models for forecasting. Inventory formulas can also help measure efficiency of operations and help you identify areas for possible improvement. Here are just some inventory management metrics you can track.

Economic order quantity
EOQ is the ideal order quantity during regular times. Use this formula to calculate EOQ:

EOQ Formula

The formula for EOQ is:

EQQ Formula

EOQ = √2DS/H, where

  • D = Demand in units per year
  • S = Order cost per purchase
  • H = Holding cost per unit, per year

For example, imagine a scenario where a retail store sells 500 sets of gloves every year on average. Gloves are small items, so it only costs the store $1 per year to hold each set of gloves in stock. The fixed cost to order more gloves is $4. EOQ = √[(2 x 500 x $4)/$1] = 63.2. The ideal order size is slightly more than 63 sets of gloves.

Reorder point
Where EOQ is an initial planning formula, ROP accounts for stock replenishment.

Reorder point = (# units used daily x # days lead time) + # units safety stock

For example, imagine a scenario with 100 units sold or used per day, a five-day reorder delivery and a safety stock level of 75 units. ROP = (100 x 5) + 75 = 575. When inventory falls below 575 units, the company reaches the reorder point and must order more stock. For a more conservative estimate, ignore the safety stock in the calculation.

Average inventory
This is a measure of how much inventory you have on-hand during a given period. Keeping this consistent over time can help you avoid stock-outs while maintaining enough product to fill customer demand.

Average inventory = (Beginning inventory + ending inventory) / 2

Inventory turnover
How many times has your company sold and replenished its inventory over the last year? The inventory turnover ratio helps you see how many days it will take to sell the inventory you have on hand. A higher ratio points to strong sales.

For this formula, you'll need to determine your cost of good sold (COGS) — which is the sum of all direct costs of producing goods, including raw materials — as well as your average inventory.

Inventory Turnover Ratio = COGS / average inventory

Lead time
How long does it take for a customer to receive an item after an order is placed? This formula measures the efficiency of your business and gives insight into customer experience.

Lead time = Order process time + production lead time + delivery lead time

Safety stock
Think of this as your reserve inventory to make sure you have enough product on hand to fill customer orders. Follow this formula to calculate it.

Safety stock = (Maximum number of units sold in a day X maximum lead time for stock replenishment) — (average daily usage X average lead time in days)

Setting Forecasting Boundaries

Consider the example of when the Toyota Prius first became available in Japan. Forecasters for the car manufacturer knew they would see growth in U.S. markets, but by how much? Prognosticators would need to consider many factors including advertising campaigns, available stock and the price of gas. But even in uncertain times, there would be logical boundaries that could be set. For example, it’s highly unlikely that the Prius would push out all the competition and sell more cars than are sold on average in the U.S. As you start to consider factors such as market forces, you can begin to set boundaries.

Forecasting boundaries ensure analysts use reasonable and probable logic. They help account for outliers but minimize those with a very low probability of happening in your forecasting formulas.

Seasonal trends can be some of the trickiest to account for. New products and replenishments are also challenging.

Forecasting for seasonal products: Historic demand data and sales figures help account for seasonality. Consider other factors, such as unexpected weather and marketplace trends.

One way to account for seasonality is to use the seasonal index formula, which is a measure of the seasonal variation as compared with that season on average. The seasonal index takes away seasonality and smooths out the data. There are multiple methods to calculate the seasonal index.

One method to calculate seasonal index is to use the simple averages method. The steps are as follows:

Step 1: Arrange the data in seasons — most often done in three-month quarters.

Step 2: Calculate the season totals and the season averages. In the example below, the season total for Q1 is the sum of all Q1s for the years 2015-2018 divided by four.

Step 3: Calculate the grand average. In the example below, the grand average is the sum of Q1-Q4 season averages.

Step 4: Calculate the season index. In the example below, calculate the seasonal index for each season by:

Seasonal index (%) = (seasonal average/grand average) x 100

Use the seasonal indices in a graph or time-series analysis to project a trend line for forecasting.

Year Sales Organized by Season for Seasonal Indices Analysis

Q1 Q2 Q3 Q4
2015 $4,520 $2,300 $2,270 $3,400
2016 $5,230 $2,100 $2,900 $3,700
2017 $5,340 $2,700 $2,500 $4,010
2018 $5,020 $3,000 $2,600 $4,600
Season Totals $20,110 $10,100 $10,270 $15,710 Grand Average

Season Average $5,028 $2,525 $2,568 $3,928 $3,512
Season Index 143% 72% 73% 112%

Forecasting for new products: Demand for new products can also be challenging to forecast. Analysts include data on similar existing products as well as qualitative data and tailor models to reflect clusters of products with similar lifecycle curves from which to draw assumptions.

Inventory forecasting models should also account for promotional events. Some software systems build promotions, like tax season or back to school, into their forecasting. They may also use past sales history, seasonal modeling and the dates of the promotions.

Inventory planning and replenishment: You can reorder or replenish inventory automatically or manually. The above formulas and models can inform the optimal amount of stock to keep on hand, as well as the number of items to order and how often to order them. As discussed, supplier glitches, transportation issues and seasonal variances may delay replenishments. Decide whether reordering should be manual or automated with an inventory control system that places orders on a predetermined schedule. If it’s a manual process, make clear who will be responsible for placing the order and exactly how that’s done.

Inventory Forecasting Examples

There are several examples of solid inventory forecasting models. The most prominent formulas are EOQ and ROP. Excel also includes a forecast function that calculates the statistical value of a forecast using historical data, trend and seasonality assumptions.

Dan Sloan, NetSuite technology consulting manager for accounting firm Eide Bailly, describes one example of forecasting he performed in 2014 for a consumer goods company where he worked.

“They were bringing in products for their fourth-quarter peak season sales,” he said. “The CEO and supply chain director were playing close attention to the labor situation on the Long Beach port and realized that a strike was imminent. They accelerated orders to bring in the product earlier. Since we had a sophisticated demand planning engine in place, it was easy to extend the lead times of those shipments and order them in time to beat the anticipated strike.

“These actions led to a huge win, as their competitor’s containers were held up in the port and missed the crucial two weekends before Christmas. Not only did this lead to record sales, but it provided a competitive advantage in terms of market share going into the next year.”

Sloan’s model for this company included qualitative data, so awareness of local news was important. The platform the company used also enabled them to pivot quickly and order additional products.

Another example is of an electronics company that wanted to gain more market share for its mobile device. Before this, the company used only industry sales data from other companies and did little market research to forecast inventory needs. It usually ended up with too little or too much inventory — and not in the right geographical regions. Customers got frustrated when there were stockouts, creating the potential that disgruntled shoppers would decamp to competitors. When the company had too much inventory, it took a financial hit when the product became obsolete.

Better forecasting for this company came in the form of qualitative focus group data and base demand (for this specific company) spread by region. The company improved communication between marketing and other areas of the business.

A convenience store is a smaller-scale example. New owners wanted a better forecast of their products to avoid excessive spoilage. Each product category the convenience store offers has data from past sales: how much sold, when stockouts happened, seasonal sales trends and national demand. The savvy owners included local factors, such as events like a parade nearby and anticipated weather trends. They also surveyed their customers to get their product preferences. They used the data to build a forecast that better stocked their shelves.

Best Practices for Inventory Forecasting

Start by gathering as much data about your sales history as possible. Six months is a good starting place, but a year or two of data can give you better insight into monthly demand. And ensure your data is accurate. Here are some other best practices to consider implementing.

  • Build a team that collaborates in developing the forecast.
  • Use an inventory management program that works well and provides documented processes.
  • Keep a close eye on inventory turnover and whether you meet benchmarks.
  • Use qualitative information to drive forecasting.
  • Use all available historical supply and demand data.
  • Calculate all past margins and profits and future goals, such as gross profit margin.
  • Use the reorder point formula.
  • Carefully measure sales trends so you can be as precise and accurate as possible.
  • Use the lead time to better understand demand.
  • Calculate safety stock.
  • Use software that supports your forecasting needs.

Another tip from NetSuite technology consulting manager for accounting firm Eide Bailly Dan Sloan: “Make sure that your demand planning engine is looking at where you fulfilled inventory from and where you sent it. For example, if you shipped a bulky item from your Los Angeles warehouse to a customer in Florida, you want to make sure you stock that item next time in your North Carolina warehouse.”

Learn from past demand, and regularly implement new practices to support these lessons.

basic practices inventory forecasting

Supportive tools for inventory forecasting include basic spreadsheets and inventory management software systems. Basic spreadsheets are not dynamic and lack many of the tools that can help you with more accurate forecasting and other functionalities, such as setting automatic re-order points.

Here some tools to calculate demand.

  • Spreadsheets: In businesses that have only a few products, basic spreadsheets can work. Use them to load in formulas and assumptions and perform basic calculations.
  • Graphs: Simple graphs with time-series data can show future projections in a format that visually oriented people will appreciate.
  • 3PL: Third-party logistics companies, also known as 3PL, often have statistical modeling experts on staff to meet the needs of growing businesses.
  • Models: The point of forecasting is to build a model that is best for your business. In this way, you can load changes to the data and new scenarios into the model to see how stock quantities should change.
  • Inventory management software: When considering which inventory management system is right for your company, look for platforms that offer embedded forecasting tools. The most advanced systems connect with other areas of your company in one integrated fashion with enterprise resource planning (ERP) so your inventory is managed from the same digital portal as your supply chain, customer relationship management, accounting and more.

Automated Inventory Forecasting

Some software packages include automated inventory forecasting that takes advantage of machine learning to constantly improve the projection process. It helps you forecast optimal stock levels, taking into account business goals and company processes. Machine learning systems reduce errors in supply chain networks and decrease stockouts by training the algorithm to learn from the incoming data and make adjustments.

Streamline Your Inventory Forecasts with NetSuite

Striking a balance between having enough but not too much inventory can mean the difference between success and failure for a business. Developing an inventory forecast can help. NetSuite inventory management software offers a suite of native tools for tracking inventory in multiple locations, determining reorder points, managing safety stock and cycle counts and forecasting. Develop your company’s inventory forecast using NetSuite's demand planning features.

NetSuite provides cloud inventory management solutions that are the perfect fit for a range of organizations, from small businesses to new startup companies to Fortune 100 companies.

Nothing is certain in life. But that uncertainty can present an important opportunity for those who are prepared. Forecasters aren’t in the business of certainties. Instead, they look at the undercurrents in the market and society that could lead to a range of possibilities and plan accordingly. Forecasts are based on data and logic, and models created from historic performance and other factors are refined over time, and often helped with sophisticated technology. Inventory management software can be a key component to your company’s success by making sure you have the right amount of product to meet customer demand while not unnecessarily tying up funds in unneeded inventory.


How is inventory forecasting done?

The basic premise of inventory forecasting is to look at historic demand for your products and forecast the amount you’ll need to meet customer desires. Start by gathering previous sales data — unless it’s a new product, six months is the minimum, and two years would be better. Find the average amount you need, look for seasonal trends and consider other factors that may affect future sales. For example, do you have a marketing campaign rolling out that may increase sales?

What are the four types of forecasting?

There are four basic approaches you may consider for inventory forecasting.

  1. Trend forecasting: Project possible trends using changes in demand for your product over time. This doesn’t always account for seasonality or other irregularities in past sales data.
  2. Graphical forecasting: By graphing historic data, you can identify patterns and add slopped trend lines to identify possible insights that may have been missed without the visual representation.
  3. Qualitative forecasting: Qualitative forecasting usually involves focus groups and market research. Forecasters then flesh out models from this type of data.
  4. Quantitative forecasting: This uses past numerical data to predict future demand. The more data gathered, the more accurate the forecast usually is.

How do you calculate projected inventory?

There are various steps and formulas you can use to project the amount of inventory your company needs on hand. Some of the fundamental formulas to consider are the following: safety stock to ensure you have enough on hand to cover need if you run out and need to order more; reorder point to know at what level of inventory you need to place another order; and lead time so you know how long it takes for you to receive new products or component parts after an order is placed.