Inventory Forecasting: Types, Best Practices, and Benefits (2024)

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

Market forces can change quickly, and something as seemingly insignificant as a productplacement 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 inventoryforecasting 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 globalsupply chain challenges, planned marketing pushes and campaigns, potential media influencesand the competitive landscape.

What Is Inventory Forecasting?

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

Inventory Forecasting vs. Replenishment

With inventory forecasting, you calculate the amount of the different types ofinventory necessary for future periods. Factors include replenishment data such astiming, availability and delivery speed — also known as lead time. Replenishment isthe 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 valuablestorage space on unnecessary products.
• Various formulas can help you get started by identifying how long it takes for productsor component parts to arrive to you after you order, at what point you should reorderstock, 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 astorm affecting shipping times, and some are internal — such as a marketingcampaign 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 ofinformation and logic to make sure you have enough product on hand to meet customer demandwithout overdoing it and ordering too much that you then must pay to warehouse. Forecastersare creating more complex tools like advanced computer-based simulations and futures marketsto create demand forecasts.

Inventory forecasting starts with the simple step of looking at how much product your companyhas 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 demandgrowing? 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. Sophisticateddemand 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 efficientforecasting relies on data and formulas. There are different methods and approaches to theseformulas.

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 marketresearch.

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

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

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

Quantitative forecasting: Considered more accurate than qualitative researchalone, quantitative forecasting uses past numerical data. The more data a company has, themore precise the forecast usually is. One example of quantitative forecasting is time-seriesforecasting, 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 foryour product over time. This doesn’t always account for seasonality or otherirregularities in past sales data.
2. Graphical forecasting: By graphing historic data, you can identifypatterns and add slopped trend lines to identify possible insights that may have beenmissed without the visual representation.
3. Qualitative forecasting: Qualitative forecasting usually involves focusgroups and market research. Forecasters then flesh out models from this type of data.
4. Quantitative forecasting: This uses past numerical data to predictfuture demand. The more data gathered, the more accurate the forecast usually is.

How to Choose the Right Forecasting Method

First, consider what data you have available and what you can collect. The processwon’t be the same for all organizations. Established companies should start withhistoric data, perform inventory analysis anduse the quantitative approach. For newer companies, start by collecting qualitative marketinformation.

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

The trick is in making sure the model reflects wild cards — those ever-changing andoftenunpredictable trends and market upsets that can shift demand in an instant. Some of thesemarket upsets are slow to occur, such as changes in styles or fads. When addressing moresignificant challenges, such as a global pandemic, statisticians should develop multiplemodels based on historical information and different plausible scenarios, possibly working with scenarioplanning 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 unsoldgoods. When used correctly, companies can better plan for potential trends, save money onstorage and keep customers happy.

4 Top Inventory Forecasting Benefits

Creating and updating inventory forecasts can be a significant investment of your time andresources. 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 takeadvantage of bulk ordering without tying up money in unnecessary inventory. Those unneededproducts 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 repeatbusiness. And understanding supplier processes and timelines helps you minimize stock-outsand keep healthy relationships with them with fewer emergency orders and bettercommunication.

Back-end improvements
Inventory and supply chain are intrinsically connected. Improved demand forecasting improvesyour 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 supplychain management. Reorder points and other steps can be automated. Advanced inventorymanagement software can keep forecasts up to date with new information as it’s fedinto your platform.

Strategic insights
Improved communication across your enterprise can help you meet company goals — andinventory forecasting can play a key role in driving that communication. For example, bylooking at past performance and expected outcomes of a marketing campaign, your inventorymanagers can ensure there is enough product on hand to meet customer demand, while alsopossibly cutting some costs with bulk buying. In this manner, your inventory management teamcan 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 muchcash tied up in inventory and having enough stock to meet demand. Three considerations inthis 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 forecastbecomes. This uncertainty is due to fluctuations in the market, current events andscientific risk. Standard intervals include 30 days, 90 days and one year. A one-yearforecast interval accounts for seasonal fluctuations.

Base demand is the known customer need when beginning a forecast. Usually, it uses the prior30 days of sales or any presales.

The application of variables and trends is the squishier part of forecasting. Some analystshave a recipe for this; they may look at global or even local influences and marketingpushes. These include sales velocity, supply chain challenges and seasonality. Each industryhas 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 inthe last period, the starting data point will be 500 units for the forecastingmodel.

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 thebaseline demand.

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

   Calculate sales velocity with this formula:

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

   For example, imagine a scenario where your company has 20opportunities to sell a product. The percent of these opportunities that usually getturned into a sale is about 50%. The average deal size is $5,000, with a sales cyclelength 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 productsubstitutes and other competitive rivalries.
7. Review seasonality as it affects each product. There are many ways to determineseasonality, including calculating a seasonal index for each month, spreading the demandover 12 months or using more complex statistical methods.
8. Consider the possibility of fads or unpaid publicity — including from socialmedia. Since these can happen unexpectedly, many inventory supply experts can't predictthese jumps in sales. Work with marketing staff who may have insight can help you planfor additional stock.
9. Create models. Often, this science is more of an art that's based on all the collectedtrends and historic knowledge. You’ll use this information to build the models forforecasting. There are many statistical techniques. Which you choose depends on thestability of the product demand.
10. Clean the data by removing unusual or outlier data points and looking for and filling inmissing information.
11. Identify either aparametric or nonparametricstatistical approach.Nonparametric doesn't necessarily mean there are no parameters to the data, simply thatthe parameters are flexible. Nonparametric data includes things like histograms andranked-choice surveys.
12. Process the data by loading it or arranging it in a datasheet appropriate for thealgorithm.
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 aforecast based on assumptions. Real-life can show it to be off base, so you may need toadjust the parameters.
16. Reforecast.

Trends and Variables

Some trends and variables are common in forecasting. For example, when a celebrity wears agarment, the fashion industry expects jumps in demand for similar products. This event isnot an outlier but anticipated, and it often comes with a plan. This coincides with productlaunches and restocking.

An outlier in the fashion industry was an unexpected extreme demand for casual wear duringthe 2020 quarantine, in response to the number of people working from home. Many companieshad to react quickly to supply shirts suitable for video calls and pants with elasticwaistbands. 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 thattriggers replenishment in an inventory management system. Accountants can manually planreorder points. Demand planning software is particularly helpful for avoiding stock-outs byordering the right items at the right time.

Reorder point formula: The reorder point formula is a trigger for companiesto replenish a product. It is the daily usage in units, combined with the days of lead timenecessary 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 pointby 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 adjustforecasts and ROP at specified intervals.

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

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

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

Inventory Forecasting Formulas

Inventory forecasting formulas help your company have the right amount of stock. Someformulas are less complex than others. For example, understanding inventoryturnover helps see how your company manages its stock. With the right inventorymanagement 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 orderquantity (EOQ), ROP, lead time, average inventory and safety stock. These are importantconcepts to understand to build out more complex models for forecasting. Inventory formulascan also help measure efficiency of operations and help you identify areas for possibleimprovement. Here are just some inventory managementmetrics you can track.

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

For example, imagine a scenario where a retail store sells 500 sets ofgloves every year on average. Gloves are small items, so it only costs the store $1 per yearto hold each set of gloves in stock. The fixed cost to order more gloves is $4. EOQ =√[(2 x500 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.

Reorderpoint = (# units used daily x # days lead time) + # units safetystock

For example, imagine a scenario with 100 units sold or used per day, a five-day reorderdelivery and a safety stock level of 75 units. ROP = (100 x 5) + 75 = 575. When inventoryfalls below 575 units, the company reaches the reorder point and must order more stock. Fora 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 thisconsistent over time can help you avoid stock-outs while maintaining enough product to fillcustomer demand.

Averageinventory = (Beginning inventory + ending inventory) / 2

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

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

Inventory TurnoverRatio = COGS / average inventory

Lead time
How long does it take for a customer to receive an item after an order is placed? Thisformula 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 fillcustomer 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. Forecastersfor 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 logicalboundaries that could be set. For example, it’s highly unlikely that the Prius wouldpush out all the competition and sell more cars than are sold on average in the U.S. As youstart to consider factors such as market forces, you can begin to set boundaries.

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

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

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

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

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

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

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

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

Step 4: Calculate the season index. In the example below, calculate theseasonal 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 projecta trend line for forecasting.

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

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

Inventory planning and replenishment:You can reorder or replenishinventory automatically or manually. The above formulas and models can inform the optimalamount of stock to keep on hand, as well as the number of items to order and how often toorder them. As discussed, supplier glitches, transportation issues and seasonal variancesmay delay replenishments. Decide whether reordering should be manual or automated withaninventory controlsystemthatplaces orders on a predetermined schedule. If it’s amanual process, make clear who will be responsible for placing the order and exactly howthat’s done.

Inventory Forecasting Examples

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

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

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

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

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

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

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

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

Best Practices for Inventory Forecasting

Start by gathering as much data about your sales history as possible. Six months is a goodstarting 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 considerimplementing.

• 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 firmEide BaillyDan Sloan: “Make sure that your demand planning engine is looking at where youfulfilled inventory from and where you sent it.For example, if you shipped a bulkyitem from your Los Angeles warehouse to a customer in Florida, you want to make sure youstock that item next time in your North Carolina warehouse.”

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

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

Here some tools to calculate demand.

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

Automated Inventory Forecasting

Some software packages include automated inventory forecasting that takes advantage ofmachine learning to constantly improve the projection process. It helps you forecast optimalstock levels, taking into account business goals and company processes. Machine learningsystems reduce errors in supply chain networks and decrease stockouts by training thealgorithm 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 differencebetween success and failure for a business. Developing an inventory forecast can help. NetSuiteinventory management software offers a suite of native tools for tracking inventoryin multiple locations, determining reorder points, managing safety stock and cycle countsand forecasting. Develop your company’s inventory forecast using NetSuite's demandplanning features.

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

Conclusion
Nothing is certain in life. But that uncertainty can present an important opportunity forthose 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 ofpossibilities and plan accordingly. Forecasts are based on data and logic, and modelscreated from historic performance and other factors are refined over time, and often helpedwith sophisticated technology. Inventory managementsoftware can be a key component to your company’s success by making sure youhave the right amount of product to meet customer demand while not unnecessarily tying upfunds in unneeded inventory.

FAQs

How is inventory forecasting done?

The basic premise of inventory forecasting is to look at historic demand for your productsand forecast the amount you’ll need to meet customer desires. Start by gatheringprevious sales data — unless it’s a new product, six months is the minimum, andtwo years would be better. Find the average amount you need, look for seasonal trends andconsider other factors that may affect future sales. For example, do you have a marketingcampaign 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 foryour product over time. This doesn’t always account for seasonality or otherirregularities in past sales data.
2. Graphical forecasting: By graphing historic data, you can identifypatterns and add slopped trend lines to identify possible insights that may have beenmissed without the visual representation.
3. Qualitative forecasting: Qualitative forecasting usually involves focusgroups and market research. Forecasters then flesh out models from this type of data.
4. Quantitative forecasting: This uses past numerical data to predictfuture 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 yourcompany 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 toorder more; reorder point to know at what level of inventory you need to place anotherorder; and lead time so you know how long it takes for you to receive new products orcomponent parts after an order is placed.