Table of Contents<\/strong><\/p>\n Inventory forecasting uses data and analytics to predict trends in inventory movement. Although it goes hand in hand, inventory forecasting isn\u2019t exactly sales forecasting<\/a>, and it isn\u2019t meant to give sales projections<\/a>.<\/p>\n Instead, it\u2019s used to help companies better plan their inventory management strategy based on historical trends and data, like how much product they should have on their shelves at any given time.<\/p>\n I asked Zalzal for his insights into inventory forecasting, and he told me that it plays a huge role in demand planning.<\/p>\n \u201cYou need to know how much you\u2019re going to sell in the next couple of months,\u201d he said. \u201cThen you can go back down and look at your inventory and understand what you\u2019re going to sell and how much inventory you need to make sure to fulfill those sales.\u201d<\/p>\n Trust me, your company doesn\u2019t want to be known as a brand that can\u2019t keep products in stock. When inventory levels drop, you risk losing loyal customers to other brands that have better inventory management.<\/p>\n One reason your company might struggle with low inventory is vendor delivery times. 72% of small and medium-sized businesses<\/a> struggle with inconsistent delivery times, especially if they\u2019re sourcing their inventory from vendors overseas. Inventory forecasting can help reduce this problem by giving businesses a better understanding of when they should re-order their products to stay ahead of product demand.<\/p>\n Zalzal told me demand planning and inventory forecasting are also especially important for companies that sell products with expiration dates. You don\u2019t want to purchase an overabundance of a product with an expiration date only to have it expire while sitting on your warehouse shelves \u2014 talk about a loss in profit margins.<\/p>\n I know enough about AI to know that it can be incorporated into nearly every aspect of business operations. And, as it turns out, AI is convenient for inventory management and forecasting, too.<\/p>\n According to Mark, using AI when forecasting can help companies make better, more accurate predictions. The better your predictions are, the better your inventory management will be. Mark mentioned that you can do inventory forecasting manually using tools like Excel. However, your margin of error will likely be higher, which could lead to unexpected surprises later.<\/p>\n AI uses machine learning to make predictions based on historical data. However, unlike Excel, machine learning can make predictions based on variables that might not be included in the historical data.<\/p>\n For example, let\u2019s say your company sells live Christmas trees, and you know that demand will increase in mid-November. Theoretically, you can use your past data to help you understand how many trees you should order and when. But if you use an AI model, you can factor in other variables, like the weather, to better understand when you should place your order to avoid transportation delays within the supply chain.<\/p>\n Although AI inventory forecasting can help you make better, more informed decisions for inventory management, there is still a significant drawback. Only 23%<\/a> of small and medium-sized businesses use AI in their forecasting efforts. Most companies say they are concerned about data security and integrity.<\/p>\n Since Mark is a data scientist and a big fan of AI, I asked him for his thoughts on this. Zalzal confirmed that security breaches are a big concern for companies since most businesses work with private and sensitive data. AI tools work by storing massive amounts of data in warehouses. However, if those warehouses are hacked, sensitive data can become public information.<\/p>\n Although this is a concern, Zalzal said there are things you can do as a company to ensure your data is protected. He mentioned there are certifications you should look out for, like certification of SCO2 compliance<\/a>, and that AI software companies must undergo compliance audits similar to audits in the financial industry. These cybersecurity audits ensure AI companies meet federal regulations to protect and manage your data.<\/p>\n <\/a> <\/p>\n There are plenty of benefits from inventory forecasting, whether you use an Excel spreadsheet or an AI tool. Let\u2019s take a look at them.<\/p>\n One of the biggest benefits of inventory forecasting is improving order accuracy. With forecasting, you get a better idea of how much inventory you should purchase, when you should buy it, and how much time you\u2019ll need to move your product. The more accurate your predictions are, the less likely you\u2019ll lose revenue.<\/p>\n Zalzal had some thoughts to add to this.\u201cIf you can predict accurately what you\u2018re going to sell by item and take everything into account, like your marketing efforts and your seasonality, and actions that the company does, that\u2019s definitely going to translate to better inventory management,\u201d he explained.<\/p>\n As Zalzal told me, \u201cThe more accurate the models are, the better you\u2018re going to stock up and the less waste you\u2019re going to have.\u201d<\/p>\n When your predictions are more accurate, you can purchase only the necessary items. This means you\u2019re reducing the risk of having too much or too little inventory and maintaining only the optimal stock levels.<\/p>\n I know I\u2019m not the only one who gets a little disappointed when my favorite company is out of stock of something I want to buy. When this happens, I usually go to Google to find a brand with the item in stock.<\/p>\n Don\u2019t let low stock cause you to lose customers. Inventory forecasting can help you maintain your stock levels, keeping your loyal customers happy and satisfied with your product selection.<\/p>\n It helps to know when certain products might become popular and how quickly you\u2019ll sell through your stock. By purchasing the appropriate amount of inventory \u2014 no more than what you need \u2014 you can reduce the amount of money you\u2019ll pay in storage and holding costs and stretch your budget<\/a> just a bit farther.<\/p>\n Plus, the longer your products sit on the warehouse shelves, the more money you\u2019ll lose. With an estimated 8% of surplus stock<\/a> worldwide going to waste, businesses lose $163 billion in inventory each year.<\/p>\n Inventory forecasting can help save your business money and reduce error margins.<\/p>\n As Zalzal told me, inventory forecasting is the foundation for demand forecasting<\/a>. When you factor in supply, demand, other variables, and historical data and trends, you can make educated predictions about future sales and how quickly your inventory will dwindle.<\/p>\n As a small business owner, I\u2019m bad about putting off tasks that need to be done as soon as possible. Thank goodness I don\u2019t have inventory to manage, or I\u2019d always be behind. If I did manage inventory, though, I would purchase a subscription to a forecasting tool to help increase my efficiency.<\/p>\n In fact, an increase in efficiency is why 51% of businesses<\/a> opt to use inventory management software. Business owners who use inventory management tools can better manage their orders. Using these tools, you can get a good idea of when to order or move products, helping you stay more efficient, organized, and on top of product demand.<\/p>\n As Zalzal and I chatted, I learned there\u2019s one more major benefit to inventory forecasting: better financial planning.<\/p>\n As a business owner, you need to ensure you have enough funds allocated to inventory management in your yearly budget. Looking at past historical data and pairing it with machine learning can help you make better predictions for your budgets in the next year.<\/p>\n \u201cFinancial planning becomes much simpler,\u201d he added. \u201cIn retrospect, over the last year, it\u2018s easy to find the mistakes. It\u2019s easy to say where we overspent and where we underspend. But when you want to look ahead into the future, you need a very accurate forecast. A good forecast allows you to put yourself one year in advance, look back at the year, and ask, \u2018What could I have done here to save and optimize our financials?\u2019\u201d<\/p>\n <\/a> <\/p>\n To do inventory forecasting, you will need some data, like lead time demand and safety stock. Lead time demand refers to the amount of inventory required to meet your demand during the time it takes for a supply order to arrive from your vendor after you place the order. You can average the lead time using this formula:<\/p>\n Safety stock describes the extra inventory you need to prevent a stockout. You can find your average safety stock using this formula:<\/p>\n After you\u2019ve crunched those numbers, it\u2019s time to plug them into the inventory forecasting formula.<\/p>\n I find it helpful to see a formula in action, so let\u2019s plug in some numbers for an example. Let\u2019s pretend I sell water bottles. Once I place my order, it takes 15 days to receive the bottles, and I typically sell five water bottles a day.<\/p>\n Here\u2019s the lead time demand formula using my data:<\/p>\n Lead Time Demand = 5 water bottles\/day X 15 days =<\/em><\/p>\n 75 water bottles<\/em><\/p>\n Next, I need to find the number to represent my safety stock. Let\u2019s pretend that on my best day, I sold 15 water bottles, and it only took 7 days for the shipment to get to my storefront. Here\u2019s what the safety stock formula looks like using those numbers:<\/p>\n Safety Stock = [(15 water bottles X 7 days)] – [5 water bottles X 15 days)]<\/em><\/p>\n After calculating those numbers, my safety stock value is 30.<\/p>\n Now, I can plug those values into the inventory forecasting formula:<\/p>\n Inventory Forecasting = (75) + (30)= 105<\/em><\/p>\n To stay on top of inventory and ensure I don\u2019t run out while waiting on a new shipment, I\u2019ll need 105 water bottles on hand.<\/p>\n <\/a> <\/p>\n If you\u2019ve ever sat through a statistics class, you know plenty of ways to crunch numbers and visualize data. For example, if you\u2019re a visual learner like me, graphs can help you better understand your trends by visually representing your data.<\/p>\n In inventory forecasting, you can use modeling to understand your data in various ways. It\u2019s helpful to understand there are multiple types of modeling, and they fall under four specific categories. Those four categories are:<\/p>\n I think it\u2019s important to use a good mix of each method to make better inventory decisions.<\/p>\n (Looking for a more in-depth course? Check out these lessons on forecasting analytics<\/a> and sales forecasting<\/a>.)<\/p>\n As I chatted with Zalzal, I learned that businesses can use several inventory forecasting models<\/a> to determine inventory projections. The type of forecasting you do depends on the data you\u2019ve collected.<\/p>\n The good news about inventory forecast modeling is that you can use software like Excel, Google Sheets, or HubSpot\u2019s forecasting software<\/a> to make sense of your data. You don\u2019t have to do the calculations by hand unless you want to, of course. (I\u2019m a big fan of calculators and software, though. Highly recommend.)<\/p>\n Let\u2019s look at six inventory forecasting models you can use to get a more accurate look at your inventory management.<\/p>\n According to Zalzal, there are three basic inventory forecasting models, and you can calculate them by hand without getting a headache. Those are trend analysis, moving average, and exponential smoothing. I\u2019ll talk more about moving average and exponential smoothing later, but for now, let\u2019s focus on trend analysis.<\/p>\n Trend analysis helps businesses use historical data to identify patterns and trends in their inventory stock. If you\u2019ve been in business for a while, you likely can predict when your products will be popular among your customers based on their purchase history.<\/p>\n For example, if you have data on your past water bottle sales, you might notice an uptick in sales in January when your customers make healthier New Year\u2019s Resolutions. You might also see an increase in sales in late spring when the weather turns warmer.<\/p>\n Using the insights from this data and your lead time numbers, you can plan the appropriate time to purchase your stock to have it on hand when demand increases.<\/p>\n The moving average is relatively simple among the inventory forecasting techniques. Zalzal told me: \u201cThe Moving average is deterministic, and there’s really no room for playing around with it.\u201d<\/p>\n Back in college, I took a statistics class. I\u2019ll be the first to tell you I was confused nearly half the time. However, moving average is a concept I understood because it\u2019s simple addition and division. I can see why Zalzal says it\u2019s deterministic.<\/p>\n The moving average is simply an average of units sold over a set period. This forecasting model is best used for products with a relatively stable demand. For example, let\u2019s pretend your business sells toothpaste. Toothpaste is a product that I hope nearly everyone uses. It\u2019s also a commodity that likely won\u2019t see a spike in demand unless something crazy happens, like panic buying because of a global pandemic.<\/p>\n So, if your business sells 100 tubes of toothpaste in the first month, 120 tubes in the second month, and 110 tubes in the third month, the moving average (found by adding those numbers together and dividing by three) is 110 tubes.<\/p>\n Knowing this number, you can place an inventory order for 110 tubes of toothpaste and sell them each month.<\/p>\n Exponential smoothing is very similar to moving average. It just takes it one step further. You\u2019ll want to run this inventory forecasting method if you notice a slow uptick in the demand for your product.<\/p>\n Let\u2019s go back to the toothpaste example. If you take a quick look at your numbers, it\u2019s clear that 110 toothpaste tubes are enough to maintain your inventory levels. However, you\u2019ve changed your marketing approach and gained loyal customers. So, in the fourth month, you sell 112 tubes. Then, in the fifth month, you sell 116 tubes. And, in the sixth month, you sell 121 tubes of toothpaste.<\/p>\n Do you see the slow uptick in the number of tubes sold? Over time, this increase will affect your inventory supply. Exponential smoothing gives your more recent data a heavier weight to help highlight the more recent demand patterns. This can help you plan your stock order without overbuying.<\/p>\n Demand forecasting based on lead time demand uses the inventory forecasting formula. You can use this forecasting method to calculate future inventory needs to ensure you have enough stock to last through delivery times.<\/p>\n For a quick refresher, this technique factors in your average demand and the lead time to give you a better idea of the safety stock or the amount of stock you have on hand during the order wait time, ensuring you don\u2019t run out of product.<\/p>\n This is a relatively simple math formula that you can do by hand. Or, if you\u2019re a whiz at spreadsheets, you can set up your sheet to calculate this for you as you enter data.<\/p>\n![\"Free](\"https:\/\/no-cache.hubspot.com\/cta\/default\/53\/b91f6ffc-9ab7-4b84-ba51-e70672d7796e.png\")
<\/a><\/p>\n\n
What Is Inventory Forecasting?<\/h2>\n
How Businesses Can Use Inventory Forecasting<\/h3>\n
AI and Inventory Forecasting<\/h3>\n
The Drawback of AI Inventory Forecasting<\/h4>\n
The Benefits of Inventory Forecasting<\/h2>\n
![\"the](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/02\/inventory-forecasting-2-20250213-3459017.webp\" "\\"\\"")
<\/p>\n1. Improves Accuracy<\/h3>\n
2. Reduces Over or Understocking<\/h3>\n
3. Increases Customer Satisfaction<\/h3>\n
4. Saves Costs<\/h3>\n
5. Supports Demand Forecasting<\/h3>\n
6. Boosts Efficiency<\/h3>\n
7. Improves Financial Planning<\/h3>\n
Inventory Forecasting Formula<\/h2>\n
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<\/p>\n![\"safety](\"https:\/\/knowledge.hubspot.com\/hubfs\/inventory-forecasting-4-20250213-6040696.webp\" "\\"\\"")
<\/p>\n![\"inventory](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/02\/inventory-forecasting-5-20250213-29506.webp\" "\\"\\"")
<\/p>\nInventory Forecasting Formula Example<\/h4>\n
What I Learned About Inventory Forecasting Methods<\/h2>\n
\n
Inventory Forecasting Models<\/h3>\n
1. Trend Analysis (Time Series Forecasting)<\/h4>\n
2. Moving Average<\/h4>\n
3. Exponential Smoothing<\/h4>\n
4. Demand Forecasting Based on Lead Time Demand<\/h4>\n
5. Regression Analysis<\/h4>\n
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