How to Forecast Inventory Accurately: A Step-by-Step Guide

If you’re running an ecommerce brand, forecasting inventory isn’t just important. It’s the difference between staying in stock and losing loyal customers. 

In fact, 1 in 6 shoppers won’t come back after a single stockout. 

And with demand shifting faster than ever, guessing based on last month’s sales just won’t cut it.

We’ll walk you through how to forecast inventory the right way, from picking the right methods and tracking the right metrics to avoiding common mistakes. 

Basics of Inventory Forecasting

Inventory forecasting means estimating how much stock you’ll need in the future based on data like past sales, seasonality, current trends, and planned promotions. 

It helps prevent two costly problems: running out of in-demand products and over-ordering items that don’t sell fast enough.

Done right, forecasting helps you

• Prevent stockouts that lose sales and damage customer trust
• Avoid overstock that ties up capital and adds storage costs
• Optimize reordering so you’re not guessing when or how much to buy
• Plan for growth by aligning stock levels with marketing campaigns and product launches

It shifts you from reactive restocking to proactive planning, giving you a real edge in meeting today’s customer expectations.

Difference between forecasting and replenishment

Forecasting and replenishment go hand in hand, but they’re not the same. 

• Forecasting is about predicting demand. It answers “How much are we likely to sell next week or next month?”
• Replenishment turns that forecast into action. It answers “When do we need to reorder and how much should we buy?”

For example, if the forecast shows high demand for a product in July, replenishment planning makes sure the right quantity arrives by late June. 

You can’t replenish accurately without a forecast, and a forecast isn’t useful unless it leads to smart purchasing decisions.

While this guide focuses on inventory forecasting, you can explore what a well-structured replenishment process looks like in our step-by-step guide.

Benefits of Inventory Forecasting

Inventory forecasting isn’t just about predicting what might sell. It’s about making smarter, faster, and more profitable decisions across your entire supply chain.

Here’s how it directly impacts your business outcomes.

1. Accurate inventory levels

Running out of stock means lost sales and disappointing customers. Overstocking means locked-up cash and wasted space.

Inventory forecasting helps you strike the right balance. When you know what’s coming, whether it’s back-to-school season, Black Friday, or a campaign-driven spike, you can stock accordingly and stay prepared.

Black Friday is a hectic time for most businesses. This guide will help you reduce inventory-related ticket response times during the rush.

2. Smarter and faster purchase decisions

When you know what’s likely to sell, purchase and replenishment planning becomes quicker and more precise.

With accurate forecasting, you’re basing these decisions on real expected demand. This speeds up purchasing, reduces back-and-forth with suppliers, and helps you buy the right products at the right time.

3. Improved customer experience

Accurate forecasting means orders are fulfilled on time and products are available when customers want them.

It also aligns teams, sales, ops and supply chain, so everyone works with the same demand plan. This means fewer delays, fewer stockouts and happier customers.

4. Cost savings across the board

Accurate forecasting reduces costs on every front

• Fewer emergency restocks
• Lower holding and storage expenses
• Less waste from unsold or expired stock
• Fewer markdowns to clear excess inventory

It also minimizes the hidden costs of poor planning, like last-minute orders, lost sales, and operational chaos.

Want to take a deeper dive into how to reduce inventory costs for your Shopify brand? Read this.

Metrics You Need to Forecast Accurately

Forecasting relies on more than just past sales. To generate reliable projections, you need core metrics that capture how products move, how long suppliers take to deliver, and how much buffer you actually need. 

Here are the key ones to track.

1. Sales velocity

Sales velocity shows how quickly an item sells over a specific period. It tells you which products are moving fast and which are just sitting. This is the starting point for deciding future reorder quantities.

Formula

Sales Velocity = Units Sold / Time Period  

For example, if you sold 300 units in 30 days, the sales velocity is 10 units per day.

2. Lead time

Lead time is the number of days between placing a purchase order and receiving the stock. It directly affects reorder timing. 

If you don’t factor in lead times, you risk running out of stock before your next shipment arrives.

Formula

Lead Time Demand = Sales Velocity × Lead Time 

This tells you how much extra to order with your current shipment to prevent stock gaps before the next one arrives.

3. Historical sales (3–12 months)

Past sales trends give context to what’s “normal.” The time range you look at depends on the product. 

Fast-moving consumer goods might only need 3 months, while seasonal items might need 12. 

This data shows patterns like spikes, drops or steady trends that affect future demand.

4. Safety stock

Safety stock is a buffer you hold in case something goes wrong, like a supplier delay or an unexpected spike in demand. 

It’s not meant to be touched during regular sales, only when things don’t go as planned. The amount varies by lead time, demand volatility, and how essential the SKU is to your core offering.

Learn how to calculate safety stock in this guide.

5. Inventory turnover

This metric tells you how many times your entire inventory gets sold and replaced during a specific period. 

A high turnover rate means products are selling fast, which usually signals healthy forecasting. A low rate may point to overstocking or poor demand prediction.

Formula

Inventory Turnover = Cost of Goods Sold (COGS) / Average Inventory

It helps track how efficiently inventory is being used over time.

Methods of Inventory Forecasting

Different forecasting methods work for different situations. Some rely on data, others on judgment. 

Most companies use a mix depending on how mature their systems are, how much data they have, and how predictable their demand is. 

Here are the different methods you can choose from.

1. Qualitative forecasting

This method relies on human judgment. It’s often used when launching new products or entering new markets where sales history doesn’t exist. 

Businesses collect input from internal teams, customer interviews, market research, or even distributor insights to estimate future demand. It’s most common in early-stage companies or when testing new SKUs. 

For example, a skincare brand launching a new product might survey 500 customers and combine that with insights from sales reps and partner retailers to decide the size of their first production run.  

2. Quantitative forecasting

This method uses hard numbers, including past sales data, trends, and seasonality to predict what’s likely to sell in the future. It works well for products with consistent demand and enough history to see patterns. 

Basic models include moving averages, exponential smoothing, regression, and time series analysis. This method is widely used by businesses with established product lines. 

3. AI and machine learning forecasting 

AI-based forecasting goes several steps beyond traditional methods. It doesn’t just look at past sales, it learns from them.

It factors in lead times, seasonality, promotions, and safety stock, while continuously updating and improving as more data flows in.

Prediko uses AI to bring accuracy and speed to inventory forecasting.

It pulls real-time sales and inventory data from Shopify, factors in supplier lead times and safety stock, detects sales spikes during holidays or campaigns, and generates forecasts at the SKU level, so your inventory plans stay accurate.

Kate Hewko, a fashion brand managing inventory across international warehouses, was struggling with overstocking, stockouts, and manual tracking errors.

Using Prediko, they replaced guesswork with forecasting based on real-time data and patterns.

The result? A 40% increase in inventory turnover, a 20% drop in stockouts, and significantly less time spent creating POs. Read more here. 

How to Do Inventory Forecasting (Step-by-Step Process)

Want to reduce stockouts, free up cash from excess inventory, and improve customer satisfaction? 

It all starts with getting your forecasting right. This section walks you through the exact steps to create a demand-driven inventory plan for your brand.

Step 1: Choose a forecast period

Start by deciding the time window you want to forecast –monthly, quarterly, or annually. This depends on how fast your products sell and how long suppliers take to deliver. 

Short-term windows help with day-to-day purchase planning, while longer periods are better for strategic ordering and budgeting.

If you’re a Shopify store, Prediko lets you set forecasting periods based on your needs, whether it's quarterly, bi-annual, or annual. This helps you plan short-term for fast-moving SKUs and long-term for seasonal products with ease.

Step 2: Gather key data points

Next, you need a clear picture of how products are performing and how your supply chain behaves. 

Gather sales velocity per SKU, supplier lead times, current stock levels, open purchase orders, safety stock, and return rates. These numbers form the base of your forecast.

Prediko automatically syncs all sales and inventory data from Shopify, POS, and your WMS, eliminating manual updates and giving you real-time inputs to plan more accurately and efficiently.

Step 3: Select your forecasting approach

Once you’ve set your forecasting period and gathered your data, it’s time to choose how you’ll actually forecast demand.

There’s no one-size-fits-all method. Your approach depends on the nature of your products, how much data you have, and how frequently demand shifts.

If you're launching new SKUs or entering fresh markets with little sales history, qualitative forecasting is your best bet.

For products with stable sales, quantitative methods like moving averages or exponential smoothing can help you project demand using historical data.

But if you want the most accurate, scalable approach, AI-powered forecasting with tools like Prediko is the way to go.

It blends real-time sales, lead times, seasonality, and campaign data to adjust forecasts at the SKU level. 

Step 4: Forecast for the upcoming period

Use the collected data and chosen method to generate demand estimates for each product over your selected period. This becomes the basis for reordering and stock planning.

Prediko’s AI, trained on 25 million SKUs across 15 industries, automatically generates forecasts by factoring in all the relevant variables, like sales velocity, seasonality, lead times, and your inputs.

It helps Shopify brands forecast faster and more accurately, without relying on spreadsheets or fixed rules.

Plus, you don’t need to calculate anything manually; the system recommends reorder quantities accordingly.

Step 5: Fine-tune using real-time data

Forecasts aren’t set-and-forget. They need ongoing adjustments. Spikes in sales, supplier delays, or unexpected returns can all throw off even the best models. Regular fine-tuning keeps your plans grounded in reality.

With Prediko, you can review and update forecasts directly on the platform. It shows actual vs. predicted sales, flags any mismatches, and lets you adjust instantly. 

This way, you’re not blindly relying on AI; you’re combining data-backed insights with your own judgment and experience. 

P.S. We put together a detailed guide on demand planning and forecasting for Shopify brands. Check it out here.

Common Inventory Forecasting Mistakes to Avoid (+ Best Practices)

Now, this 5-step process comes with its own set of challenges, which include

1. Relying only on short-term data

Looking only at the last few weeks or a single sales spike can give you a distorted view of actual demand. Forecasts based on narrow time frames often lead to over-ordering or running out of stock when the demand is still there.

What should you do 

Use a longer data window, ideally 3 to 12 months, and analyze trends at the SKU level. Some products might sell a lot but fluctuate wildly. Others may seem slow overall, but sell steadily in specific regions or channels.

The more granular the data, the better your forecast. Patterns at the SKU, channel, or region level often reveal things that topline sales can’t.

2. Not factoring in current stock levels and returns

A forecast that only looks at demand but ignores what’s already in your warehouse or what’s being returned is incomplete.

You might end up reordering products you already have or miss the chance to restock items that are low because returns haven’t been processed yet.

What should you do 

Always combine demand forecasts with real-time inventory data, including what's in stock, in transit, and pending returns. 

Use a platform like Prediko that automatically pulls this information together so your forecast reflects the full picture.

3. Forecasting in silos

Forecasting in isolation misses the bigger picture.

When forecasting is done solely by the ops or inventory team, without input from sales, marketing, or customer support. It often overlooks key demand signals. 

What should you do 

Set up monthly or bi-weekly syncs with your sales, marketing, and customer support teams. These teams often have insights that haven’t yet shown up in your data.

For instance, sales might know a large retail order is coming, while marketing could be planning a campaign that would bring in more orders of a specific SKU.

Bringing these insights into your forecast avoids disconnects and improves accuracy.

4. Failing to segment forecasts by channel or location

Looking at your forecast as one big number can mask important patterns. The same product might sell fast online but slower in retail stores, or perform well in one region but not in another. 

What should you do 

Segmenting by channel or location reveals these nuances and improves inventory allocation.

In 2020, Nike opened a warehouse in Los Angeles that uses predictive analytics and regional demand models to position inventory for digital orders, enabling faster delivery and reducing excess stock in other regions.

Breaking forecasts down by store, channel, or region reveals trends you’d miss in an overall view, helping you make smarter, more targeted stock decisions.

Using Prediko to Forecast Inventory

Most forecasting problems happen when you rely on static data or disconnected dashboards. You base reorders on outdated trends, and by the time inventory runs low, it’s already too late to respond properly.

This guide outlined how to forecast inventory step by step—starting with gathering key data points like sales velocity and lead times, choosing the right method, and refining your plan using real-time data.

Prediko is built for exactly this. It forecasts demand accurately at the SKU level, tracks supplier lead times, plans raw material requirements, and lets you generate purchase orders in just a few clicks. 

You get a complete, real-time view of your inventory, without switching tools or relying on manual updates.

Start a free 14-day trial to see how it helps you forecast and reorder the right way.

FAQs

What is the formula for forecasting inventory?

A common formula is: Forecasted Demand = (Historical Sales Velocity × Time Period) + Adjustments for Trends and Seasonality.

What are the methods of inventory forecasting?

The main methods are qualitative, quantitative, and AI-based forecasting.

How can inventory levels be forecasted?

Inventory levels are forecasted by analyzing past sales, lead times, seasonality, safety stock, and current trends. 

How to forecast inventory in Excel?

Use built-in functions like AVERAGE, TREND, or FORECAST.LINEAR, along with historical sales data, to calculate future demand.