For some retailers, acquiring new customers is easy. The hard part can be delivering on promises—like having enough inventory to sell.
Carry too little inventory and you risk turning away ready buyers. But stock too much, and you crowd your stockroom, push up inventory costs, and overwhelm store teams.
Demand becomes harder to predict as consumer behavior shifts and supply chains fluctuate. Retail demand planning gives you back control. When you can sense changes early, you can stock the right inventory, plan staffing, and grow your business profitably.
But accurate planning depends on clean, unified data—something retailers struggle with when point of sale (POS) and ecommerce run on separate systems.
In this guide, you’ll learn what retail demand planning is, a five-step planning process, and how modern tools like AI and unified commerce improve accuracy.
What is retail demand planning?
Retail demand planning is a retail strategy used to assess customer demand and plan how to meet it. The goal is to successfully forecast inventory and guarantee the ability to sell to customers should demand change.
When there are seasonal, cyclical, or other market fluctuations that can impact product supply and inventory levels, a retail demand plan helps you prepare for these inventory management challenges so your stock isn’t depleted or sitting in surplus.
Effective demand planning blends quantitative forecasting (like historical sales patterns) with qualitative insights (like trends, promotions, or market shifts) to build a clearer picture of future demand.
This foundation supports every step that follows, from choosing the right forecasting methods to building a reliable demand-planning process.
Why is demand planning critical for retailers?
Demand planning helps retailers stay profitable and resilient in any environment—from peak seasons to slower periods and supply chain disruptions.
Avoid costly stockouts and overstocks
Successful demand planning factors in sales history, lead times, and upcoming campaigns to avoid being out of stock. This helps you manage logistical and other challenges because you can more accurately estimate how many units you will sell.
If you’re forecasting a 75% increase in demand over Black Friday, for example, retail demand planning might mean renting an additional storage unit from August to December.
Increase customer satisfaction and loyalty
Stockouts don’t just result in losing a transaction in the moment. They can also frustrate customers and degrade trust.
Including demand planning in your retail strategy can help you meet customer demand, and therefore, increase customer satisfaction.
A strategy that includes hiring seasonal staff and restructuring your stockroom, for example, can help set you up for success. If there’s a last-minute influx of foot traffic, you already have a plan in place and can provide shoppers with a great customer experience.
Optimize inventory and improve cash flow
Inventory optimization is the process of planning what to sell and when to sell it by, and having the appropriate inventory levels on hand to supply customer demand—while minimizing operational costs.
If you’re forecasting 500 extra monthly orders during your January sale, for example, part of the demand-planning process might include stockroom reorganization so you can efficiently count inventory—including receiving inventory to minimize miscounts and return fraud—and set reorder points so you’re not overextended financially with holding costs.
These benefits compound over time: more accurate planning leads to fewer fire drills, smoother operations, and stronger margins across your retail locations.
Demand planning vs. demand forecasting: What’s the difference?
While both terms estimate the level of demand a store will experience, they play different roles in your retail planning. Demand forecasting gives an estimate of how much you are expected to sell based on historical data. The process also includes analyzing data points such as sales, inventory performance, and new product launches and discontinued items.
Retail demand planning uses this forecast to plan:
- Staffing schedules
- Open-to-buy budgets
- Pricing and promotional strategies
- Stock levels by region or store
- Production and purchase schedules
In short, forecasting gives you a prediction; demand planning is how you act on it. Next, we’ll break down the tools you’ll use to forecast demand and the workflow to turn those forecasts into action.
Common retail demand-planning methods
These methods form the foundation of modern retail forecasting and feed directly into the five-step demand-planning process that follows.
Analyzing historical data (time series analysis)
Time series analysis is the study of historical sales data over regular intervals (days, weeks, months) to identify patterns like trends, seasonality, and cycles.
We can see this in practice for a retail store that sells winter coats. Over the past two years, they notice:
- Sales spike every December (seasonality).
- Sales gradually increase each year (upward trend).
- Occasionally, there’s a small dip in January (cycle).
Using time series analysis, the store forecasts that next December, demand will be higher than last December. They can stock extra coats to avoid running out, and ensure they have enough seasonal staff scheduled to cover a boost in foot traffic during the winter months.
Causal modeling (linear regression)
Linear regression is a tool that models future demand based on historical data. It uses a variety of data sources—such as past sales, gross domestic product (GDP), and consumer spending habits—to find the causal relationship, helping retailers predict demand patterns in the longer term.
Qualitative forecasting (market research and expert opinion)
Economic market fluctuations, natural disasters, staff shortages, and supply chain interruptions can throw even the most well-thought-out demand plan off course. McKinsey reports that nearly 90% of companies encountered supply chain disruptions in 2024, with average response times of about two weeks.
Qualitative forecasting captures events that happen outside your control—such as inflation, fluctuating unemployment rates, and other ecommerce trends—that impact demand for unexpected reasons.
Let’s look at how to apply these methods in a structured, repeatable demand planning workflow.
The 5-step demand-planning process
1. Organize and clean historical data
Start by collecting internal data needed for the demand forecast. Spokespeople and stakeholders can contribute key information from their respective departments, such as:
- POS sales data
- Marketing results
- Inventory data
- Product lifecycle
- Production lead times
When you use different platforms to run your online and retail stores, inventory discrepancies are more likely to happen because systems aren’t in sync. This can lead to more frequent inventory counts to reconcile differences between your ecommerce platform and POS system’s inventory quantities and ensure stock levels are accurate.
Unifying inventory management with an enterprise resource planning (ERP) system can help make integrating, tracking, and strategic planning for inventory easier—minimizing the risks of missteps and inefficiencies. For instance, lowering the chances of overstocks and stockouts can result in cost savings thanks to streamlined order fulfillment and other processes. When POS and ecommerce data live on a single platform like Shopify, you give forecasting models a more reliable foundation.
💡Tip: With Shopify, you see unified inventory reports that can help you confidently forecast, track, and count stock from several sales channels.
2. Generate a baseline statistical forecast
A sales forecast uses past performance, including historical revenue numbers, to predict future sales. This is where quantitative methods like time series analysis or moving averages come into play.
If you compare sales over the past three years, for example, you may find that Q1 averages 25% more sales than Q2. It’s safe to assume the same will happen this year, so forecasting a 25% increase in sales for Q1 in 2026 acts as the foundation of your demand plan.
Moving average demand
Moving average demand considers prior sales periods (such as quarterly or annually) when determining future demand.
The moving average formula shows a trend in any dataset. If you’re forecasting retail sales for the upcoming year, look at past revenue. For example:
- $10,000 sales in 2021
- $30,000 sales in 2022
- $75,000 sales in 2023
- $132,000 sales in 2024
Add those figures ($247,000) and divide by the number of years (four) to get a moving average of $61,750.
3. Gather insights from sales, marketing, and finance teams
Challenge the demand forecast by having stakeholders review and critique your products’ future demand. This system of checks and balances can be an effective way to check for projection inaccuracies and assess the quality of collected data. This is also where qualitative forecasting complements the statistical work you did in step two.
The more eyes you have on the demand forecast, the greater the chances of identifying any overlooked data points or other relevant analytics that must be taken into consideration.
Encourage stakeholders to take part in demand sensing—a short-term forecasting technique largely based on instinct and noticeable trends. If you’re selling women’s clothing, you might pick up on ’90s style corsets doing the rounds on TikTok.
Ann McFerran, CEO of beauty brand Glametic, says, “Demand planning goes further by considering other factors that could impact demand, such as seasonality and consumer taste trends. This information is essential for meeting your customer demand while minimizing excess inventory.”
4. Finalize the consensus plan
Turn your insights into strategy and develop a final plan that includes sufficient resources to supply an increased volume of customer demand.
You can create a solid retail plan by asking yourself the following questions:
- Can vendors supply enough inventory before demand increases?
- Is the logistics team prepared for a large volume of new orders?
- Are there enough retail associates to staff a busier store?
“Knowing which products sell out right away versus sit on our shelves for months until Christmas is important, so we can not only have adequate and appropriate inventory, but are able to order ahead so artisans have time to produce inventory,” says Sylvia Fountaine, owner and founder of the recipe site Feasting at Home.
“We also import from some international artisans, so product lead time accuracy was crucial for keeping our shelves well stocked.”
A unified view of inventory across locations and channels makes these decisions easier—everyone is working from the same real-time data on stock levels and lead times.
💡Tip: The freeStocky app lets you manage vendors, purchase orders, and automate replenishment from your Shopify POS admin.
5. Measure forecast accuracy and iterate
Measure the results of your demand-planning strategy to determine its effectiveness. To do this, set key performance indicators (KPIs) such as sales forecast accuracy or order accuracy. Then, benchmark these metrics to assess the success of your demand plan.
Answer these questions to create a more accurate forecast and demand plan for next time:
- How accurate was the initial forecast?
- Did you serve more or fewer customers than expected?
- Did store operations run smoothly throughout the busy period?
When POS and ecommerce data are connected, retailers can track these metrics in one place. They can spot patterns faster and act on those learnings during the next planning cycle. Demand planning becomes a continuous loop of forecasting, execution, and improvement—not a one-time exercise.
The future of demand-planning technology
As supply chains are forced to become even more resilient, some retailers are digitizing their supply chains to forecast demand more accurately.
How AI and machine learning improve forecast accuracy
Retailers are integrating technologies into their operations such as demand-planning software with artificial intelligence and machine learning capabilities.
An AI supply chain algorithm takes the following factors into consideration:
- Historical sales data
- Market trends
- Promotions
- Seasonality and weather
- Consumer behavior patterns
According to McKinsey, AI-enhanced demand forecasting can help distributors reduce inventory levels by 20% to 30% by using machine learning and dynamic segmentation to better predict demand.
These gains depend on the quality of the underlying data: AI models perform best when trained on accurate, timely, and consistent information from every channel.
That’s what makes a unified platform like Shopify essential—it provides the clean, centralized data from online and POS that demand-planning software and AI models need to be effective.
As the only solution to unify both sales channels on the same platform, there’s no need to custom code integrations or deal with complex middleware with Shopify. The unified data model centralizes order, inventory, and customer data by default—an approach proven to help retailers reduce total cost of ownership (TCO) by 22% and improve GMV by 8.9%, on average.
Choosing the right demand planning software
Demand-planning software makes the process easier by collecting and analyzing data so you won’t have to do it manually. Make yours work harder by integrating the software with your online store. They work best when they can access consistent data from every channel you sell through.
Demand and operations planning apps that work with Shopify stores include:
Optimize demand planning at your store
Retail demand planning may feel like predicting the future—a nearly impossible task. But analyzing both internal and external data gives you the insight necessary to better predict changes in consumer demand. It isn’t just preparation for peak seasons—it’s a year-round strategy for profitability and resilience.
Turn the information you find into a demand plan. From reassigning store associates to ordering advance inventory, take key steps to put your store in the best possible situation to handle changes in customer demand. With Shopify, unified inventory and sales data make it easier to spot trends early, adjust plans quickly, and keep each location stocked with the right products.
Demand planning FAQ
What is a demand-planning process?
Demand planning is a process used by businesses to analyze customer demand and forecast future demand for products and services. It helps companies identify customer needs and expectations, analyze market trends and competition, and plan for production and distribution.
What are the core components of demand planning?
- Data collection
- Inventory forecasting
- Inventory management
- Promotion planning
- Supply chain management
What is the role of a demand planner?
The role of a demand planner is to create and maintain accurate demand forecasts for a company’s products. This person analyzes historical sales data, industry trends, and customer demand to create a forecast of what the company can expect in terms of sales volume.
How is AI used in demand forecasting?
Artificial intelligence helps retailers forecast demand by analyzing sales data, market trends, and external factors like seasonality to predict future demand more accurately. Machine learning models can identify complex patterns and adjust forecasts in real time as new data comes in. This helps businesses reduce stockouts, optimize inventory, and improve supply chain efficiency.
