Closing the Loop Between Sales Forecasting and Returns Forecasting
Retailers do everything humanly possible to ensure a precise sales forecast. They will study past sales, identify patterns, and create forecasting models that help plan production and supply. However, many of the same retailers will turn around and treat returns forecasting as random noise.
The challenge with this approach is that it leaves the forecast cycle half complete. Returns forecasting cannot be an option if retailers want accurate forecasting and better supply chain planning. It must be part of the same loop because returns can actually reshape inventory levels, cash flow, and customer demand.
This means that when retailers focus on forecasting sales, they are only planning for gross demand. However, when they factor in returns forecasts for both sales and returns, they plan for net demand, which is what matters most for financial performance and customer satisfaction.
Traditional Sales Forecasting Models Need Disruption
It is hard to imagine retail without sales forecasting. Both are deeply integrated, which is why it is common to see companies analyzing historical data, tracking past sales volumes, and applying statistical models like regression analysis or time series analysis. The detailed nature of sales forecasting is what enhances retailers’ and manufacturers‘ ability to account for all aspects of sales, including seasonal swings, pricing strategies, economic conditions, and promotions.
The benefits of sales forecasting are clear and undeniable. In fact, until recently, when returns became huge, it was the sole forecasting project retailers relied on. The fact is that accurate forecasting guides production capacity, inventory planning, and strategic planning. It helps demand planners align supply with expected demand shifts, avoid lost sales, and protect margins.
Market research and financial forecasting models make sales forecasting a central part of supply chain management. However, the world has changed, and sales forecasting can no longer give the full picture. For instance, forecasting and selling 100 T-shirts is a good start, but if you have returns of 30, your actual sales are only 70. Now imagine if you have already made pre-orders based on the sales forecast of 100. This is why treating the outbound number as the final word creates poor forecasting and distorts inventory management.
What Makes Returns Forecasting So Compelling
Two decades ago, returns were an anomaly. But today, they are predictable and measurable. Ignoring them inflates sales forecasts and leaves you with excess inventory and weak cash flow. Take, for example, in apparel and footwear, return rates often exceed 30%. In electronics, they average between 10–20%. That scale is too large to dismiss. (source)
Returns forecasting transforms gross demand into net demand, which accounts for the goods that will flow back. Factoring them ensures that financial models, inventory optimization, and warehouse planning reflect the true picture or reality. Retailers that omit returns will often have to face expensive challenges such as overstocks, markdowns, and higher supply chain costs.
If you can include that metric, your supply chain will gain more accurate forecasts, better financial forecasting, and increased customer satisfaction.
Reverse logistics is now part of the daily cycle. Companies already collect data on returned goods through warehouse management systems and e-commerce platforms. By analyzing this current and historical data with forecasting methods, companies can identify patterns in return volumes, timing, and categories. This makes returns forecasting a reliable and repeatable discipline. Ultimately, you cannot manage what you do not measure.
Drawing the Parallel: Sales vs Returns Forecasting
Sales forecasting and returns forecasting are parallel disciplines, but are calculated using similar tools and approaches. For example, both rely on historical data, require statistical models, and are shaped by consumer trends and external factors. This is why treating one as vital and the other as secondary creates an imbalance in the supply chain.
- Historical analysis:
- Sales forecasting studies past sales to project future demand.
- Returns forecasting studies past returns data to forecast future returns.
- Consumer behavior:
- Sales forecasts account for how promotions or pricing strategies influence buying.
- Returns forecasts account for behaviors like bracketing, impulse shopping, or dissatisfaction with product availability.
- Forecasting methods:
- Sales forecasts use regression analysis, exponential smoothing, and machine learning algorithms.
- Returns forecasts can use the same forecasting models, supported by predictive analytics and advanced analytics tools that identify patterns in return flows.
- Impact on inventory management:
- Sales forecasting guides outbound production capacity.
- Returns forecasting prepares for inbound flows, which shape inventory levels and warehouse capacity.
- Financial outcomes:
- Missed sales due to poor demand forecasting create lost sales.
- Ignored returns can lead to overstocking, tied-up cash in unsellable stock, and poor financial forecasting.
However, accurate and reliable forecasts, whether for sales or returns, actually depend on data quality. Poor forecasting results when data points are incomplete, late, or siloed. More accurate forecasts require vast datasets from POS systems, e-commerce platforms, and reverse logistics operations. Forecasts based on weak data will fail, whether predicting sales or returns.
Closing the Loop with Retail Forecasting Best Practices
The most effective planning comes from treating sales and returns as part of one closed cycle. This is what is called a closed-loop planning—a process where forecasts of what will go out and what will come back are integrated into a single forecast cycle.
Benefits of closing the loop:
- Sharper inventory accuracy: Aligns production and purchasing with net demand, not just the inflated projections.
- Warehouse efficiency: Balances outbound and inbound flows, which ultimately improves operational efficiency.
- Financial control: Reduces waste, avoids overstocks, and protects cash flow.
- Supply chain resilience: Accounts for reverse logistics operations, reducing exposure to supply chain disruptions.
Best practices for closed-loop planning:
- Treat returns data and sales data as equals.
- Leverage predictive analytics and machine learning when forecasting return volumes, timing, and categories.
- Integrate returns forecasting into demand planning software to adjust forecasts in real time.
- Form cross-functional teams that include demand planners, reverse logistics managers, and finance.
- Continuously refine both sales and returns forecasts using actual sales, return data, and external events like economic indicators and market shifts.
Advanced forecasting depends on both human expertise and advanced technologies. For example, machine learning algorithms can identify independent variables such as product price changes, marketing campaigns, and economic conditions that influence both sales and returns. Statistical models can then project future demand with greater accuracy and scenario planning can help companies test how external events—like economic downturns or supply chain disruptions—will affect sales and returns together.
Retailers who adopt closed-loop planning always have stronger accuracy when forecasting. Beyond that, they also have better inventory optimization and more financially viable supply chain strategies. Meanwhile, those who ignore returns forecasting remain trapped in half forecasts, leading to costly errors and disappointed customers.
Leverage ReverseLogix For Returns Forecasting
ReverseLogix is built with meaningful metrics and game-changing insights in every module. With end-to-end returns management and best-in-breed tracking and analytics, ReverseLogix is the solution to ensure your closed-loop strategy actually leverages accurate returns forecasting and data.
With ReverseLogix, you can unite all your existing business technologies and deliver metrics and data to every team member. Gain total visibility across the entire returns journey and optimize performance to stay competitive and increase your forecasting edge in real-time.
Operationalize all your data so it’s usable and understandable. Access customized reporting based on departments, user roles, or locations so that distributed teams can always stay in sync. Put ReverseLogix in your hands and unlock more value from every return.
Frequently Asked Questions
Returns forecasting gives demand planners a clearer view of inventory levels by showing not only what will be sold but also what will come back. When returns are ignored, inventory planning often leads to excess inventory or stockouts. By forecasting returns, companies can adjust forecasts, align production capacity, and improve inventory optimization. This helps maintain the right balance between product availability and cost reduction, leading to more satisfied customers and stronger cash flow.
The same forecasting methods used in sales can be applied to returns. These include time series analysis, regression analysis, exponential smoothing, and quantitative models. Advances in machine learning algorithms also make it possible to identify patterns in return data that traditional models might miss. By combining human expertise with advanced forecasting tools, companies can build forecasts that account for independent variables like price changes, economic conditions, and external factors such as supply chain disruptions.
Predictive analytics uses large datasets and statistical models to project future demand more accurately. In returns forecasting, predictive analytics can identify trends in consumer behavior, spot categories with higher return risk, and simulate how external events like economic indicators or market shifts affect return rates. By applying advanced analytics to return data, companies gain more accurate forecasts and can make informed decisions about capacity planning and production capacity.
Forecasts are only as good as the data behind them. Poor forecasting often stems from incomplete or inconsistent data points. Returns forecasting requires collecting granular data from POS systems, e-commerce platforms, and reverse logistics operations. High-quality current and historical data helps identify patterns and build forecasts with stronger forecast accuracy. With accurate data, businesses can apply financial forecasting models and make informed decisions that improve operational efficiency and reduce waste.
Closed-loop planning, where sales and returns forecasting are integrated, ensures customers find the products they want in stock while also experiencing smoother returns processes. By anticipating returns, companies can protect product availability, reduce lost sales, and make customer experience more consistent. Forecasting returns also allows businesses to design more financially viable return policies that align with pricing strategies and long-term strategic planning. This strengthens trust and improves overall customer satisfaction.
