Demand Forecasting in the Age of AI & Machine Learning

Businesses face different inventory challenges when they are dealing with supply chains. Addressing supply chain issues is paramount. Demand forecasting enables businesses to reduce supply chain costs and achieve significant improvements in financial planning, capacity planning, profit margins, and risk assessment decisions.

Demand forecasting is the most widely used machine learning application in supply chain planning. Study shows that 45% of companies are already utilizing the technology, and 43% of them plan to implement AI-powered demand forecasting within the next two years.¹

Machine learning algorithms improve forecasting methods in accuracy and optimize replenishment processes. With these advances, companies are minimizing the costs associated with cash-in-stock and out-of-stock scenarios. Here, we explain demand forecasting in detail.

What is demand forecasting?

Demand forecasting is a field of predictive analytics, and as its name suggests, it is the process of estimating customer demand by analyzing historical data. Organizations use demand forecasting methods to avoid inefficiencies caused by the misalignment of supply and demand across the business operations.

With demand forecasting methods, companies can improve their decision-making processes about cash flow, risk assessment, capacity planning, and workforce planning.

What is demand forecasting analytics?

Demand forecasting analytics refers to the use of data, statistical algorithms, and machine learning techniques to predict future customer demand. It is the analytical engine behind modern supply chain planning and includes several approaches:

• Time series forecasting: Based on historical trends (e.g., ARIMA, exponential smoothing)
• Causal models: Incorporates external drivers such as pricing, marketing campaigns, and economic indicators
• Machine learning models: Uses advanced algorithms like XGBoost, LSTM, and Prophet to process both structured and unstructured data
• Hybrid approaches: Combine internal operational data with external factors like weather, social media, and macroeconomic variables

Demand forecasting analytics helps companies move from reactive inventory planning to a proactive, data-driven strategy that adapts in real-time.

AI in Demand Forecasting

According to Mckinsey Digital, AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks ² The improved accuracy leads to a 65% reduction in lost sales due to inventory out-of-stock situations, and warehousing costs decrease around 10 to 40%. The estimated impact of AI in the supply chain is between $1.2T and $2T in manufacturing and supply chain planning.³

Traditional forecasting models are becoming redundant

Traditional forecasting models such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods, where only historical data is considered, are getting outdated because of the increased amount of data generated from businesses and external sources. With the implementation of machine learning into businesses’ supply chain management, companies can improve the accuracy of forecast results and optimize their replenishment plans.

Machine learning carries demand forecasting to the next step; it enables enhanced forecasts based on real-time data using internal and external data sources such as demographics, weather, online reviews, and social media. With the help of external data and modern machine learning algorithms, supply chain networks can outperform networks managed more manually by data analysts and adapt to external changes.

For new products that lack historical data, machine learning forecasting tools can identify clusters of prior products with similar characteristics and lifecycle curves and use those datasets as a substitute to make predictions.

How Machine Learning Improves Demand Forecasting

1. Multi-variable analysis: ML models can consider multiple variables simultaneously—price changes, promotions, seasonality, weather, social media sentiment, economic indicators—yielding a more holistic forecast.
2. Nonlinear pattern detection: Algorithms like Random Forest, XGBoost, and LSTM can capture complex, nonlinear relationships between inputs and demand that traditional statistical models often miss.
3. Real-time adaptability: ML models can be retrained frequently, allowing businesses to adapt to sudden changes such as supply disruptions, viral trends, or economic shocks.
4. Support for cold-start products: When historical data is unavailable (e.g., for new product launches), ML can use clustering techniques or similarity matching to predict demand based on comparable products.

Common ML Models Used

• Regression models (Linear, Ridge, Lasso) – simple but effective for structured data
• Tree-based models (Random Forest, XGBoost, LightGBM) – robust and interpretable
• Deep learning models (LSTM, GRU, TCN) – ideal for time series data with sequential dependencies
• Hybrid models – combine time series and causal forecasting for high accuracy

Evaluation Metrics

To measure ML forecast quality, companies typically use:

• MAE (Mean Absolute Error)
• RMSE (Root Mean Square Error)
• MAPE (Mean Absolute Percentage Error)

Regular evaluation and backtesting are critical to ensuring models remain accurate over time.

What are the benefits of demand forecasting?

Demand forecasting helps organizations optimize their supply chain, sales, and marketing operations and prevent having excessive amounts of goods in stock or out-of-stock situations:

Improving accuracy by time

Machine learning algorithms learn from existing data and make better predictions over time.

Increased customer satisfaction

Stockouts reduce customer satisfaction, while being available with your product boosts customer satisfaction. Thus, it improves brand perception and increases customer loyalty.

Improved markdown/discount optimization

Cash-in-stock is a common situation for retail businesses. In this situation, certain products stay unsold longer than expected. This results in higher-than-expected inventory costs and increases the risk that these products will go out of fashion or become obsolete, thereby losing their value.

In these scenarios, organizations sell their products with reduced margins. With accurate demand forecasting, such scenarios can be minimized.

Improved workforce planning

Demand forecasting for the full year can support HR departments to make efficient trade-offs between the part/full-time employee mix, optimizing costs and HR effectiveness.

Overall efficiency

Accurate demand forecasts help teams focus on strategic issues rather than firefighting to reduce/increase stocks and headcount to manage unexpected demand fluctuations.

What are the common pitfalls of demand forecasting?

Any business may fail as a result of preventable pitfalls and cannot reduce the cost of inventory as much as they aim. Businesses can avoid these unfortunate results by taking measures to look for these all-too-common pitfalls:

• When the marketing data is excluded, the error margin of demand predictions may increase since marketing data and marketing effectiveness have a big impact on future sales.
• New products don’t have sufficient historical data. That’s why organizations’ accuracy expectations should not be high for newly released products.
• If the organization’s supply chain network is hard to take different actions due to its inflexibility, the business struggles to obtain the value of demand forecasting. The purpose of demand forecasting is to make changes to reduce the cost of supply chain management by optimizing processes.
• Forecasts can only be as good as the input data. Companies need to ensure that important changes are reflected in real-time in their data sources.
• Since demand forecasting is a field of predictive analytics, common pitfalls of analytics also apply to demand forecasting as well.

What are modern AI-powered demand forecasting tools?

Real-life case study

FutureMargin is an AI-powered demand planning software. One of its customers is Martinus, a major East European omnichannel e-commerce bookstore. The partnership of FutureMargin and Martinus resulted in⁴ :

• 84% increase in the ratio of products expedited on the day of placed order
• Improvements in average order fulfillment time by 14%.

External Links

• 1. Gartner Login.
• 2. https://www.mckinsey.com/~/media/McKinsey/Industries/Semiconductors/Our%20Insights/Smartening%20up%20with%20artificial%20intelligence/Smartening-up-with-artificial-intelligence.ashx%20str%209
• 3. Visualizing the uses and potential impact of AI and other analytics | McKinsey. McKinsey & Company
• 4. https://futuremargin.com/assets/img/case-study/futuremargin_demand_planning_case_study.pdf