In today’s fast-moving and volatile markets, demand planners face challenges that traditional forecasting approaches simply cannot handle. Forecasting models that only rely on internal company data—such as sales history, stock levels, or promotions—are no longer enough. Market shifts, economic developments, and external shocks often play a bigger role than anything visible inside your ERP or planning system.

Unfortunately, very few demand planning tools are built to incorporate these external signals. The result? Demand planners are left with blind spots that can cause costly mistakes. That’s why you don’t necessarily need a new demand planning software—you need a forecasting software that integrates seamlessly with your existing system. These solutions fetch historical data from your demand planning software, enrich it with external factors, and then export high-quality forecasts back into your workflow.

Below, we explore 8 of the best demand forecasting software platforms for demand planners in 2025.

1. Indicio

Indicio is a next-generation forecasting platform designed for demand planners who want to go beyond the limitations of traditional planning tools. It stands out by combining internal company data with external factors such as macroeconomic indicators, commodity prices, consumer trends, and financial signals. Using its advanced factor-selection engine, Indicio identifies which drivers matter most for your specific products or markets. Its no-code interface makes it highly accessible, while scenario simulation features help teams prepare for multiple possible futures. Indicio also integrates with external data providers like Macrobond, Bloomberg, Refinitiv, Fred, Eurostat, and more than 20 others. With an extensive library of statistical, machine learning, Bayesian, mixed-frequency, and penalized models, it’s built to give demand planners an edge.

Pros:

• Incorporates both internal and external drivers, with automated factor identification.
• No-code interface with advanced scenario simulations.
• Direct integrations with 20+ external data sources.

Cons:

• Higher cost compared to open-source forecasting packages.
• Does not allow custom-coded models.
• Requires a stable internet connection.

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2. SAS Forecast Server

SAS Forecast Server is a long-standing solution trusted by enterprises with complex forecasting needs. It is particularly strong in handling large-scale datasets and running automated model selection across thousands of time series simultaneously. The software’s statistical depth ensures robustness, making it a common choice for organizations in retail, manufacturing, and utilities. However, its learning curve and reliance on technical expertise make it less accessible to teams without a strong analytics background.

Pros:

• Handles very large-scale forecasting across thousands of series.
• Deep statistical rigor and automation for model selection.
• Enterprise-grade support and reliability.

Cons:

• Steep learning curve; requires advanced skills to use effectively.
• Interface is less modern and user-friendly compared to newer tools.
• Licensing and support costs can be high.

3. Blue Yonder Luminate Demand Edge

Blue Yonder’s Luminate Demand Edge is built specifically for retail and consumer goods, offering AI-powered forecasts that adapt quickly to demand signals. It integrates internal transactional data with causal factors like weather, promotions, and holidays to provide more accurate insights. Luminate’s cloud-native architecture and real-time processing make it especially strong for businesses dealing with rapid demand fluctuations. However, it is more limited in economic and market-level external data integrations compared to dedicated forecasting platforms.

Pros:

• Strong AI-driven demand sensing capabilities.
• Tailored for retail and consumer goods industries.
• Cloud-native, scalable, and real-time ready.

Cons:

• Primarily optimized for retail use cases, less flexible for other industries.
• Limited integrations with broader macroeconomic data sources.
• Implementation costs and time can be significant.

4. SAP Integrated Business Planning (IBP)

SAP IBP is one of the most widely adopted demand planning solutions, especially among enterprises already running SAP ERP. Its demand forecasting capabilities are built into a broader supply chain planning suite, offering strong collaboration and integration features. While SAP IBP is excellent at aligning demand with supply and financial planning, its native forecasting engine is relatively limited to internal data. Companies often pair IBP with external forecasting software to enrich its capabilities.

Pros:

• Deep integration with SAP ERP and supply chain modules.
• Strong collaboration and workflow management.
• Good for aligning demand forecasts with financial and supply planning.

Cons:

• Native forecasting is limited and often too simplistic.
• Heavy reliance on SAP ecosystem; limited flexibility.
• High total cost of ownership.

5. o9 Solutions

o9 Solutions offers a digital brain for integrated business planning, with AI-driven forecasting capabilities. It emphasizes connecting demand, supply, and financial planning while enabling scenario planning across different horizons. The platform integrates a variety of data sources, but it focuses more on internal business drivers and structured data rather than external macroeconomic or alternative datasets. While powerful, o9 often requires customization and long implementation projects.

Pros:

• Strong scenario planning and digital twin capabilities.
• Comprehensive integration of demand, supply, and finance.
• AI and analytics embedded across the platform.

Cons:

• External data coverage is limited compared to specialized forecasting tools.
• Implementation can be lengthy and resource-intensive.
• Costs are high for mid-sized businesses.

6. Oracle Demand Management Cloud

Oracle Demand Management Cloud is part of the Oracle Supply Chain Management suite. It provides statistical and machine learning–based forecasting, with built-in collaboration and demand sensing features. Its strength lies in integrating demand forecasts with supply and operations planning. However, while it includes causal factors such as promotions or seasonality, it does not natively cover wider macroeconomic or market datasets. Organizations often need to feed it enriched forecasts from external platforms.

Pros:

• Robust statistical and ML-based forecasting models.
• Strong integration with Oracle SCM ecosystem.
• Good demand sensing and collaboration features.

Cons:

• Limited support for external macroeconomic drivers.
• Requires Oracle ecosystem buy-in for maximum value.
• Can be complex to configure and maintain.

7. Forecast Pro

Forecast Pro is a well-established forecasting solution known for its accessibility and depth of statistical models. It is particularly popular among mid-sized businesses and academic users. The tool makes it easy to build forecasts with time series methods and causal models, though it requires manual work to incorporate external drivers. While not as flashy as newer AI-driven platforms, Forecast Pro offers a practical balance of functionality and affordability.

Pros:

• Wide range of statistical models with transparent methodology.
• Relatively easy to learn and use.
• More affordable than most enterprise solutions.

Cons:

• Limited automation compared to modern AI tools.
• Weak integrations with external data sources.
• Not as scalable for very large organizations.

8. Anaplan

Anaplan is best known as a connected planning platform that supports financial, sales, and supply chain planning. Its demand forecasting capabilities are flexible thanks to its modeling environment, allowing teams to build customized planning workflows. Anaplan is strong in cross-functional collaboration, but its forecasting strength depends on what users build themselves. External factor integration is possible but usually requires manual setup or third-party connectors.

Pros:

• Highly flexible and customizable modeling environment.
• Strong collaboration across business functions.
• Cloud-based and scalable.

Cons:

• Out-of-the-box forecasting features are limited.
• Setup and maintenance require significant expertise.
• External factor integration is not automated.

Final Thoughts

Choosing the right demand forecasting software isn’t about replacing your demand planning system—it’s about enhancing it. Tools like Indicio stand out by incorporating both internal and external drivers, giving planners a sharper view of the future. Others, like SAP IBP or Oracle Demand Management Cloud, excel at integration and workflow but rely on enriched forecasts from specialized forecasting engines.

In volatile markets, the difference between a forecast built on internal history alone and one enriched with market, economic, and external data can be the difference between being blindsided and staying ahead.

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