Scaling Demand Forecasting for Thousands of Products: How AI Empowers Smarter, More Accurate Predictions

For many retail and manufacturing companies, growth often comes from acquisitions, bringing valuable products, new customer segments, and increased complexity. Forecasting demand across thousands of SKUs – each with unique patterns and often housed in disconnected systems – can quickly overwhelm traditional methods. The result? Risky stockouts for high-demand items, costly overstock of slower-moving products, and significant inefficiencies in the supply chain.

According to McKinsey, companies that adopt AI-driven forecasting can reduce errors by 20% to 50%, decrease warehousing costs by up to 10%, and cut lost sales from unavailability by as much as 65%. These results demonstrate the transformative power of AI in managing demand forecasting for complex product portfolios. For demand planners and business leaders, leveraging AI isn’t just a solution to existing challenges – it’s a strategic advantage for scaling operations and staying competitive in an increasingly dynamic market.

The Challenges of Demand Forecasting for Expansive Portfolios

Managing demand forecasting across thousands of products introduces several hurdles:

• Data Complexity: Acquired products often come with their own data histories and systems, making integration time-consuming and error-prone. Inconsistent formats and disconnected systems challenge forecasting across large portfolios.
• Diverse Demand Patterns: High-volume, high-margin products drive revenue, while others may be less predictable. Using a single forecasting approach across all products can lead to missed opportunities or overstock, especially for items with niche demand.
• Resource Constraints: Large portfolios strain demand planners, who often lack the resources to adjust forecasts for every product effectively. Traditional tools struggle to support the scale and complexity without substantial manual intervention.
• Scalability Issues: As companies expand, demand planning needs to scale accordingly. Without a flexible solution, forecasting becomes a bottleneck that can impact profitability.

Segmenting Products for More Targeted Forecasting

One effective way to manage demand forecasting complexity is through product segmentation. By grouping products based on characteristics like revenue impact, volume, demand stability, or margin, demand planners can apply targeted strategies for each segment:

• High-Priority Products: These items are high-revenue or high-margin, where accuracy is crucial. Advanced AI models at an individual product level might be appropriate. These models can detect subtle demand patterns to create accurate and reliable forecasts.
• Moderate-Priority Products: These are important but less critical than your high-priority items. Forecasts for these products can benefit from sophisticated AI techniques to create a single model that supports forecasting demand for each product in the group.
• Low-Priority Products: Traditional manual forecasting techniques often leave the “long tail” of products with low margins out of the forecasting process. The good news is that with some intelligent grouping of such products, AI models can learn from the data across product categories and create forecasts even for low-volume products.
• New Products: Similar to low-volume products, new products don’t have data to build demand plans around. With AI, demand planners can use similar products and attributes to predict new product demand.

The Power of AI for Demand Forecasting

AI-driven demand forecasting is well-suited for companies with diverse, growing product lines. Here’s how AI can help:

• Automated, Scalable Modeling: AI can apply multiple forecasting models for different product segments with automated training and retraining processes. This scalability is essential for handling large SKU bases spread across varied systems, ensuring each segment remains accurate with minimal manual intervention.
• Enhanced Accuracy with Machine Learning: Machine learning models can uncover demand patterns that traditional methods might miss. AI improves forecast accuracy across the board using the best-fit model for each segment.
• Integration Across Data Sources: An AI platform, like Sway AI, can simplify data integration across multiple systems, consolidating disparate data into a unified forecasting environment. This is particularly valuable when incorporating products with different data systems into a single forecast.
• Adaptability: AI-based systems can adapt quickly, which is beneficial for high-priority items where even minor fluctuations impact inventory decisions. This real-time adaptability allows companies to respond to shifts without extensive manual adjustments.
• Efficiency with Forecast Automation: Automation in AI-driven systems helps planners manage data preparation, model training, and forecast generation efficiently, allowing them to focus on strategic decisions for high-value products rather than routine data processing.

The Value of AI for Companies with Growing Portfolios

AI-driven demand forecasting is particularly beneficial for companies with expanding product lines:

• Improved Forecast Accuracy: AI models reduce forecast errors, improving stock management, customer satisfaction, and reducing costs from overstocking or stockouts.
• Efficient Resource Allocation: AI-driven automation minimizes the need for manual data preparation, allowing planners to allocate resources effectively and focus on high-value products.
• Scalable Demand Planning: AI ensures demand planning scales with growth, without proportional increases in resource needs, supporting sustainable expansion.
• Data-Driven Insights: AI provides actionable insights for prioritizing products, selecting forecasting models, and updating forecasts at optimal times for the business.

A Scalable Solution for Complex Demand Forecasting

AI-based demand forecasting provides a scalable, efficient, and accurate solution for companies managing large product portfolios. With AI, companies can segment products and apply tailored models, making forecasts more precise. As portfolios grow, AI-based forecasting offers the flexibility to respond to changing demands, ensuring that demand planning remains manageable and effective.

Sway AI is purpose-built to empower demand planning teams to tackle advanced demand forecasting for large product catalogs – without requiring advanced data science expertise. Its intuitive no-code interface, pre-built components, and access to state-of-the-art forecasting models make creating more accurate forecasts across an entire product portfolio easy. From high-margin, high-priority products to the long tail, Sway AI equips teams with the tools to optimize demand forecasting.

With Sway AI, demand planning becomes a proactive, strategic advantage. Teams can reduce costs, improve decision-making, and streamline supply chain efficiency, all while keeping pace with the growing complexity of today’s markets. Sway AI transforms the challenge of forecasting into a powerful enabler for business growth and operational success.

To learn more about demand forecasting with AI, download the ebook today!