How to Predict Intermittent Demand with AI: A Game-Changer for Demand Planners

Did you know that businesses using AI for demand forecasting have seen forecast accuracy improve by 30-50%, inventory levels reduced by 20-30%, and carrying costs cut significantly? These results transform how companies approach one of the toughest forecasting challenges: intermittent demand.

Intermittent demand—the erratic, low-volume demand for certain products—has long frustrated demand planners. These items often lack clear patterns, making them difficult to predict. Stocking too little results in stockouts, lost revenue, and unhappy customers, while overstocking ties up valuable capital in the unused inventory of low-volume items.

Fortunately, AI is changing the game, offering tools that accurately predict intermittent demand and uncover valuable insights, such as identifying similar or replacement products. This analysis helps improve forecasting accuracy and enables demand planners to reduce SKU complexity, leading to significant cost savings.

The Challenges of Predicting Intermittent Demand

Intermittent demand products often lack clear patterns. Rare events, specific customer requests, or seasonal trends can influence their sales. Demand planners struggle with these challenges for several reasons:

• Lack of Data: Sparse historical data makes identifying trends or applying traditional forecasting methods difficult.
• Human Bias: Demand planners often prioritize high-volume or high-margin products, leaving low-volume items under-forecasted or ignored.
• Complex Influences: External factors such as market conditions, promotions, or unexpected events may significantly impact demand for these products, making manual forecasting unreliable.
• Overlapping Products: Many companies carry multiple SKUs that fulfill the same customer need, further complicating forecasting efforts.
• Overreliance on Heuristics: Planners often use simple rules of thumb, such as averaging past demand or maintaining static safety stock levels, which lack precision and can lead to costly errors.

Current Approaches for Intermittent Demand

Most businesses today still rely on outdated methods for intermittent demand forecasting:

• Manual Forecasting: Planners use gut feelings or simple spreadsheet analyses, leading to inconsistent results.
• Simple Statistical Models: Methods like exponential smoothing or moving averages fail to capture the complexity of intermittent demand and often result in overstocking or stockouts.
• Over-reliance on Safety Stock: Excessive safety stock levels act as a buffer but tie up cash in inventory and increase carrying costs.

AI offers a more advanced, efficient alternative, enabling better predictions and deeper insights, such as identifying product groups and rationalization opportunities.

How AI Transforms Intermittent Demand Forecasting

AI analyzes and leverages vast amounts of data that traditional demand planners often overlook or cannot effectively process. Here’s how AI makes intermittent demand forecasting possible:

• Integrating Diverse Data Sources
  AI models can incorporate external data such as weather trends, economic indicators, market events, and supplier lead times alongside internal data like historical sales, promotional calendars, and inventory levels. Due to their sheer volume and complexity, demand planners often overlook these sources.
• Processing High Volumes of Data Unlike human planners, AI can handle large datasets spanning many years, regions, or geographies. This enables the model to detect long-term patterns and correlations that manual processes cannot identify.
• Identifying Similar and Replacement Products
  AI can find relationships between products that serve similar functions or are used as substitutes, even if they’re not obvious. The model builds a more comprehensive picture of overall demand by analyzing demand across these groups.
• Using State-of-the-Art (SOTA) Modeling Techniques
  AI employs advanced algorithms such as time-series analysis, machine learning, and probabilistic forecasting to create highly accurate demand models. These techniques are particularly effective for products with erratic sales patterns, as they can identify subtle trends and anticipate rare spikes.
• Adapting and Learning Over Time
  AI continuously learns from new data, ensuring that models remain accurate as market conditions or customer behavior changes. This adaptability helps businesses stay ahead of fluctuations that demand planners may miss.

By analyzing this broader range of data and applying SOTA techniques, AI can make accurate predictions even for products that seem random or unpredictable.

Results and Value from AI-Driven Intermittent Demand Forecasting

AI-driven intermittent demand forecasting delivers measurable results. Companies that implement AI tools report benefits such as:

• Increased Forecast Accuracy – AI improves forecast accuracy for intermittent demand by 30-50% compared to traditional methods, reducing stockouts and overstocking. (Source: Gartner, 2023)
• SKU Rationalization & Reduction – By identifying overlapping products, companies can reduce the number of SKUs and inventory by 20-30%, freeing up valuable space and reducing carrying costs. (Source: McKinsey, 2022).
• Improved Cash Flow – Lower inventory levels free up capital for reinvestment in growth opportunities.
• Customer Satisfaction – Businesses can meet customer needs more reliably by reducing stockouts and boosting satisfaction and loyalty.
• Scalability- AI scales effortlessly across thousands of SKUs, enabling businesses to forecast demand for many intermittent products without adding manual effort.

Driving Business Value with AI Forecasting

When businesses can accurately predict intermittent demand and rationalize SKUs, they unlock significant value:

• Cost Savings: Companies can significantly cut overhead by reducing inventory costs and eliminating redundant SKUs.
• Improved Decision-Making: AI provides actionable insights that empower planners to optimize inventory and align resources effectively.
• Competitive Advantage: Companies that adopt AI can react faster to market changes, outpacing competitors relying on manual methods.

End The Guessing Game with AI

Intermittent demand forecasting no longer has to be a guessing game. With AI, businesses gain the tools to predict erratic demand patterns, uncover product overlaps, and optimize inventory management. These capabilities reduce costs and improve operational efficiency and customer satisfaction.

By investing in AI-driven demand forecasting, companies can turn a long-standing challenge into a competitive edge. Ready to unlock the power of AI for your business? Download our demand forecasting ebook or contact us today.