🔮 A Cornerstone of Effective Demand Planning

In modern supply chain management, Forecastability Analysis is a critical process that assesses the inherent predictability of demand across products or product groups.

It goes far beyond just generating forecasts — it helps organizations understand which products are easy to predict and which are not, enabling smarter decisions in planning, inventory, and operations.

By quantifying forecastability, companies can:

Optimize resource allocation

Tailor forecasting methodologies

Improve forecast accuracy and profitability

💡 Why Is Forecastability Analysis Essential?

Not all products behave the same way. Some exhibit stable, predictable demand, while others are highly volatile or erratic.

Understanding these differences allows planners to apply the right forecasting effort and technique to each type of product.

🔍 Key Benefits

Prioritizing Efforts

Focus forecasting resources on high-impact, high-uncertainty products.

Reduce wasted time and cost on items with low business impact.

Optimizing Forecasting Methods

High forecastability: Use statistical models (e.g., Exponential Smoothing, ARIMA).

Low forecastability: Combine statistical forecasts with market intelligence, such as:

Customer or distributor insights

Competitive analysis

Economic indicators or event-based factors

Improving Decision-Making

Forecastability analysis informs critical planning decisions, such as:

Forecast Hierarchy: Product level vs. product family level.

Forecast Horizon: Weekly, monthly, or quarterly time frames.

Inventory Policy: Setting safety stocks, reorder points, and service levels based on predictability.

⚙️ The Forecastability Analysis Process

Forecastability Analysis typically follows three key steps, combining data science and business logic for practical application.

Step 1. Demand History Analysis

Objective: Understand the nature of historical demand for each product.

🔹 Data Collection & Segmentation

Gather historical sales or demand data.

Segment products by sales volume, profitability, or strategic importance.

🔹 Demand Pattern Identification

Identify patterns such as:

Seasonality

Trend growth or decline

Random fluctuations

Anomalies (e.g., promotions, stockouts, or data errors)

🔹 Product Classification

Products are categorized based on their demand history and behavior:

Category	Description	Typical Action
New	Insufficient history for reliable forecasting	Use analog or judgmental methods
Emerging	Some data available; trends forming, but weak seasonality	Apply short-term models with regular review
Mature	Stable demand, strong historical patterns	Use automated statistical forecasting
Sporadic	High percentage of zero sales; irregular demand	Use demand sensing or event-based models
Obsolete	No recent sales	Phase out or set to zero forecast

Step 2. Selecting a Forecast Strategy

Once products are classified, select the most appropriate forecasting strategy and granularity.

Method Evaluation

Compare statistical methods (e.g., ARIMA, Exponential Smoothing) and non-statistical approaches (e.g., judgmental, market intelligence).

Evaluate performance using error metrics like MAPE, Bias, or RMSE.

Hierarchy & Granularity

Choose the forecasting level (SKU, product family, region).

Adjust the forecast horizon depending on demand volatility and business cycle.

Promotion & Event Modeling

Integrate external factors such as:

Promotions and discounts

Product launches

Competitive or economic events

Step 3. Quadrant Analysis

A Forecastability vs. Impact Matrix (also called Forecastability Quadrant) helps visualize where each product stands and how to manage it.

	High Impact	Low Impact
High Forecastability	✅ Automate and monitor exceptionsFocus on accuracy using statistical models (e.g., ARIMA, ETS).	⚙️ Keep it simpleUse efficient models and focus on inventory optimization.
Low Forecastability	🧩 Blend science and insightCombine statistical models with market input and risk management.	🚀 Stay agileUse pull-based replenishment, flexible inventory, and minimize forecast effort.

Example:

✍️Key Takeaways

✅Forecastability Analysis is not about improving every forecast — it’s about focusing efforts where they matter most.

For products with high forecastability:

Apply automated statistical models and implement exception-based monitoring. Review performance regularly and fine-tune parameters.

For products with low forecastability:

Shift focus from accuracy to agility. Adopt flexible inventory policies, responsive replenishment, and close collaboration with sales and customers to react quickly to real demand.

✅When embedded into the Demand Planning process, Forecastability Analysis becomes a powerful enabler of operational efficiency, customer satisfaction, and financial performance.