Rolling Averages vs. Historical Data: Which Matters More for Inventory Planning?

When it comes to inventory planning, one of the biggest debates is whether to rely more heavily on rolling averages or historical data. Both approaches have their strengths: historical data offers long-term insights into seasonality and demand patterns, while rolling averages provide a sharper focus on recent trends. The real question isn’t which is better—but how to combine the two to create a balanced, effective inventory strategy.

In this article, we’ll break down the roles of rolling averages and historical data, and show you how to leverage both for smarter inventory decisions.

What Are Rolling Averages?

A rolling average smooths out fluctuations in sales by calculating the average over a specific time frame, such as the past 4, 8, or 12 weeks. Unlike historical data, which typically looks at fixed periods (e.g., last year’s holiday season), rolling averages provide a continuous and updated view of demand.

Rolling averages are particularly useful for:

• Identifying Current Trends: They highlight what’s happening now, rather than relying on past patterns that may no longer apply.
• Smoothing Volatility: For products with erratic sales, rolling averages filter out spikes and dips, giving you a clearer picture of demand.
• Short-Term Planning: They’re ideal for forecasting demand over the next few weeks or months.

What Is Historical Data?

Historical data looks at your store’s sales over a longer time horizon—typically the same time period in previous years. It’s a powerful tool for understanding:

• Seasonality: Historical data reveals recurring patterns, such as holiday shopping surges or back-to-school peaks.
• Growth Trends: Year-over-year comparisons show whether demand for a product category is growing, declining, or staying steady.
• Baseline Demand: It provides a reference point for typical sales levels, helping you plan for periods of stable demand.

When to Use Rolling Averages

Rolling averages are best suited for situations where recent trends are more indicative of future demand than older patterns. These scenarios include:

• New Product Launches: With no historical data to rely on, rolling averages help you track early sales trends and adjust stock levels dynamically.
• Short-Term Volatility: Products with inconsistent or erratic sales benefit from rolling averages, which smooth out one-off spikes or dips.
• Evolving Market Conditions: In fast-moving industries, rolling averages reflect shifts in customer behavior or competitor activity more quickly than historical data.

For example, if a new ad campaign drives a sudden increase in sales, rolling averages will capture this surge immediately, whereas historical data might miss it entirely.

When to Use Historical Data

Historical data shines in situations where long-term patterns drive demand. Key scenarios include:

• Seasonal Products: Items like holiday decorations, summer apparel, or school supplies benefit from the insights historical data provides on recurring peaks and troughs.
• Steady Sellers: Products with consistent demand over time rely more on historical baselines than recent trends.
• Strategic Planning: When forecasting for a new fiscal year or planning inventory for peak periods, historical data provides a stable foundation.

For example, if your bestsellers consistently double in sales during the holiday season, historical data ensures your forecasts account for this predictable surge.

The Case for Combining Rolling Averages and Historical Data

While rolling averages and historical data each have their strengths, the best inventory strategies combine both. Together, they offer a comprehensive view of demand, balancing the stability of long-term trends with the responsiveness of short-term insights.

Here’s how to integrate them:

1. Use Historical Data for Baselines: Start with historical data to establish the typical demand for each product over time.
2. Layer in Rolling Averages: Adjust the baseline with rolling averages to reflect recent trends, ensuring your forecast is relevant to current market conditions.
3. Apply Weights: Assign weights to each data source based on your confidence in their relevance. For instance, during stable periods, you might give 70% weight to historical data and 30% to rolling averages. In volatile periods, this weighting might flip.

This approach ensures your inventory plan is both grounded and adaptable.

Step 1: Define Time Frames

Choose the appropriate time frames for both rolling averages and historical data. For example:

• Rolling averages might cover the past 4–12 weeks for fast-moving products or 6–12 months for slower-moving items.
• Historical data might look at year-over-year performance, focusing on the same time period in previous years.

Tailoring these time frames to your products ensures you’re capturing the most relevant insights.

Step 2: Adjust for Product Types

Not all products require the same balance of rolling averages and historical data. For example:

• New or Trend-Driven Products: Prioritize rolling averages to track emerging demand patterns.
• Seasonal Items: Lean on historical data for recurring spikes but adjust with rolling averages for unexpected shifts.
• Stable Sellers: Use a blend of both to maintain inventory at optimal levels.

By categorizing products, you can create a more precise and efficient inventory strategy.

Step 3: Monitor and Refine

Integrating rolling averages and historical data isn’t a one-time effort. Monitor actual sales against your forecasts regularly to identify areas for improvement. Look for:

• Deviations: Are short-term trends overriding long-term patterns, or vice versa?
• Anomalies: Did unexpected events, like a viral product or competitor action, skew demand?
• Accuracy: Which data source—rolling averages or historical data—proved more reliable for specific products or periods?

These insights help you refine your strategy over time, ensuring it remains effective as your business evolves.

The Benefits of a Balanced Approach

Combining rolling averages and historical data gives you the best of both worlds. Key benefits include:

• Improved Forecast Accuracy: By blending long-term trends with real-time insights, you can create forecasts that are both stable and responsive.
• Optimized Inventory Levels: Avoid overstocking by staying flexible to short-term shifts, while maintaining enough inventory to meet recurring demand.
• Greater Agility: A balanced approach allows you to adapt quickly to changes in the market, ensuring your inventory strategy stays ahead of the curve.

Final Thoughts: Building a Dynamic Inventory Plan

Rolling averages and historical data aren’t competitors—they’re complementary tools that, when combined, provide a complete picture of demand. By integrating these approaches, you can create an inventory plan that’s robust enough to handle long-term trends while staying agile in the face of short-term changes.

Ready to take your inventory planning to the next level? Start balancing rolling averages with historical insights today and watch your forecasting accuracy soar.