How to Improve Forecast Accuracy in S&OP

Why better forecasting is not a luxury — it’s a strategic necessity.

1. Start with the Right Forecasting Model — Not Just Tools

Many companies rush to implement statistical tools or machine learning algorithms without first understanding the nature of their demand.

Before automating anything, assess:

Demand pattern (stable, intermittent, seasonal, erratic)
Granularity (by SKU, by channel, by region…)
Forecast horizon (short vs long term, tactical vs strategic)

➡️ For stable SKUs with high volume, simple models like moving averages or exponential smoothing may outperform complex algorithms.

➡️ For volatile items, a hybrid approach combining statistical models and manual overrides may be best.

📌 Best practice: Build a forecast segmentation grid by SKU and apply differentiated logic per cluster.

2. Bridge the Gap Between Commercial and Operations

The number one reason forecasts fail? Misalignment between sales, marketing, and supply chain.

Sales overstates. Operations mistrusts. Finance doesn’t reconcile either version.

The solution: a collaborative forecasting process within your S&OP.

Involve commercial teams in demand reviews — but challenge assumptions with data (historical sales, campaign uplift, cannibalization).
Establish a monthly consensus cycle where each function “owns” part of the forecast logic.
Track Forecast Value Added (FVA) by contributor to identify which manual inputs improve or degrade accuracy.

📊 FVA = (Error of baseline model – Error of adjusted forecast) / Error of baseline

3. Leverage Historical Data — But Don’t Be Its Slave

Historical sales data is the backbone of any forecast. But raw data isn’t insight. Clean it first:

Remove outliers (promotions, stockouts, one-off spikes)
Normalize for changes (price, packaging, channel migration)
Adjust for lost sales (stockouts) to avoid false negatives

Then, supplement with external drivers: market trends, inflation, seasonality shifts, macro events. The best forecasting models combine internal patterns and external signals.

💡 Example: In cosmetics, weather data (temperature drops) often predicts surges in moisturizing product sales.

4. Measure Forecast Accuracy — But Measure the Right Way

You can’t improve what you don’t measure. Yet many companies rely only on MAPE (Mean Absolute Percentage Error), which can be misleading for low-volume SKUs.

🧮 Consider using:

WAPE (Weighted Absolute Percentage Error) — gives more weight to high-volume items
Bias (systematic over/under-forecasting)
Forecast Accuracy by portfolio (ABC / XYZ segmentation)

➡️ Don’t just aim for one global accuracy number. Instead, build a dashboard by segment, region, horizon, and assign improvement targets per stream

5. Close the Loop: Forecast, Measure, Improve

Forecasting isn’t a one-time activity — it’s a living process. Embed a continuous improvement loop:

Forecast creation
Execution comparison (actuals)
Root cause analysis (why the delta?)
Process learning and refinement

Some clients of CoreChain run a post-S&OP retrospective every quarter, analyzing top forecast gaps and identifying if the root cause was data, process, or assumption.

This process builds forecasting maturity over time — not just accuracy in the next cycle.

Conclusion

Forecast accuracy is not about perfection — it’s about consistency, transparency, and accountability. When done right, it reduces firefighting, stabilizes operations, and creates a common language across teams.

At CoreChain, we help our clients improve forecast accuracy by 15–25% in just a few months, by redesigning their process, segmenting their product logic, and embedding metrics like FVA and WAPE into their operating rhythm.

Want to explore how accurate your forecasts really are — and what you can do to improve them?

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