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Data Analysis & Forecasting in FMCG

Data Analysis & Forecasting in FMCG
For Forecast Analysts Who Want to Simplify Complexity

There’s a quote often thrown around in our world:
“There are three kinds of lies – lies, damned lies, and statistics.”
Funny, but also a warning — if misused, even good data can lead us astray.

That’s why in the FMCG industry, where speed and accuracy matter, we need to turn data into insightful, actionable forecasts — not just numbers on a screen.

1. Define the Problem

🔍 Ask:

What are we forecasting? (e.g., toothpaste sales)

Time frame? (Next 6 months?)

At what level? (SKU/region/category?)

Who needs it? (Sales, supply chain?)

Clear definitions = clean direction.

2. Collect the Right Data

📊 What to gather:

12–24 months of historical sales

Weekly or monthly buckets

Promo calendars, price history, holidays

Stockouts or distribution issues

Tip: Avoid daily data for long-term — too noisy, low value for volume forecasting.

3. Analyze the Data

🔍 A. Spot the trend
Use simple Moving Average to smooth ups and downs:

=AVERAGE(B2:B4)

📈 B. Detect seasonality
Look for spikes during holidays, back-to-school, etc.
Use Excel line charts to make it visual.

⚠️ C. Find outliers
Big jump? Was there a promo or price drop? Explain the anomaly.

4. Select Your Model

⚙️ Start simple and go deeper as needed:

Moving Average – best for stable, repeating patterns

Exponential Smoothing – great for trend-sensitive products

Regression – when price, promo, or other variables drive demand

5. Forecast & Test

📈 Build your forecast using the model you trust.

🧪 Measure performance using: MAPE (Mean Absolute Percentage Error)
Good benchmark: MAPE < 15% in FMCG.

6. Validate & Improve

✅ Compare your forecast to actuals.
✅ Re-tune based on promo periods or trend shifts.
✅ Document your assumptions — transparency helps teamwork.

7. Present with Clarity

✨ Visualize the outcome:

Line graphs for trend

Bar charts for YoY or MoM

Annotate promo spikes or pricing moves

Pro Tips for Forecast Analysts:

✅ Always check data integrity
✅ Collaborate with Sales & Marketing – they have valuable context
✅ Keep a log of all assumptions – helps when results go sideways
✅ Don’t overcomplicate – simplicity scales