Forecast vs Actuals

Quick Answer: A forecast variance report shows the difference between Mo's forecast and actual sell-in (or sell-through) by SKU — surfacing where the forecast was over or under for the period. In Moselle, Mo can build one in under 5 minutes from your live data.

What is a Forecast Variance Report?

A forecast variance report compares forecasted demand against actual sales for a given period, at the SKU level. It tells you not just what sold, but how far off the plan was — and in which direction.

Every forecast will have some variance. The goal is not a perfect forecast; it is a forecast that is consistently close enough to make good inventory decisions. Variance reports are how you find where the plan is drifting and correct it before the gap creates a stockout or an overstock.

Why Forecast Variance Reports Matter

• Identify systematic over- or under-forecasting: If the same SKUs consistently miss in the same direction, it signals a structural issue with how demand is being modelled for that product

• Prioritize forecast updates: Not every SKU needs a manual review. Variance reports surface the ones that do, so you're not reviewing everything — just what's actually off

• Improve future accuracy: Tracking variance over time creates a feedback loop that makes your planning progressively more reliable

• Catch demand signals early: A SKU running significantly above forecast for two or three consecutive weeks is a signal to investigate whether underlying demand has shifted permanently

What Makes a Great Forecast Variance Report?

The most useful variance reports are:

• SKU-level: Aggregate variance numbers at the brand or category level hide the specific products driving inaccuracy

• Directional: Knowing whether the forecast was too high or too low matters as much as knowing how far off it was

• Time-bounded: Variance over the last 4 weeks tells a different story than variance over the last 12 months. Use a window that matches the decision you're making

• Percentage and absolute: A 100-unit miss on a SKU that sells 1,000 units per week is noise. The same miss on a SKU that sells 80 units per week is a major problem. Show both unit variance and percentage variance to give outliers the right weight

Key Metrics to Include

Metric	What It Tells You
Forecasted units	What the plan expected to sell in the period
Actual units sold	What actually sold (sell-in or sell-through)
Variance (units)	Absolute difference between forecast and actual
Variance (%)	Proportional gap — essential for comparing across SKUs
Direction	Over-forecast (unsold stock risk) vs. under-forecast (stockout risk)

Before You Start: Make Sure Your Data Is Clean

• Actual sales data is fully synced. Gaps or delays in your channel sales data will make variance numbers unreliable for the affected period

• The forecast covers the same period as the actuals. Make sure you're comparing the forecasted demand for the same date range as your actuals — not a shifted window

• You know whether you're measuring sell-in or sell-through. For DTC brands, these are effectively the same. For wholesale brands, sell-through (retail consumer sales) and sell-in (shipments to retail) can diverge significantly and measure different things

• Returns are handled consistently. If actuals are net of returns, the forecast should be modelled the same way for a fair comparison

How to Build a Forecast Variance Report with Mo

Time Required: 5 minutes Difficulty: Beginner

1

Open Mo and Set Your Context

Click Mo in the left sidebar to open the chat page. Be specific about the time window and what you want to compare:

"Show me forecast vs. actuals by SKU for the past 4 weeks"

"Which SKUs had the largest forecast variance last month?"

"Give me a forecast accuracy report — forecast vs. actual units — for all channels"

Defining the time period clearly in your opening prompt produces a much cleaner first output.

2

Review the Output

Mo will return a SKU-level table showing forecasted units, actual units, and the gap between them. Start by scanning for the largest absolute misses and the largest percentage variances — these are your priorities.

Separate the over-forecasted SKUs from the under-forecasted ones:

• Over-forecasted (forecast > actual): Stock was ordered on the assumption of higher demand. If this persists, it leads to excess inventory and tied-up cash

• Under-forecasted (actual > forecast): Demand was stronger than the plan assumed. If the inventory wasn't there to support it, this may have caused stockouts or missed revenue

3

Add Percentage Variance

Absolute unit variance can be misleading without scale. Add the percentage view:

"Show percentage variance alongside unit variance"

"Flag anything over 20% variance"

A 20% variance threshold is a common starting point for flagging SKUs that warrant a manual forecast review. Adjust up or down based on your tolerance and SKU volume profiles.

4

Refine and Filter

Narrow the report to the lens that matters for your current decision:

• Add "for Sephora sell-through" to compare against retail actuals instead of sell-in

• Add "show percentage variance" to view the gap as % rather than units

• Add "flag anything over 20% variance" to isolate significant misses

• Add "show only under-forecasted SKUs" to focus on stockout risk

Common follow-up asks:

"Which SKUs have been consistently under-forecasted for the last 3 months?"

"Show me the top 10 SKUs by absolute variance last quarter"

"Break this down by channel"

"Export this as an Excel file"

5

Save as a Favourite

Once the report is producing reliable output, save it for weekly reuse.

Type "Save this chat as a prompt" → copy Mo's output into a new chat to verify it runs correctly → then save it as a favourite called Weekly Forecast Variance.

Running this report weekly alongside your sales summary creates a continuous feedback loop between what you planned and what actually happened — which is the foundation of a progressively improving forecast.

How to Read Your Forecast Variance Report

Variance Signal	What It Means	Recommended Action
Over-forecast >20% (recurring)	Demand consistently weaker than modelled	Revise forecast down; review future buy quantities
Under-forecast >20% (recurring)	Demand consistently stronger than modelled	Revise forecast up; check coverage and reorder timing
Over-forecast >20% (one-off)	Possible one-time demand dip or data issue	Investigate cause before adjusting forecast
Under-forecast >20% (one-off)	Possible promotional spike or data gap	Confirm cause before revising the model
Variance <10%	Forecast is performing well for this SKU	No action needed — maintain current model

A one-week variance spike is often noise. Two to three consecutive weeks of variance in the same direction is a signal worth acting on.

Best Practices for Forecast Variance Reports

Look for patterns, not just outliers. A single week of variance is often explained by timing differences in orders or syncs. The valuable insight comes from SKUs that consistently miss in the same direction over multiple periods.

Separate sell-in and sell-through variance for wholesale brands. A large sell-in variance might not mean your forecast was wrong — it might mean a retailer placed a large order ahead of schedule. Sell-through variance is a more reliable signal of true consumer demand accuracy.

Use variance as a forecast calibration trigger, not a grading system. The point is not to penalise an off-forecast — it is to use variance data to make the next forecast better. Focus on structural patterns rather than one-period misses.

Pair variance with coverage. An under-forecasted SKU that also has low WOS is an urgent problem. An under-forecasted SKU with 16 weeks of coverage has margin to absorb the miss. Always read variance in the context of your supply position.

Set a consistent variance threshold for your team. Agree on what percentage variance triggers a manual review (commonly 15–20%) and apply it consistently. This prevents review fatigue from looking at every minor miss while ensuring real problems get flagged.

Frequently Asked Questions

What is a good forecast accuracy target?

Forecast accuracy varies significantly by industry, product type, and lead time. Most consumer brands target 70–85% accuracy at the SKU-week level. New products, highly seasonal SKUs, and promotional items naturally carry higher variance and should be tracked separately from core catalogue performance.

Should I use sell-in or sell-through for the variance calculation?

It depends on what decision you're making. Sell-in variance tells you how well your purchasing and shipment plan matched actual orders. Sell-through variance tells you how well your forecast aligned with what consumers actually bought. For most planning purposes, sell-in is the primary signal. For retail partners, sell-through is more relevant.

How far back should I pull forecast variance?

For a weekly ops review, 4 weeks is a practical window that balances recency with pattern visibility. For a quarterly forecast calibration, pull 12 weeks or longer to identify structural drift in the model.

What causes large forecast variance?

Common causes include unplanned promotions or markdowns, unexpected channel shifts, new product ramp curves, external demand shocks, or a forecast that was not updated to reflect recent velocity trends. Investigating the cause matters as much as identifying the gap.

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