✨Forecasting
Forecasting predicts future customer demand based on historical sales data, trends, and seasonal patterns. Accurate forecasts enable brands to order the right quantities at the right time.
How Moselle builds your forecast
Moselle uses machine learning to generate SKU-level demand projections across a 12-month horizon. Here's what's happening under the surface.
It starts with your sales history Mo analyzes your historical order and sales data to identify baseline demand patterns — how much of each SKU sells in a typical week, how that shifts across seasons, and how different channels perform relative to each other.
It accounts for seasonality and trends Mo separates recurring seasonal patterns (a product that always spikes in November) from actual trend changes (a product that's genuinely growing or declining). These are different signals, and treating them the same is one of the most common forecasting mistakes.
It flags anomalies Unusual demand events — a viral moment, a stockout that suppressed real demand, a one-time bulk order — can distort a forecast if they're treated as normal. Mo detects these and adjusts so they don't inflate or deflate your baseline.
It updates continuously Your forecast isn't static. As new sales data comes in, Mo recalculates. If demand shifts, your projections shift with it — without you having to manually revise anything.
Two ways to look at your forecast
Moselle supports both bottom-up and top-down planning views:
Bottom-up builds from the SKU level upward — useful for operational decisions like replenishment and production scheduling, where SKU-level accuracy matters.
Top-down starts from an overall revenue or category target and distributes it down — useful for financial planning and budgeting.
You can use both views and compare them. Where they diverge is usually worth a closer look.
A note on forecast accuracy
Moselle gets you 80% of the way there on day one — a working forecast grounded in your actual data, without any manual setup or formula-building. That 80% is enough to make better decisions than most brands are making today.
The remaining 20% is where your judgment comes in. Mo's forecast is built from signals it can see — historical sales, seasonality, channel trends. It doesn't know about the promotion you're planning next month, the supplier delay you just heard about, or the new product launch that's going to shift demand in a category. That context is yours to add.
You can see how accurate Mo's projections have been for any SKU or category. If a forecast consistently misses in one direction, that's a signal worth investigating — either there's context Mo doesn't have, or there's a pattern in your demand that hasn't been captured yet.
Override when you have information Mo doesn't. Add context, adjust the projection, and Mo will recalibrate. Over time, as Mo learns your business, the gap between the forecast and reality narrows.
Why Forecasting Matters
Generate Revenue — Inventory availability when customers want to buy. Stockouts mean lost sales to competitors.
Reduce Costs — Avoid overstocking that ties up cash and increases carrying costs.
Catch Mistakes Early — Surface anomalies before they become costly problems.
Demand Planning vs. Forecasting
Forecasting is the analytical process of predicting future demand — the numbers.
Demand Planning is the broader operational discipline that uses forecasts to make inventory decisions, coordinate with suppliers, and align cross-functional teams.
Forecasting is an input to demand planning. Moselle handles both: generating forecasts automatically and translating them into actionable replenishment and production plans.
Measuring Accuracy with MAPE
MAPE (Mean Absolute Percentage Error) measures the average percentage difference between forecasted and actual values.
< 10%
Highly accurate
10-20%
Good
20-30%
Reasonable
> 30%
Needs improvement
Moselle tracks MAPE automatically by SKU, channel, and time period.
Top-Down vs. Bottom-Up Forecasting
Top-Down
Starts with revenue goals and works backward to unit requirements.
Set revenue target → calculate each SKU's contribution → determine units needed
Best for: new brands, stable demand patterns, financial alignment
Bottom-Up
Analyzes each SKU individually and aggregates upward.
Analyze historical sales per SKU → project demand → aggregate totals
Best for: rich historical data, seasonal products, complex catalogs
Recommended Approach
Use both. Build a top-down forecast from revenue goals, generate a bottom-up forecast from SKU data, compare, and reconcile. Moselle automates this process.
Phase 1: Understand ForecastingPhase 2: Build Your ForecastPhase 3: Refine & Maintain
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