> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dema.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Inventory forecasting

> Understand how historical data is transformed into actionable business insights.

<Info>
  Dema's inventory forecasting engine transforms your historical sales data and
  product characteristics into precise, forward-looking demand forecasts for the
  remainder of your selling season. These intelligent insights empower you to
  optimize inventory levels, implement strategic pricing, and allocate marketing
  resources with confidence—preventing costly stockouts and reducing excess
  inventory before they impact your bottom line.
</Info>

## How to access

The forecasts are available in the *Reports* section of the platform as selectable metrics.

<div class="container">
  <iframe src="https://demo.arcade.software/Y2fmXvZsZnfFHYGzBrhb?embed&embed_mobile=inline&embed_desktop=inline&show_copy_link=true" title="LTV forecasting" frameborder="0" loading="lazy" webkitallowfullscreen mozallowfullscreen allowfullscreen allow="clipboard-write" class="responsive-iframe" />
</div>

## How our forecasting engine works

<CardGroup cols={2}>
  <Card title="Core algorithm" icon="cubes">
    The system is built on *gradient boosted decision trees*, an advanced
    algorithm architected for large-scale retail datasets, delivering speed and
    accuracy for complex inventory forecasts.
  </Card>

  <Card title="Continous model learning" icon="gears">
    Incorporate new sales patterns, seasonal shifts, and market changes to
    maintain forecasting precision throughout your selling cycle once a day and
    update your forecasts accordingly.
  </Card>
</CardGroup>

### Core algorithm

The engine employs an ensemble of decision trees that systematically analyze your data through intelligent questioning patterns. For example: *"Did this SKU experience strong performance in the European market during last spring's campaign?"* or *"How did similar products in this category perform during comparable promotional periods?"*

By aggregating insights from thousands of these decision trees, the model generates comprehensive **end-of-season sales forecasts** rather than traditional day-by-day forecasts. This approach provides more stable and actionable forecasts for strategic inventory planning.

<Tip>
  Consumer preferences and market conditions evolve rapidly. Configure automatic
  model retraining to trigger after significant changes in your product
  assortment, pricing strategy, or distribution channels to maintain optimal
  forecasting accuracy.
</Tip>

### Hierarchical architecture

The system employs a bottom-up approach to ensure that both granular inventory decisions and high-level strategic planning benefit from consistent, accurate data.

The resulting output is a collection of individual end-of-season sales forecasts, each on the smallest granular level, enabling you to make the most informed decisions.

**Distribution Center** → **Product Category** → **Individual SKU/Variant**

This approach enables accuracy from the ground up, ensuring that detailed insights aggregate correctly to provide reliable top-line forecasts.

### Features

The system uses a combination of historical performance indicators, product attribute analysis, and market context variables to create a comprehensive feature set.

* Trend velocity and seasonal patterns
* Geographic variations
* Product consistency and demand metrics
* Price point positioning across product hierarchy
* Brand equity and collection performance history
* Marketing spend activity across available product hierarchies

<Note>
  Feature importance can differ greatly across categories, brands, warehouses,
  and regions. Our machine learning model dynamically adapts to these
  differences by identifying and prioritizing the most predictive features based
  on your specific business context and customer behavior.
</Note>

***

## Forecasted metrics

The system generates a comprehensive set of forecasted metrics that can be used to make informed inventory decisions which can be accessed in the *Reports* section of the platform.

| Metric                                     | Description                                                                                                                                                                                                                                           |
| ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Forecast incoming returns**              | Estimates the number of products expected to be returned on already sold orders for the rest of the season, helping anticipate inventory adjustments and returns impact. Based on historical trends and inputted return rates.                        |
| **Forecast gross products sold**           | Forecasts the total quantity of products expected to sell during the rest of the season, aiding in demand planning and promotional strategies.                                                                                                        |
| **Forecast return percent**                | Estimates the percentage of sold products that are likely to be returned during the rest of the season, based on historical trends and inputted return rates.                                                                                         |
| **Forecast net products sold**             | Estimates the number of products expected to sell during the rest of the season, adjusted for historical return rates. This metric helps in planning inventory and marketing strategies by forecasting future sales performance based on past trends. |
| **Forecast plus actual net products sold** | Combines forecasted net product sales with actual sales to date, providing a comprehensive view of seasonal sales performance.                                                                                                                        |
| **Forecast sell-through**                  | Calculates the ratio of forecasted net products sold to total available stock (current, incoming, and forecasted returns), providing insights into inventory efficiency.                                                                              |