# Exponential Smoothing Forecasting

The **Exponential Smoothing Forecasting** report is the demand-forecasting tool production planners and S&OP leads use to project future demand from historical sales patterns. It uses the *exponential smoothing* method to extend recent demand history into a forward-looking forecast, so you can plan procurement and production ahead of demand instead of chasing it.

## Where to find it

**Manufacturing > Reports > Exponential Smoothing Forecasting.**

## Filters

| Filter | What it does |
|--------|--------------|
| **Company** | Mandatory. Scopes the historical demand data to one company. |
| **From Date / To Date** | The **forecast** window: the period the report will project demand for. Defaults to today through today + 12 months. |
| **Based On Document** | The source document the forecast learns from: *Sales Order*, *Sales Invoice*, *Delivery Note*, or *Quotation*. |
| **Based On** | The numeric field used as the demand signal: *Qty* (units shipped or ordered) or *Amount* (monetary value). |
| **Based On Data ( in years )** | How many years of history feed the forecast: *3*, *6*, or *9*. Longer history dampens noise; shorter reacts faster to recent shifts. |
| **Periodicity** | *Monthly*, *Quarterly*, *Half-Yearly*, *Yearly*. The granularity at which both history and forecast are bucketed. |
| **Smoothing Constant** | The exponential smoothing parameter, between *0.0* and *1.0*. Lower values weight history more (smoother forecast); higher values weight recent periods more (responsive forecast). *0.3* is a sensible default. |
| **Item Code** | Scope the forecast to a single item. |
| **Warehouse** | Scope to a specific stock location. |

 ![Exponential Smoothing Forecasting filter panel](https://docs.nexwaveapp.com/api/files.get?sig=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJrZXkiOiJ1cGxvYWRzL2YxMTg5ZTEzLTdlMTQtNDA1ZC04MDI1LWMxZmI0Y2Y2MTg0ZC82ZDliYThiYS0wNzJmLTRjMzYtYjY4ZC02MTM3N2YxMjkyNTEvMDEtbWYtZmlsdGVycy5wbmciLCJ0eXBlIjoiYXR0YWNobWVudCIsImlhdCI6MTc5MjYyMDkwMCwiZXhwIjoxNzkyNzA3MzAwfQ.m7r_Z39q5ssR2pKQ1ZRoi5v2reEoQy5urdYpGFEj39Q)

## What the report shows

| Column | What it shows |
|--------|---------------|
| **Item Code** | One row per item in scope. |
| **Period columns (history)** | One column per past period showing actual demand from the source document. |
| **Period columns (forecast)** | One column per future period in the From Date - To Date range, showing the projected demand. |
| **Total Quantity** | A bold subtotal row showing aggregate forecast across items. |

A chart at the top of the report plots actual demand against forecast over time, so the eye can spot under-forecast or over-forecast bias quickly.

 ![Exponential Smoothing Forecasting output with demand vs forecast chart](https://docs.nexwaveapp.com/api/files.get?sig=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJrZXkiOiJ1cGxvYWRzL2YxMTg5ZTEzLTdlMTQtNDA1ZC04MDI1LWMxZmI0Y2Y2MTg0ZC9kZjFjZTllZC04ODg1LTQxNmItYWFhZi0wOGJkZmFlZmY4NGUvMDItbWYtb3V0cHV0LnBuZyIsInR5cGUiOiJhdHRhY2htZW50IiwiaWF0IjoxNzkyNjIwOTAwLCJleHAiOjE3OTI3MDczMDB9.bkkF9uXbpTksRQ-eZl2X2UuSOq3wb3dM8nAfrgib_JI)

## Reading the numbers

* The forecast for the next period is largely driven by the most recent few periods of actuals plus the smoothed trend.
* A **flat forecast** with **volatile actuals** = the **Smoothing Constant** is too low. Increase it (e.g. 0.5) so the model reacts more.
* A **forecast that shadows the actuals exactly** = the Smoothing Constant is too high (close to 1.0). Decrease it to dampen noise.
* A persistent gap between forecast and actuals in one direction (always under, always over) is *bias*. The model is missing seasonality or a trend break; consider re-running with a different **Based On Data ( in years )** value to capture the new pattern.

## How to use the forecast

The Exponential Smoothing Forecasting report is read-only: it does not auto-create Production Plans or Material Requests. The intended workflow is:


1. Run the report at the start of each planning cycle (typically monthly).
2. Use the forecast quantities as input when creating a **Production Plan**: open the plan, populate items manually with the forecast quantities, then click **Get Material Request** and **Get Work Order** to scaffold the downstream documents.
3. Re-run the forecast as new actuals come in; the forecast for the next period will refine.

## Common pitfalls

* **Sparse history produces unstable forecasts.** Items with fewer than 6-12 historical periods of demand should be planned manually, not from this report.
* **Cancelled documents are excluded** from the historical baseline. If you frequently cancel and re-issue, consider switching **Based On Document** to *Sales Invoice* or *Delivery Note* for a more stable signal.
* **Item-level scoping over-fits when volumes are small.** For low-volume items, aggregate to Item Group or use a longer history window.
* **Periodicity mismatch.** Forecasting *Quarterly* when planning *Monthly* hides intra-quarter peaks. Match the report's periodicity to your planning cycle.
* **Forecasts in years with no history are zero.** The model needs prior actuals in the same Periodicity bucket to extrapolate; for new items, start with a manual estimate.

## Related documentation

* [Manufacturing](/doc/267f5cef-ea45-4a51-8686-30c2f3390480): module overview covering Production Plan, Work Order, BOM, and Material Requirements Planning.
* [Production Analytics](/doc/ac9621c5-939d-4e7f-b2f0-179d3b537095): historical throughput report; pair with this forecast to compare planned vs actual production.