6. Manual statistical configuration¶

In this lesson, you use the monthly profile created in chapter 5 with the clusters from chapter 4 to configure two statistical models: Demand — All Locations × White Paper Finished Goods and Demand — All Locations × Tissue Finished Goods. Finally, you validate the previews without persisting a Demand Plan.

Statistical configurations by cluster combination¶

• TON UOM: converts Sales history in KG through the validated global relationship 1 TON = 1,000 KG and lets planners analyze the forecast in tons.
• Holt-Winters: represents monthly level, trend, and seasonality. See the internal model catalog and the FPP3 section on seasonal methods.
• 1,095 historical days: covers approximately three scenario years.
• Historical Sales split: the Community model that disaggregates an aggregate forecast to material-location series.

1. Open the statistical configurations by combination¶

1. Under Demand Planning, open Demand Planning Cluster-Level Configuration.

This is the real screen before selecting the three objects. Not Selected is not an error: it means the parameter editor loads only after the execution profile, location cluster, and material cluster have been chosen. This is a different screen from profile configuration: the profile provides the general context used to locate the combination.

On this screen, Product Cluster is the displayed label for the material cluster.

1. Select Execution Profile = DP_MONTHLY_TUTORIAL.
2. Select Location Cluster = Demand — All Locations.
3. Select Product Cluster = White Paper Finished Goods.
4. Wait for the saved configuration to load before editing.

When you open Forecast Model, Community Edition presents five manual options: Moving Average, Rolling Moving Average, ARIMA, Holt-Winters, and Exponential Smoothing. Fields marked PRO remain visible to preserve the screen context, but they are locked and outside this exercise.

2. Configure the white-paper statistical model¶

Fill the Community fields:

With Holt-Winters selected, the Alpha, Beta, and Gamma controls appear below the model. Keep all three on Automatic selection.

Choose Save Parameters and wait for the success message.

3. Generate the white-paper forecast preview¶

1. Under Reference Period, select February / 2027.
2. Choose Generate Forecast Preview.
3. Confirm the Forecast preview generated for 36 material-location series message.
4. Under Forecast Lag (# periods) for Error Calculation, keep Lag 0 — 2027-02-28.
5. Read the four KPI cards and both charts before opening the detail table.

The reproduced white-paper result is:

Forecast Preview connects history to the projection and exposes the direction estimated by the model. Seasonality Comparison places the months of different years side by side so you can test whether projected seasonality remains consistent with history. The table below the charts moves from the cluster aggregate to each material-location DFU and reveals where error is concentrated.

How the result reaches the DFUs¶

The model calculates the combination's aggregate series, and the Historical Sales split allocates each period across material-location DFUs according to their proportions in the configured history.

flowchart TB
  A["Aggregate group series<br/><strong>Jan 100 · Feb 120 · Mar 110 · Apr 130 TON</strong>"] --> S["Historical Sales split<br/><span style='font-size:10px;color:#64748b'>historical proportions applied to each period</span>"]
  S --> D1["DFU: Product A × North Customer · 45%<br/><strong>Jan 45 · Feb 54 · Mar 49.5 · Apr 58.5 TON</strong>"]
  S --> D2["DFU: Product A × South Customer · 35%<br/><strong>Jan 35 · Feb 42 · Mar 38.5 · Apr 45.5 TON</strong>"]
  S --> D3["DFU: Product B × North Customer · 20%<br/><strong>Jan 20 · Feb 24 · Mar 22 · Apr 26 TON</strong>"]

  classDef aggregate fill:#0b1b2e,stroke:#00bcd4,color:#ffffff,stroke-width:2px;
  classDef split fill:#e8f8f5,stroke:#00a98f,color:#0f172a,stroke-width:2px;
  classDef dfu fill:#f2efff,stroke:#7c5cff,color:#0f172a;
  class A aggregate;
  class S split;
  class D1,D2,D3 dfu;

The preview executes the real statistical backend but does not create a Demand Plan. These numbers are evidence for this scenario and reference period; they are not a universal accuracy target.

4. Repeat the configuration for tissue¶

1. Change only Product Cluster to Tissue Finished Goods.
2. Wait for the new combination's configuration to load.
3. Repeat the same model, UOM, windows, and coverage settings.
4. Choose Save Parameters.
5. Generate the forecast preview for February / 2027 and confirm the Forecast preview generated for 12 material-location series message.
6. Check the tissue aggregate: 376.9 TON in sales, -31.6 TON bias, -8.4% bias, and a displayed % MAPE of 8.4%.

Interpretation and governance¶

Both clusters start with the same model to support a controlled comparison. This does not prove Holt-Winters is the permanent winner. A mature process compares out-of-sample error, investigates bias and stability, records exceptions, and revisits parameters when demand behavior changes.

Unit failures, missing history, or empty clusters must stop validation. Never accept an all-zero preview as silent success.

Checkpoint¶

• monthly DP_MONTHLY_TUTORIAL profile with 12 periods saved as the general configuration;
• two separate statistical configurations, one for each combination of the global location cluster with the white-paper and tissue clusters;
• Holt-Winters parameters and the TON UOM saved in both configurations;
• Alpha, Beta, and Gamma on automatic selection;
• February 2027 previews generated and read without creating a Demand Plan;
• plan execution remains out of scope.

The next lesson, still under construction, creates the Planning Book collaboration view. Return to the course map or continue to 7. Run the January 2027 Demand Plan.