Inventory Forecasting with AI #10

Inventory Forecasting with AI

10. Final model

Final Test – Full Dataset

Moment of truth: How does the model perform when run on all 15,000 items? Model and data configuration (based on previous findings):

Feature engineering: 120 features (combination of individual sales and group-features)
Outlier trimming
Merging sparse categories
Training only on items with at least 1 month of history
Encoder length: 18 months
Architecture: TFT
Validation: last 3 months not included in training

For the final test, I selected a model with group features but without synthetic data. This setup proved to be the most stable across the entire assortment, although synthetic data remains an interesting option for future projects or specific subsets of products.

Metrics	Full dataset		33 months		13-32 months
Metrics	Value (ALL)	Value (Item)	Value (ALL)	Value (Item)	Value (ALL)	Value (Item)
WAPE	30.1022	49.7127	28.1069	41.6723	33.4304	40.1634
RMSE	45.907	22.3895	50.4051	23.9145	26.3204	17.0358
R²	0.9232		0.93		0.7245
MAPE	65.5438		57.332		60.9407
ROBUST	0.551	72.2735	0.5498	58.7128	0.5049	66.3819
STABLE	0.3884		0.3642		0.5422

Metrics	6-12 months		1-6 months		no history
Metrics	Value (ALL)	Value (Item)	Value (ALL)	Value (Item)	Value (ALL)	Value (Item)
WAPE	38.5444	45.8518	44.6778	47.1055	80.7317	220.7146
RMSE	26.6287	17.1458	43.1306	27.5178	95.9705	61.587
R²	0.7529		0.7451		-0.1913
MAPE	64.676		79.1142		186.139
ROBUST	0.5233	66.5118	0.5441	89.7998	0.4013	250.967
STABLE	0.4801		0.4776		0.9196

Results by history length:

Full history (36 months) – WAPE around 30% (ALL), high R² (0.92+).
Medium history (12–35 months) – similar results to full history, with only a slight drop in R².
Short history (6–12 months) – still solid forecasts, WAPE around 38%.
Very short history (1–6 months) – higher errors, but still usable for rough planning.
No history – predictions remain unusable; the model has no data point to determine the correct scale.

Compared to Chapter #9 (partial dataset test), the metrics are practically identical – the model maintains stability even with triple the dataset size. A slight improvement is visible for SKUs with short history, mainly thanks to a greater number of categories within groups → richer embedding data → better pattern transfer.

Visualization

The charts compare the prediction from the partial dataset (previous chapter) with the new prediction from the full dataset.

🔴 Red – prediction from the model on the partial dataset.
🟢 Green – prediction from the final model on the full dataset.

The most visible differences are found in SKUs with short history – here, group-level information now plays a much stronger role. For longer histories, the curves almost perfectly overlap, confirming that expanding the training dataset to the full range did not harm performance for established items.

Promo Campaigns

The model performs very well on the full dataset, so I also tested how it handles promotional campaigns. In the training data, the model receives information about when and how long a given product was on sale. Within feature engineering, I also add additional context:

how often the item has promotions
how long promotions typically last
what their historical impact on sales has been

The visualization shows the difference in forecasts when an item had a planned discount vs. no discount.

🔴 Red – without planned discounts
🟢 Green – discount applied in the last 3 months

Where history confirms the success of promotions, the sales forecast increases.
Where past campaigns have not worked, the curve remains almost unchanged (e.g., C511478).

This shows that the model can make decisions based on the actual historical impact of promotional events, and that a discount by itself is not a universal trigger for increased sales.

Production Model

To deliver a complete solution to the customer, I trained the final version of the model – this time on the entire 42-month history (some months months have passed since the start of the project).

This time, there is no validation window, so the evaluation is done by visually comparing the model’s output with the customer’s internal forecast and the actual sales (which I backfilled retrospectively). Even without precise metrics, the key takeaway is that the customer is satisfied and sees clear value in the project.

What the visualization app shows

⚫ Historical data – actual sales
🔵 POC prediction – output from the test model with only basic settings
🟢 Final prediction – final model
🔴 Customer forecast – customer’s internal forecast

In most cases, the model’s predictions stay very close to actual values
Deviations appear mainly in anomalous sales that differ significantly from normal behavior
The model reliably captures seasonal patterns and maintains their shape even when volumes fluctuate
It can correct errors in the customer’s manual forecast
For items with shorter history, predictions are more influenced by group behavior, which can lead to deviations from reality
New items with less than 6 months of history remain challenging – accuracy starts to drop noticeably in this range

👉 Try the results yourself: https://demo-inventory-forecasts.streamlit.app/
(If the app is sleeping, click “Yes, get this app back up!” – it will be ready in under a minute.)

Note: All data shown is anonymized and slightly modified to remain informative.

Conclusion of the Series – Inventory Forecasting with AI

After ten chapters, thousands of experiments, and terabytes of trained data, I’ve finally reached the final solution.

Throughout the project, I have:

Built a robust data processing and feature engineering pipeline
Handled outliers effectively
Included items with incomplete history
Tested dozens of scenario, model, and configuration combinations
Introduced group-features for transferring knowledge between similar items

Final choice:
A TFT model with group-features, without synthetic values, trained on items with at least 1 month of history.

The result?

Stable predictions across the ntire assortment, strong seasonality preservation, and the ability to correct errors in the customer’s manual forecasts.