The Demand Planning module starts with your historical shipments. These come from your ERP (NetSuite, SAP, Business Central, etc.) and are ingested at the customer, item, and location level on a weekly basis. Historical data (typically 2-3 years) is loaded during onboarding.
Note: The statistical baseline is generally based on the shipment history. Thus, the more historical data available to baseline the statistics, the better. Confido recommends 2 years, ideally at weekly granularity, of historical data per customer/product combination. |
3.1 Shipment History Ingestion
Confido connects directly to your ERP to pull weekly shipment actuals by customer, item, and ship-from location. If you are mid-ERP transition or have imperfect historical data, Confido can supplement with a CSV upload of historical shipment files. The onboarding team will work with you to align on the best approach for your data situation.
Note: If your historical shipment data has data quality issues — e.g., missing ship dates, incorrect item codes, or gaps from a system transition — Confido can work around this during the data load. It is better to load imperfect history than to start with no history at all, as even partial data helps the statistical models. |
3.2 Automatic Baselining and Outlier Detection
Once shipment history is loaded, Confido runs an automatic baselining process. This strips out large deviations — promotional spikes, pipe fills for new distribution, supply constraints, or other one-off events — to give you a clean baseline that represents your steady-state shipment rate.
Deviations can also be tagged manually, based on a defined list of reason codes, if the automatic detection misses something or if the cause was internal (e.g., a supply constraint that caused a delayed order). Each tagged deviation can be labeled with a business reason, so the history of what happened and why is preserved. You can find the outlier section within the demand planning workspace. The default is set to the current customer/product combination; to toggle to all outliers, select “All grouping”.
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Tip: Taking time to review and clean up the baseline during onboarding pays dividends throughout the year. A well-baselined shipment history means statistical models run on it will be more accurate from day one, and you will have fewer recommendations to clean up as new data arrives. |
3.3 Item Proxy for New Products
New items can be added to customers directly in the demand planning workspace. Click the “3 dots” next to a customer and select “Add innovation”. For the innovation item to appear, make sure it is in the product master (either through a direct ERP pull or manual addition).