How to model stock and supply chain in SAP Analytics Cloud

Supply chain planning in SAP Analytics Cloud comes down to one discipline: keeping stock levels visible across locations, time horizons and scenarios, so the business can act before a stockout or overstock becomes expensive. This guide explains how to structure a stock model in SAC — the right dimensions, the aggregation rules that matter most, and the KPIs that give operations a real decision-making surface.

Why a dedicated stock model, not a spreadsheet

The typical supply chain spreadsheet breaks at scale: one file per warehouse, manual consolidation, no drill-down, and no scenario comparison. A SAC stock model replaces that with a single governed model where every location feeds the same structure, actuals load directly from the ERP, and planners can run what-if scenarios (demand spike, supplier delay, safety-stock change) against the same dataset without copying anything. The gain is not just speed — it is the elimination of the reconciliation error that hides in every multi-file consolidation.

The right dimensions for a stock model

A clean stock model needs at minimum four dimensions beyond the standard Version and Date: Product (the SKU or article group), Location (warehouse, distribution center or plant), Entity (the legal or reporting unit that owns the stock), and Scenario (or use the native Version dimension for Actual vs Forecast). If you model at SKU level, a fifth dimension for Category or product family lets you aggregate sensibly without exploding the model size. Keep the granularity as coarse as the decision requires — daily SKU-level for a fast-moving consumer goods business, weekly category-level for a slower-moving industrial one.

The aggregation rules that matter most

Stock levels are balances, not flows — and this single fact decides your aggregation settings. A closing stock figure should aggregate with LAST over the time dimension (the end-of-period balance, not the sum of all periods). Flows — receipts, issues, transfers — aggregate with SUM. Getting this wrong produces the classic error: a "year-end stock" that is twelve times the actual figure because SAC summed twelve monthly closing balances. Check the time-aggregation of every stock measure before you build a single Story on top. For a deeper explanation of why this matters, see choosing the right aggregation in SAC and the time-dimension exception.

Key KPIs for a supply chain Story

The KPIs that drive supply chain decisions are a mix of balance and flow measures. Days of inventory on hand (closing stock divided by average daily consumption) tells you how long current stock lasts — it combines a LAST-aggregated balance with a SUM-aggregated flow, so it must be a calculated measure, not a stored one. Fill rate (orders fulfilled on time and in full as a percentage of total orders) measures service level. Stock coverage by location surfaces imbalances — one warehouse overstocked while another runs short. Reorder point alert flags items approaching safety stock. And forecast accuracy (actual demand versus planned) closes the loop between planning and operations.

Connecting to ERP data

SAC's live connection to S/4HANA is the key advantage for stock models: material movements post in the ERP and flow into the SAC model without manual export. For non-SAP sources, a scheduled import via the Data Management module or a connection to SAP Datasphere keeps the model current. Either way, structure the import so actuals land in the Actual version and forecasts land in the Forecast version — the Version dimension keeps them separate and comparable without any data duplication.

Where to start

Our supply chain stock management template gives you the dimension structure, the KPIs and realistic sample data pre-loaded, so you import and build your Story instead of modeling from scratch. The aggregation rules are already set correctly on every measure. Not sure this is the right template for your use case? Let the assistant recommend one.