Inventory optimization, answered directly.

Set stock per item from its actual demand and lead-time behavior instead of blanket rules, so excess is cut where it is not needed and stock is added where shortages occur. At one industrial manufacturer, nVentic identified a 45% inventory reduction potential while service levels rose from 85% to 99%; most clients achieve around 10% within a few months.

Start from the transaction history rather than the master data. Stock movements, purchase orders and receipts show the lead times and demand that actually occurred, so targets do not depend on parameters someone once entered. nVentic separates data-quality issues from inventory issues and checks key parameters such as lead time for each item before any change is made.

Transaction-based inventory analysis reconstructs what demand, supply and stock actually did from the individual transactions recorded in the ERP: stock movements, demand events, purchase orders and receipts. Instead of averages and planning parameters, it works at item, site and time level, so inventory targets reflect real variability and the analysis shows where capital is trapped and why.

Within days to a few weeks once the ERP data is available. In published nVentic cases, the first analysis took one day from receiving the data to the report at an industrial manufacturer, one week for six million annual transactions at an automotive OEM, and Wiha was ready to act on inventory two weeks after starting.

No. nVentic works from standard reports exported from the ERP your teams already use, such as stock movements, stock positions and purchase orders. The analysis layers over existing systems, so there is no replatforming project to approve before you can see where working capital is tied up and which items to act on first.

Safety stock for each item should follow that item's demand variability, lead-time variability and target service level, not a blanket rule such as weeks of cover. nVentic sets statistical safety-stock and reorder targets per SKU from transaction history and tests them against constraints like minimum order quantities; at Wilo, extra safety stock went only to suppliers that genuinely represented risk.

By finding where capital sits at item and site level and removing excess without cutting availability. nVentic typically identifies a 20–50% inventory reduction potential while maintaining or improving service levels. Holding stock is not free either: for every €100M held in inventory, carrying costs can reach €25M a year, so each reduction also lowers running costs.

nVentic complements SAP IBP and similar planning tools rather than replacing them. Planning suites plan forward from the parameters and master data held in the system; nVentic reconstructs from the transaction history what demand, lead times and stock actually did, shows where working capital is trapped per SKU and site, and identifies which parameters to correct, before or alongside any planning system.

nVentic works independently of the ERP vendor, because the analysis only needs the transaction history an ERP already records. Many projects run on SAP, using standard stock reports, purchase order lists and master data extracts; at Wilo, every site in scope was evaluated from raw SAP data across twelve manufacturing sites in seven countries.