The supply chain is the cornerstone of any manufacturing or retail company. It also bears the most significant costs. Defining optimal inventory levels for warehouses through safety stock requires a deep understanding of suppliers’ behavior, usage and consumption of components, as well as a well-defined service level. The latter embodies the key decision of an inventory manager: the trade-off between inventory costs and stock level.
A team of data scientists analyze the individual component level using historical data. To predict future consumption, they build and run an ARIMA time series analysis and deploy this as an Analytical Service for Inventory Managers. Automatically providing the expected supply and consumption levels, simplifies and raises the accuracy of the Inventory Manager’s job. By defining expected service levels, overstock/understock becomes clearly identifiable and in line with the agreed methodology and base data.
Why KNIME Software
An ARIMA time series model created in KNIME Analytics Platform is deployed as an Analytical Service using KNIME Server. Via the KNIME WebPortal, Inventory Managers can view stock levels and write back the calculated order plan to the supply chain management system.