Inventory Planner
Key Info:
Inventory Planner by Sage is a cloud forecasting, replenishment and retail analytics application for product businesses. It uses sales, stock, supplier and lead-time data to recommend what and when to buy, identify overstock and stockout risk, prepare purchase orders and analyse inventory performance.
Details
Forecasting and replenishment
Inventory Planner calculates demand forecasts and replenishment recommendations from connected sales and stock history, lead times and configuration choices. Users can review methods, seasonality, trends and overrides at product or variant level. A proof should compare recommendations with known historical periods, promotional spikes, stockouts and new products and should document when planners are allowed to override the calculated result.
Purchasing and stock planning
Recommended quantities can feed stock orders and purchase orders, while open-to-buy planning helps align inventory investment with targets. Multi-warehouse views and supplier information support location and vendor decisions. Buyers should test minimum order quantities, case packs, multiple suppliers, delayed receipts, transfers and currency or landed-cost assumptions. Confirm which data writes back to connected systems and which remains planning-only.
Analytics and exception work
Dashboards and reports expose stock value, pending purchases, overstock, best sellers, margins, aging and other inventory KPIs. Alerts and filters help planners focus on exceptions rather than every SKU. Evaluation should trace each important metric to its source fields and reconcile a sample against accounting and commerce records. Measure how much manual cleanup is needed for bundles, returns, discontinued items and inconsistent SKU mappings.
Fit and evaluation
Inventory Planner may suit retailers, wholesalers and ecommerce teams that already have transaction systems but need dedicated forecasting and purchasing decisions. It is not the system that physically executes every warehouse movement. Selection should verify supported connectors, warehouses, forecast methods, users, reports, API access, data history, refresh frequency, permissions and plan limits, then run a parallel planning cycle before adopting its recommendations.
Decision record
Record the tested stock model, location structure, exception scenarios, required edition, verified integrations, data ownership, migration assumptions and named operational owner. Define opening-balance approval, cutover reconciliation, user training, support escalation and the measures that will determine whether the rollout is successful. Include a rollback threshold and a dated review of data quality after the first operating cycle. Keep unsupported pricing, customer-size, language and device values blank until a current official source maps them exactly to a BBS field.