Top 10 Best Retail Demand Forecasting Software of 2026

SAS Demand Forecasting fits retailers that produce forecasts for many store-item combinations. Automated model selection reduces the need to choose a method for every series. Hierarchy reconciliation helps planners review results at both detailed and aggregate levels.

Promotion and holiday inputs give analysts a way to account for demand changes beyond historical sales patterns. Those inputs require consistent event histories, which can be difficult for retailers with fragmented promotion data. A team using SAS Viya can connect forecast workflows to downstream planning systems through its APIs, but replenishment execution remains outside the forecasting workflow.

LEAFIO Demand Planning & Forecasting, developed by LEAFIO AI, is AI-powered retail demand planning and forecasting software for multi-store chains. Its machine learning models forecast demand for each SKU in each store and warehouse, producing weekly and monthly forecasts plus a daily view that separates baseline and promotional demand and applies day-of-week coefficients. The models account for sales history, seasonality, promotions and promo cannibalization, national, regional, and floating holidays, store traffic, and weather for weather-sensitive categories such as ice cream and bottled water.

Forecast quality starts with clean demand history: the system excludes promotional days, wholesale transactions, and one-off spikes from the baseline and restores demand lost to stockouts. Planners see MAPE per SKU, track accuracy with PE, WMAPE, RMSE, MAE, and bias, compare the model with their own uploaded forecasts in a plan-fact report, and adjust trends manually, with custom corrections kept across recalculations.

Forecasts connect directly to LEAFIO's automated ordering, where the CDA, FRESH, and DFO algorithms turn them into store, perishable, and DC-to-store orders. LEAFIO Demand Planning & Forecasting is used by grocery, convenience, pharmacy, beauty, toy, and specialty retailers; Toy House reduced overstock by 19% across 67 stores. Reliable sales, assortment, and inventory data are needed for the best results.

Anaplan lets model builders define product, location, and time dimensions instead of imposing a fixed retail structure. Teams can compare promotion scenarios and reconcile a consensus forecast across merchandising, supply chain, and finance. PlanIQ projections can feed those models alongside planner adjustments.

CloudWorks schedules data integrations, and the REST API supports automated imports and exports. Store-item model design, access rules, and data mappings require specialist administration. Retailers that need automated purchase-order execution must connect a separate replenishment system.

GMDH Streamline focuses retail demand forecasting on the inventory decisions that follow each forecast. It produces SKU-location forecasts and uses supplier lead times to recommend purchase orders and stock transfers.

Integrations with NetSuite, SAP Business One, and Excel bring sales and inventory records into the planning workflow. Its purchasing focus serves multi-location replenishment teams more directly than teams seeking assortment or markdown planning.

Blue Yonder forecasts retail demand by item and location, with planning workflows connected to its replenishment applications. Luminate Planning uses machine learning forecasting and accounts for promotions, holidays, and external demand drivers.

Retail teams can coordinate forecasts across stores and distribution centers. Connecting sales, inventory, and product data takes integration work, and replenishment runs in a separate Blue Yonder application.

RELEX Solutions suits multi-store retailers that need forecasts connected to inventory execution rather than a standalone prediction dashboard. Its SKU-location forecasting incorporates promotions, holidays, and weather into demand projections.

The RELEX Living Retail Platform connects those forecasts with automated replenishment, allocation, and space planning. Deploying that breadth requires consistent product, store, and promotion data.

e2open links demand forecasts to retailer and distributor data across its multi-enterprise network. Demand Planning builds forecasts, while Demand Sensing uses recent point-of-sale, order, and inventory signals to adjust them.

Channel Data Management consolidates downstream partner feeds for supplier planning. Its supplier-oriented workflows require more partner coordination than a retailer needs to forecast only its own stores.

ToolsGroup pairs retail demand forecasts with service-driven inventory optimization in its SO99+ engine. SO99+ models demand variability at the item-location level and uses it to set stock targets.

ToolsGroup’s retail suite also covers assortment, allocation, and replenishment across stores and distribution centers. That breadth makes SO99+ less suited to teams that only need forecast reports.

Slimstock links demand forecasts to purchasing and store replenishment in its Slim4 planning suite. Slim4 calculates safety stock using demand patterns and service-level targets, then flags exceptions for planner review.

Retail teams can manage allocation and assortment decisions alongside inventory planning instead of passing forecasts to a separate ordering tool. That breadth requires teams to configure purchasing and inventory rules, not just forecasting inputs.

o9 Solutions fits large retailers connecting store forecasts to merchandise and supply decisions through its Enterprise Knowledge Graph. Its demand planning capabilities include machine learning forecasting and promotion modeling across product and location hierarchies.

Retail workflows extend into assortment, allocation, and replenishment, so teams can assess inventory effects when demand assumptions change. The breadth of connected planning requires substantial data integration and makes o9 Solutions less practical for teams seeking only a forecasting application.