SKU Science

Based on historical data, SKU Science calculates forecasts at the appropriate granular level for the business, including individual items, customer-specific combinations, or aggregated product families. The system automatically selects the optimal forecast from 644 statistical combinations to identify trends and seasonal patterns. Users can modify forecasts at any hierarchy level, such as store or territory, to ensure localized accuracy.

SKU Science features historical sales data and computes forecasts to provide instant access to pre-configured business review dashboards. These tools allow teams to analyze trends and compare performance against fiscal year budgets or historical year-over-year results. Data can be aggregated dynamically by territory or customer, allowing planners to visualize backlogs versus budgets and take immediate corrective actions.

For every inventory element, the system computes forecast accuracy, absolute error, and bias based on specific lead times. It tracks the forecast value added by measuring the impact of manual baseline overrides or external sales team uploads against the automated model. By financially valuing each error, the platform focuses management attention on high-impact items that most affect the organization's bottom line.

All data generated by the software can be organized into customized reports designed to facilitate structured executive reviews. These reports consolidate complex datasets to meet specific supply chain objectives and are fully exportable. This ensures that forecasting outputs can be seamlessly integrated into existing IT ecosystems, such as ERP or BI systems, to drive procurement and production decisions.