HIMPACT vs Quantics Planning vs SAS Collaborative Planning Workbench - Which Tool Fits Your Business?

HIMPACT’s use of daily analytics and proprietary algorithms significantly enhances forecast accuracy by continuously adapting to real-time demand signals.

The system smartly separates regular, promotional, and lost sales demand, providing a granular understanding of customer behavior and helping buyers make more informed decisions.

With automated updates and exception alerts, HIMPACT reduces manual workload and allows planners to focus on strategic decision-making rather than routine tasks.

The inclusion of Service and Lost Sales Analysis gives users a deeper understanding of customer demand changes and the root causes of missed sales, refining future forecasts.

Quantics eliminates silos by enabling seamless collaboration across departments, ensuring that all stakeholders contribute to a unified and consistent planning strategy.

The platform integrates a market view into planning processes, allowing businesses to understand demand drivers and shape forecasts based on real-world influences.

Users gain access to real-time data insights that enhance visibility into performance, helping them adapt quickly to changes and optimize plans efficiently.

Quantics helps organizations manage uncertainty by continuously refining forecasts, leading to more accurate, responsive, and resilient planning.

SAS® Collaborative Planning Workbench automates the entire consensus planning process using a structured, analytics-backed workflow, significantly reducing manual effort and planning inconsistencies across departments.

By consolidating internal and external demand forecasts and applying weighted statistical techniques, it improves forecast precision and provides a more holistic view of demand.

Users can easily simulate different planning outcomes and adjust baseline forecasts, enhancing flexibility and preparedness for market volatility.

Built-in FVA tools allow organizations to quantify the impact of manual adjustments versus statistical models, enabling data-driven improvements.