Dive in and see how your business can drive success with AI predictions
Pecan.ai combines advanced machine learning and AI modeling to deliver a forecast you can trust.
Pecan’s demand forecasting uses proprietary AI and machine learning to deliver top-tier accuracy and reducing bias and MAPE by significant margins. Count on precise predictions that empower proactive decision-making.
Forecasting across multiple aggregation levels, from SKUs to product families, provides precision across business segments.
Pecan’s models adapt to New Product Introductions and both constrained and unconstrained demand scenarios, aligning with real-world shifts.
Pecan’s demand forecast solution uses patented anomaly detection, outlier removal, and drift control to ensure data integrity. This means your forecasts remain stable and predictable, regardless of market changes or data quality fluctuations, helping you plan confidently.
Removes outliers for cleaner data input. Keeps data consistent, avoiding model degradation.
An advanced algorithm smooths stockouts and replenishments, creating a continuous forecast trend that enhances stability, even amid disruptions.
Continuous monitoring ensures forecast quality and makes adjustments as needed post-deployment.
Pecan’s automated data engineering streamlines model building and deployment, allowing rapid response to changes.
Through our Framework for AI-Driven Demand Intelligence, Pecan goes beyond forecasting to deliver transparency, stability and added value to demand planning processes.
SKUs are segmented into tiers to maximize forecast effectiveness, allowing planners to prioritize categories with the most impact.
On top of the predictions and forecasts, provide clear interpretable insights that build trust and enhance understanding.
Intuitive, user-friendly dashboards tailored specifically to the clients supply chain needs, allowing for quick, actionable insights.
Seamlessly Integrates into S&OP and IBP processes as the consensus forecast that can help unify the operational and commercial considerations
