Demand Planning Solutions: AI Forecasts That Planners Accept

An AI-powered demand planning platform takes in every signal that influences what customers will buy: historical sales, inventory and fulfillment records, point-of-sale data, distributor data, promotional calendars, weather forecasts, competitor pricing, web traffic and social media spend. It runs them through a tournament of 20+ statistical and machine learning forecasting methods at the same time and selects the model that fits each SKU, channel and location best. The result is a live demand signal that recalibrates continuously and feeds supply planning, inventory optimization, production scheduling, financial budgeting and S&OP without re-entry or format conversion.

Solvoyo puts every function on one shared demand plan, which removes the version control problem that makes spreadsheet-based collaboration unworkable. Sales supplies promotional plans and key account commitments, and finance supplies budget targets and revenue constraints. Marketing supplies campaign timing and expected lift factors. Operations supplies the capacity constraints that bound the achievable plan. A structured workflow with approval routing and conflict detection manages each contribution, so a disagreement between functional forecasts comes up in a planning discussion, before it can become an unplanned supply shortage or an inventory write-off.

They are the same question asked from opposite directions, and Solvoyo connects them with one demand elasticity model quantified at the SKU and category level. Forecasting asks: given what we plan to do commercially, what demand will result? Demand shaping asks: given the gap we need to close, which commercial actions will close it most efficiently, whether pricing, promotion, placement or portfolio? The elasticity model makes the forecast more accurate through more precise promotional lift modeling. It also generates prescriptive gap closure recommendations when a category is tracking below its revenue target.

Solvoyo’s AI builds the forecast for a new product from analog items and product attributes, without waiting for sales to accumulate. Attribute-based machine learning models and like-item modeling predict the launch curve from comparable products, and foundation models can generate a forecast with zero history at all. As real sales arrive, the forecast self-corrects, which keeps new and short-lifecycle items accurately planned from day one.

Solvoyo matches the model to the forecasting problem. Gradient-boosted trees (LightGBM, XGBoost) work on engineered demand features, deep learning models (Temporal Fusion Transformer, DeepAR) handle complex patterns, and foundation models cover items with little history. Every model is validated against a simple statistical baseline that it has to beat, so the method chosen for each SKU is the one proven most accurate, whatever its complexity.

Solvoyo’s AI detects out-of-stock and low-availability periods and corrects the demand history for them. It unconstrains suppressed sales back to true demand before the model trains on it. Without that correction, a stockout teaches the model that demand fell, and it under-orders the same item again. The AI zeroes out censored periods and imputes real demand, so the forecast reflects what customers wanted to buy even when the shelf was empty.

Solvoyo tracks accuracy and bias continuously with WMAPE, MAPE and forecast bias, for system-generated forecasts and for user overrides alike. It also measures Forecast Value Add, which shows where user overrides improve forecast accuracy. Because these metrics run continuously, underperforming models are caught and replaced automatically.

Yes. Many of our customers start with Excel-based planning. During the Digitize step, we run automated diagnostics on your historical sales and demand data to surface gaps and quality issues, and we use AI to fill in missing data points, from incomplete sales histories to missing product attributes. You can start generating reliable, AI-driven forecasts without waiting for a lengthy data cleanup project, and you reach autonomous demand planning faster.

The forecast feeds Supply Planning, Inventory Planning and S&OP without re-entry or format conversion. To go deeper, read how demand sensing turns real-time signals into better plans or how Unilever automates demand planning with Solvoyo.