Align, Sense, and Shape Your Way to Better Accuracy

An AI-powered demand planning platform ingests every available 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 — and runs them through a tournament of 20+ statistical and machine learning forecasting methods simultaneously to select the model that best fits each SKU, channel, and location. The output is not a static weekly forecast that planners manually distribute to downstream teams; it is a live, continuously recalibrated demand signal that feeds directly into supply planning, inventory optimization, production scheduling, financial budgeting, and S&OP without re-entry or format conversion.

Solvoyo provides a single platform where each function contributes to a shared demand plan without the version control problem that makes spreadsheet-based collaboration unworkable. Sales inputs promotional plans and key account commitments. Finance inputs budget targets and revenue constraints. Marketing inputs campaign timing and expected lift factors. Operations inputs capacity constraints that bound the achievable plan. Each contribution is managed through a structured workflow with approval routing and conflict detection — so disagreements between functional forecasts surface as a planning discussion rather than as an unplanned supply shortage or inventory write-off after the fact.

Demand shaping and demand forecasting address the same question from opposite directions. 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 — pricing, promotion, placement, portfolio — will most efficiently close it? Solvoyo connects both by quantifying demand elasticity at the SKU and category level, using that model both to improve forecast accuracy through more precise promotional lift modeling, and to generate prescriptive gap closure recommendations when a category is tracking below its revenue target.

For new products, Solvoyo’s AI builds the forecast from analog items and product attributes rather than waiting for sales to accumulate. Attribute-based machine learning models and like-item modeling predict the launch curve from comparable products, and foundation models generate a forecast with zero history at all. As real sales arrive, the forecast self-corrects — so new and short-lifecycle items are planned accurately from day one.

Solvoyo applies machine learning models matched to each forecasting problem — gradient-boosted trees (LightGBM, XGBoost) on engineered demand features, deep learning models (Temporal Fusion Transformer, DeepAR) for complex patterns, and foundation models for items with little history. Every model is validated against a simple statistical baseline it must beat, so the method chosen per SKU is the one proven most accurate, not the most complex.

Solvoyo’s AI detects out-of-stock and low-availability periods and corrects the demand history for them — unconstraining suppressed sales back to true demand before the model trains on it. Without this step, a stockout teaches the model that demand fell, so it under-orders the same item again. By zeroing out censored periods and imputing real demand, the forecast reflects what customers wanted, not what the empty shelf allowed.

Accuracy and bias are tracked continuously with metrics like WMAPE, MAPE, and forecast bias, for system generated forecast as well as for user overrides. The platform also measures Forecast Value Add to detect areas where user overrides are improving the forecast accuracy. This means forecast quality is monitored, not assumed, and underperforming models are caught and replaced automatically.