Demand Planning Software That Picks the Right Model, Per SKU

Demand planning software forecasts future customer demand so businesses can plan inventory, production, and procurement proactively. TranslytiX runs 22+ statistical, machine-learning, and deep-learning models per SKU — including ETS, ARIMA/SARIMAX, Prophet, Croston-family models for intermittent demand, Random Forest, XGBoost, LightGBM, CatBoost, and deep learning models like DeepAR and N-BEATS — and automatically selects the best-fit model for each item’s demand pattern. A bottom-up planning layer and a structured, role-based consensus and approval process then turn the statistical forecast into a locked plan the business can act on. Company-reported results include a 10%+ improvement in forecast accuracy, validated across footwear, FMCG, FMCD, industrial, chemicals, and B2B engineering manufacturers.

TranslytiX evaluates every SKU against its full model library in parallel — fast-moving items are tested against time-series approaches like ETS, ARIMA, SARIMAX, and Prophet; intermittent or spare-parts demand is tested against specialist models like Croston, SBA, TSB, and ADIDA; and where enough history and complexity exist, tree-based models (Random Forest, XGBoost, LightGBM, CatBoost) and deep learning models (DeepAR, N-BEATS) are also evaluated, sometimes combined through ensembling. The platform then automatically assigns the best-fit model per SKU based on demand behaviour and data characteristics, rather than forcing one method across the whole portfolio.

Traditional demand planning tools typically apply one or two forecasting methods across the whole product portfolio and stop once the statistical number is produced. TranslytiX evaluates 22+ models per SKU and automatically selects the best fit, so intermittent, seasonal, and newly launched products each get an appropriate method instead of a one-size-fits-all curve. It then layers bottom-up planner input on top with a structured, role-based approval workflow (Area Sales Manager through CEO) and a full audit trail, and releases the locked forecast directly into inventory and production planning. The platform is ERP-agnostic — it plugs in above SAP, Oracle, ERPNext, or Tally without a rip-and-replace migration — and is built for mid-market manufacturers rather than requiring an enterprise-scale implementation.

Yes. New product introductions are forecast using predecessor-linkage and attribute clustering, so a new SKU inherits a sensible starting forecast from similar existing products instead of sitting with no usable history. Intermittent and spare-parts demand — where most periods have zero sales — is handled by specialist models (Croston, SBA, TSB, ADIDA) rather than standard time-series methods, which typically overforecast this kind of demand. Make-to-order and make-to-stock items are also segmented and treated separately, since mixing them in one forecast distorts both.