AI Demand Forecasting Software India

Enmovil's demand forecasting tools are an AI-native forecasting engine that ingests distributor offtake data, secondary sales signals, GST e-invoice demand patterns, and region-specific seasonality inputs — including monsoon demand shifts and FMCG festival demand spikes — and produces SKU-level consumption forecasts with auto-calculated MOQ, ROP, ROQ, and safety stock parameters that planners can act on immediately.

Stage 1: consumption data analysed per SKU across distributor offtake, secondary sales, GST e-invoice signals, and region-specific seasonality. Stage 2: ML forecast self-validates against a configurable MAPE target — SKUs outside the band get flagged with root-cause hypotheses. Stage 3: six stock parameters derive from the validated forecast and push to inventory planning, with procurement notified on any in-flight PO impact.

Enmovil detects FMCG festival spikes (Diwali, Eid, regional harvest cycles), monsoon dips, and automotive spare parts buffer-building — and folds them into SKU-level forecasts automatically. Seasonal adjustments apply at category level with SKU overrides where patterns differ. Secondary sales data gives early-warning signals primary orders haven't reflected yet.

MAPE is tracked per SKU, category, and portfolio. Each forecast self-validates before deployment — SKUs that miss the target get flagged for review, not pushed to replenishment. Intermittent-demand SKUs run on alternate models designed for irregular consumption, not averaged alongside high-velocity FMCG items.

Caddie is the AI layer inside Enmovil's forecasting platform. It reads live forecast accuracy bands, consumption shifts, and distributor offtake signals — and answers in plain language. Surfaces off-forecast categories, identifies at-risk SKUs before a festival peak, calculates updated ROP and safety stock, and pushes revised parameters with procurement notification.