AI-based Perfect Order forecasting unlocks sales growth across 100K+ stores
A Fortune 500 beverage company operating a large Direct Store Distribution network needed to move its ~2,000 account managers from manual, judgment-based ordering to data-driven execution. An incumbent vendor solution had stalled at 20–25K stores, unable to scale further. Sigmoid rebuilt the Perfect Order platform across three core areas — data foundation, ML forecasting, and frontline workflow integration — achieving 94% in-stock rates, a 2% increase in net sales, and enterprise-wide rollout across the full retail network.
per visit
across retailers
cross-sell
A scalable Perfect Order platform existed in name only — constrained at 20–25K stores while the business needed it across 100K+.
Recommendations were not aligned to enterprise KPIs such as in-stock availability, service levels, and sales execution — leaving the platform unable to drive consistent, commercially relevant ordering behavior at scale.
As store counts, data volumes, and model complexity grew, the incumbent solution hit hard limits — dashboard performance degraded, pipelines became inefficient, and scale-up beyond 20–25K stores became structurally blocked.
Recommendations were not embedded in the account managers' field workflows — meaning insights generated by the system were rarely acted on, and there was no mechanism for continuous improvement of ordering strategies.
Sigmoid led a structured transition from the incumbent vendor, rebuilding the Perfect Order platform across three core areas: a centralized data foundation ingesting sell-in, sell-out, on-hand inventory, and in-transit signals to generate store x SKU-level forecasts via xGBoost; a KPI-driven recommendation engine aligned to in-stock, service level, and sales execution objectives; and direct embedding of recommendations into the myday iOS field app with a Test & Learn framework for continuous improvement — scaling the platform from ~20–25K stores to full network coverage.
Configure a store profile and see how the xGBoost-powered engine generates an order recommendation compared to a typical manual estimate. The simulator shows both scenarios: manual under-ordering (promo blind spot) and manual over-ordering (panic or quota push).
2% Increase in Net Sales Through Better In-Store Execution
Standardized, data-driven ordering improved product availability and unlocked cross-sell and upsell opportunities that manual ordering consistently missed, translating platform precision directly into incremental revenue.
10pp Reduction in Manual Order Adjustments
Increased automation and higher recommendation quality reduced manual intervention by 10 percentage points, freeing account managers to focus on planning and upselling rather than order correction.
