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# AI-based Perfect Order forecasting unlocks sales growth across 100K+ stores
**URL:** https://www.sigmoid.com/case-studies/ai-based-perfect-order-forecasting-unlocks-sales-growth-across-100k-stores/
Date: 2026-07-08
Author: Joha Momin
Post Type: page
Summary: CPG · Beverages · Perfect Order Interactive case study AI-based Perfect Order forecasting unlocks sales growth across 100K+ stores A Fortune 500...Read More...
---
CPG · Beverages · Perfect Order
Interactive case study
# 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.
Manual Ordering at Scale — The Before State
What account managers across the DSD network looked like without AI
Store visits driven by manual judgmentFull network
Every order quantity set by experience — no data backing
Incumbent platform coverage20–25K stores
Solution live but could not scale beyond this threshold
AM capacity for high-value activityLow
Most visit time consumed by manual order entry, not upselling
Manual
order entry
per visit
Inconsistent
service levels
across retailers
Missed
upsell &
cross-sell
— 01
Business Scenario
A scalable Perfect Order platform existed in name only — constrained at 20–25K stores while the business needed it across **100K+**.
Key Challenges Addressed
C1
**Business context missing from ordering decisions**
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.
C2
**Scaling bottleneck under rising data and model complexity**
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.
C3
**Poor frontline integration stalling adoption**
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.
One Store Visit — The Manual Ordering Process
What a DSD visit looked like without AI-driven recommendations
1
Arrive
Store Arrival
No live data — AM relies entirely on memory and prior visit notes
No prior data
2
Count
Manual Shelf Count
Only counts visible inventory — misses sell-out velocity and in-transit stock
Error-prone
3
Estimate
Estimate Demand
Judgment-based — no promo calendar, Circana trends, or sell-out velocity
Misses promo uplift
4
Order
Order Entry
Manually typed quantity — not tied to in-stock availability or service level KPIs
No KPI alignment
5
Submit
Submit & Move On
No validation, no outcome tracking — cycle repeats unchanged at every visit
No feedback loop
⚠ Step 3 is where value was lost — **judgment replaced data, promo and sell-out signals never reached the order**
Sigmoid's Solution
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.
High-Level Solution Architecture
CI/CD &
ORCHESTRATION
UC4 Automation
Bitbucket
GCP Cloud Build
GCP Secret Manager
Google Compute Engine
DATA INPUTS
Circana & POS Data
Inventory & In-Transit
Promotion Data
Route Manager Data
Retailer Forecast & Planogram
GCP ML CORE
STAGING LAYER
Snowflake: Promotions & ML Inbound
Dimension Tables (Snowflake)
ML MODELS
Vertex AI · xGBoost Forecasting
KPI-Driven Recommendation Engine
Store x SKU Level Predictions
PIPELINES
Inference Pipeline (GCP)
Post-Processing
DE-II Snowflake Tables
Retraining Pipeline (GCP)
Test & Learn Framework
OUTPUT & EXECUTION
ANALYTICS LAYER
Perfect Order PBI Dashboard
Value Measurement PBI Dashboard
FIELD EXECUTION
myday iOS Field Application
DSD Stores — North America
Retraining feedback loop
STORAGE LAYER
Temp Stages for External Data
Intermediate Parquet Files
Model Storage & Versioning
Prediction Logs
Key Solution Capabilities
KPI-Driven Recommendation Engine
Order recommendations are explicitly tied to in-stock availability, service levels, and net sales objectives, ensuring every suggestion reinforces the enterprise's commercial priorities rather than just historical demand patterns.
Embedded Frontline Workflow Integration
Recommendations are surfaced directly inside the myday iOS field app during store visits, removing the friction that had previously blocked adoption and enabling point-of-execution action without leaving the AM's workflow.
Test & Learn Framework for Continuous Improvement
A structured experimentation framework continuously evaluates and refines ordering strategies through controlled trials, replacing ad hoc model updates with a governed, evidence-based improvement cycle.
Perfect Order Recommendation Engine
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).
Store & Period Configuration
Store Channel
Convenience / Small Format
Grocery / Supermarket
Large Retail / Club Store
Drug / Pharmacy
Current Inventory Level
Low
Adequate
High
Week in Period
Wk 1
Wk 2
Wk 3
Wk 4
Active Promotion
None
Price Promo
Feature
Projected In-Stock Rate (AI)
94%
AI-recommended order applied
Order Recommendation vs Manual Estimate
Generating...
Manual Estimate
--
cases (judgment-based)
AI Recommendation
--
cases (xGBoost + KPI engine)
--
Order Accuracy Delta
--
Projected Sales Lift
--
Manual Adjustment Rate
*illustrative
Business Impact
94%
In-Stock Rate with More Accurate Ordering
xGBoost-powered store x SKU-level recommendations aligned to enterprise KPIs delivered consistent product availability across the full DSD retail network at enterprise scale.
#### 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.
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