All Case Studies
Consumer Packaged GoodsMicrosoft AzureAI-Driven Operational EfficiencyData-Driven Decision Intelligence

AI-Driven Retail Transformation & Demand Intelligence

National Retail Chain

38%
Reduction in Inventory Distortion
$12.9M
Annual Savings from Optimized Inventory
15%
Reduction in Markdown Rate
92%
Demand Forecast Accuracy

Business Context

The organization and its strategic environment

A 450-store national retail chain with $2.1B in revenue was losing $34M annually to inventory distortion — overstock in some categories, stockouts in others. The merchandising team relied on 6-month-old trend data and gut instinct for buying decisions.

Technology Landscape

Systems and infrastructure before DGT

Microsoft Dynamics 365 for ERP, Azure SQL for data, Power BI for reporting (underutilized), manual demand planning in Excel, and no machine learning infrastructure.

The Challenge

What the client was facing

$34M annual loss from inventory distortion, 6-month lag in trend data for buying decisions, no demand forecasting beyond historical averages, and 22% markdown rate on seasonal merchandise.

The DGT Solution

How DGT addressed the challenge

DGT built an AI-powered demand intelligence platform on Azure with ML-based demand forecasting, automated replenishment, real-time inventory optimization, and executive dashboards. The platform ingests POS, weather, social, and economic signals.

DGT Accelerators Used

Insights360API Hub

Delivery Approach

How DGT executed the engagement

Proof-of-value first: DGT built a demand forecasting model for 3 product categories in 4 weeks, validated accuracy against historical data, then scaled to all categories. Azure ML pipeline with automated retraining.

Governance Model

How the engagement was managed

Merchandising steering committee, data science collaboration model with DGT and client analysts, weekly model performance reviews, and monthly business impact assessment.

Timeline & Phases

The execution roadmap

1

Data Assessment

2 weeks

Data quality audit, signal identification, Azure ML setup

2

Proof of Value

4 weeks

3-category demand model, accuracy validation, stakeholder demo

3

Full Model Build

6 weeks

All-category models, external signal integration, replenishment automation

4

Dashboard & Integration

4 weeks

Insights360 dashboards, Dynamics 365 integration, buyer tools

5

Optimization

4 weeks

Model tuning, seasonal adjustment, knowledge transfer

Risks Addressed

Key risks DGT mitigated during the engagement

Model accuracy for long-tail SKUs with sparse data
Merchandising team trust in AI-driven recommendations
External signal data quality and availability
Integration with existing Dynamics 365 replenishment workflows

Why DGT Won

What set DGT apart in this engagement

Azure AI engineering expertise with retail domain knowledge
Insights360 provided pre-built retail KPI dashboards
API Hub connected external data signals in 2 weeks
Proof-of-value approach reduced risk and built stakeholder confidence

Outcome Metrics

Measurable before-and-after results

MetricBeforeAfter DGT
Inventory Distortion$34M/yr$21.1M/yr
Markdown Rate22%18.7%
Demand Forecast Accuracy64%92%
Buying Decision Lead Time6 months2 weeks

The Impact

Headline results delivered

38%
Reduction in Inventory Distortion
$12.9M
Annual Savings from Optimized Inventory
15%
Reduction in Markdown Rate
92%
Demand Forecast Accuracy
"DGT's demand intelligence platform changed how we buy. We went from guessing to knowing — and the P&L shows it."

Jennifer Walsh

Chief Merchandising Officer, National Retail Chain

Could This Work in Your Environment?

Let's discuss how DGT can deliver similar outcomes for your enterprise. Book a session to walk through your specific challenges.