Retail Chain Saved $1.2M Annually with Infoslab’s AI Demand Forecasting Pod

Project Overview

Our client, a global retail powerhouse with over 500 physical storefronts and a booming online presence, spans operations across North America, Europe, and Asia-Pacific. Their vast catalog of thousands of SKUs and product categories posed major inventory optimization challenges. Seasonal shifts, regional preferences, and promotional spikes added complexity to forecasting, with their legacy systems and talent limitations creating a perfect storm of inefficiency.

Industry

Retail & E Commerce

Challenge

The client had a rudimentary, manually updated BI setup that provided static weekly forecasts. With no centralized forecasting engine, the supply chain was at the mercy of inconsistent data and outdated methodologies. Specific pain points included:

• Promotional blind spots: Marketing-driven demand surges were misjudged, causing overstock in some areas and stockouts in others.

• Manual-heavy workflows: Regional analysts created disconnected spreadsheets updated once per week.

• Data silos: Sales, inventory, weather, and marketing data were spread across departments.

• AI hiring bottlenecks: Recruiting domain-experienced ML talent took 3–4 months due to global demand-supply imbalances.

Infoslab’s Talent-Led Solution

To combat this, Infoslab deployed a domain-specialized AI Pod within 10 days that delivered immediate value and long-term impact. The pod consisted of:

Lead Data Engineer: Architected streaming data pipelines using Databricks. Built Delta Lake tables integrating POS, e-commerce, weather, calendar, and promotions data with near-real-time refresh cycles.

ML Engineer: Created time-series forecasting models using Prophet, ARIMA, and XGBoost. Models adapted dynamically to seasonality, local trends, and promotions.

Analytics Translator: Worked with merchandising and supply chain teams to deliver business-friendly dashboards and train users to act on model outputs.

Project Coordinator: Ensured agile execution, aligned teams across business units, and navigated retail-specific constraints.

Why Infoslab?

Retail + AI Talent: Cross-functional pod with retail forecasting domain fluency.

Speed to Value: Talent deployed in 10 days instead of 3-month hiring lag.

Self-Sufficiency Focus: Built with internal handover and skill upliftment in mind.

Cost Efficient: 30% lower costs than comparable FTE-based team.

Get in Touch with Infoslab

Technical Highlights

• Automated data pipelines reduced refresh cycles from 2 days to under 60 minutes.

• Models trained on SKU-level, store-level, and region-level granularity.

• Enforced forecast governance using MLFlow for versioning and model retraining triggers.

• Created PowerBI dashboards showcasing forecast confidence intervals, daily inventory risks, and promotional uplift curves.

Delivery Highlights

• 7% increase in product availability led to higher customer retention.

• Enabled accurate S&OP alignment across sourcing, logistics, and demand planning.

• Internal team trained to own and extend models using our documentation and workshops.