Enterprise AI Readiness & Data Modernization Framework

CPG and Retail companies operate in highly competitive markets with thin margins, complex supply chains, and constantly shifting consumer expectations. Speed, accuracy, and customer insight are essential.

CPG and Retail organizations face persistent operational and data challenges that impact visibility, efficiency, and growth, including:

Fragmented supply chain and inventory data

Volatile consumer demand and rising logistics costs

SKU proliferation and product lifecycle pressure

Inefficient forecasting and replenishment

Limited real-time visibility into inventory and promotions

Customer experience gaps across channels

The Frame Difference – How We Serve CPG and Retail Better

Clients choose Frame for our proven ability to enhance forecasting accuracy, streamline replenishment, and optimize commercial performance through Data & AI, supported by:

Accelerators that shorten implementation timelines
Deep experience in forecasting, replenishment, and omnichannel data
Cloud-agnostic engineering for Azure, AWS, GCP, Databricks
Proven ability to improve availability, reduce waste, and optimize promotions

Let us help you in the CPG/Retail Industry

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Frame’s Solutions for CPG & Retail

Frame delivers modern Data & AI capabilities that help CPG and Retail organizations operate with greater accuracy, agility, and predictive intelligence, such as:

Services that Support This Industry

Our service portfolio is designed to address the full spectrum of data, analytics, and AI needs across CPG and Retail, including:

Case Studies

Terminal Operator – Scheduling Optimization

A large terminal operator managing truck, rail, and barge movements struggled with congestion, long turnaround times, and inconsistent scheduling efficiency.

Pipeline Operator – Industrial Equipment Producer – Quality Analytics

A leading industrial equipment manufacturer produced complex, high-precision components across multiple plants.

Drilling Operator – NPT Analytics

A leading drilling operator faced persistent Non-Productive Time (NPT) across multiple rigs. Engineers lacked a unified view of operational data—rig state logs, drilling parameters, daily reports, and well logs were stored in disconnected systems.