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AI-Powered Inventory Optimization and Sales Enablement Platform

sphereinc.com

Logistics

Retail

Information technology

Operational Inefficiencies in Inventory and Sales Processes

The client faced inefficient inventory targeting, slow decision-making (2-3 days for inventory approval), reliance on gut-driven negotiations, and margin erosion due to manual workflows. Teams struggled with siloed data, leading to missed revenue opportunities and reduced agility in a competitive market.

About the Client

A $200M/year B2B distribution company specializing in retail supplies with a focus on logistics and supply chain efficiency.

Strategic Objectives for AI-Driven Optimization

Automate inventory selection and pricing decisions
Enable data-driven customer targeting and upsell opportunities
Implement predictive analytics for demand forecasting and margin optimization
Reduce operational costs and improve gross margins

Core System Functionalities

Unified CRM data integration (sales history, customer behavior)
Real-time third-party market trend integration (eBay, Amazon, competitor pricing)
AI-powered SKU purchase recommendations
Vendor negotiation range guidance
Customer-specific pricing tier identification
Demand surge and price elasticity forecasting
Automated margin threshold alerts

Technology Stack Preferences

AI/ML frameworks (e.g., TensorFlow, PyTorch)

Cloud platforms (AWS, Azure)

Data integration tools (e.g., Apache Kafka, Fivetran)

Predictive analytics engines

Critical System Integrations

CRM systems (e.g., Salesforce, HubSpot)
E-commerce marketplaces (Amazon, eBay)
Vendor ERP systems
Real-time pricing APIs

Non-Functional Requirements

Scalable architecture for handling 1M+ SKUs
Real-time data processing (<100ms latency)
Enterprise-grade data encryption and compliance (GDPR, SOC2)
User-friendly dashboard with role-based access

Expected Business Impact of AI-Driven Optimization

The solution is projected to reduce inventory approval cycles by 85% (from 3 days to 4 hours), drive 18% YoY revenue growth ($36M+), improve gross margins by 10%, and cut operational costs by 30%. Enhanced customer personalization is expected to increase repeat buyers by 25% within 12 months.